This dataset and the accompagnying codebook was created using R version 3.4.4 on an Rstudio Server.

General remarks

PsyCourse 3.1 contains data of 1223 clinical and 320 control participants. The variable “v1_stat” gives information whethere the respective individual is a clinical or a control participant. For a more in-depth description of the PsyCourse study, please refer to and cite this article.

Three very important points to consider:

  1. PsyCourse is purely observational, i.e. there is no intervention at baseline such as e.g. in clinical trials. More bluntly stated, there is nothing special happening at the first measurement point.

  2. PsyCourse is very heterogeneous in terms of participants. Approximately half of them were treated as in- or daypatient at baseline, and the participants are from different disease stages.

  3. Longitudinal measurements were approximately six months aparts, which is a rather short time, so most longitudinal measurements are highly correlated.

Interviews were conducted in German, using German translations of the tests and scales presented herein. Some scales were measured only cross-sectionally, others were measured longitudinally, some were only measured in clinical participants, others only in control participants (see below). Wherever possible, we alternately used parallel versions of tests assessed multiple times to avoid recall effects.

Importantly, the assessment period of many variables varies greatly, this should be taken into account when making inferences. For example, the SCID questions generally assess if a symptom has ever occurrred (lifetime assessment), whereas other rating scales use the past week, or shorter, as assessment period.

We created this dataset and the accompanying code bookfor three reasons:

  1. Some items (especially demographic information) in the original case-report form (CRF, “Phänotypisierungsinventar”) are rather complicated (e.g. items on the German educational system) and virtually every item needs explanation.

  2. Missing values in our phenotype database do not distinguish between structurally missing data (missing because not applicable, e.g. skipped because a screening question was answered negatively) and missing because the information was not collected (for whatever reason, e.g. drop-out of study participants). This is especially important for machine learning analyses. In this dataset, structurally missing data are coded as -999; this includes items that were not assessed in the clinical or control subsample. The remaining missing values are coded as NA.

  3. The original CRF is in German language, so it is of little use for English-speaking collaborators.

Apart from providing a reproducible codebook for the PsyCourse study, this file also contains some descriptive statistics (e.g. how many NAs, distribution of data).

For easy navigation, the table of contents contanins links to the respective sections of the document, also the overview of the measured variables contains some links.

Variable names

The names of variables in the wide dataset are contained in the heading describing each variable. For example, “Age at first interview (continuous [years], v1_age)”, means that age at first interview is measured on a continuous scale, unit years, and that it has the name “v1_age” in the dataset.

If you request data from us in the context of a proposal:

  • Please use the corresponding variable names, e.g. v1_age, and not some description of the variable (e.g. “Age of participant”)
  • Please indicate whether you would like the data in long or wide format. If you request data in long format, please use the long format variable names, if possible. See also below (“Format of dataset”).

Scale levels

Variables in this dataset have one of the following scale levels: continuous, ordinal, categorical, dichotomous, checkbox and character. For continuous variables, the units (e.g. cm) are usually given in square brackets following the variable description([]). Ordinal or categorical variables often have a list of their levels/categories in square brackets. Please note that for ordinal variables, -999, if applicable, constitutes a level itself and thus care should be taken not to analyze together with the other ordinal levels. Dichotomous variables are usually coded “Y” (yes) and “N” (no), if not mentioned otherwise. Checkboxes are either checked (coded “Y”) or -999.

Important: When analyzing GAF or other continuous or ordinal variables containing -999, please keep in mind that the -999 will falsify your analysis, if you do not recode it.

Format of dataset

We now provide the dataset both in wide format (one row corresponds to one individual), and in long format (in which each row represents a study visit of one individual).

The variable names given in this codebook refer to the variables in wide format dataset. These have a “vX_” prefix, where X indicates the particulat study visit. In long format, variables that were measured multiple times (e.g. PANSS) do not have this prefix in the variable names (but the prefix is still present in cross-sectionally measured variables).

The wide format dataset is contained in the file “191018_v3.1_psycourse_wd.RData”, the long format dataset is contained in the file “191018_v3.1_psycourse_ln.RData”.

Also, .csv files are provided (“191018_v3.1_psycourse_wd.csv” and “191018_v3.1_psycourse_ln.csv). The field separator used in the .csv files is tab. Please note that information of scale levels is lost when using the .csv files. We recommend to analyze these data with R, using an .RData file and .

Disclaimer

This dataset was created from exports of our phenotype database. As we are still completing follow-up visits, relases of the dataset will include more participants and the information already in the database is also continuously improved.

Please also note that the dataset may contain errors, even though we do our best to avoid these. The re-coding of many variables also introduces error. Therefore, we recommend to look at the data you are analyzing carefully, and to contact us if suspicion arises.

Some basic QC steps were implemented, but please be sure to inspect the data you are analyzing thorougly.

Longitudinally and cross-sectionally measured variables

In the following table, “cl” means a test was only measured in clinical participants, “cn” that a test was only measured in control participants, and “b” that it was measured in both clinical and control participants.

Control individuals were recruited at the centers LMU Munich and UMG Göttingen. Now, all psychiatric rating scales (PANSS, IDS-C30, YMRS, and GAF) are also assessed in control individuals. However, at the beginning of control recruitment, these rating scales were not included in the assessment protocol. Therefore these data are missing from a subset of control individuals, as is the CAPE-42 (also introduced later). These control participants were recruited in the center UMG Göttingen.

Section Instrument Visit 1 Visit 2 Visit 3 Visit 4
Demographic information b b b b
Psychiatric history cl
Medication b b b b
ALDA scale cl1
Family history of psychiat. ill. b
Physical measures and somatic ill. b2
Substance abuse b b b b
Event triggering first ill. ep. cl
Illness episodes between study visits cl cl cl
Life events precipitating ill. ep. cl cl cl
Psych. problems between study visits cn cn cn
Screening for psychiatric disorders MINI-DIPS cn3
DSM-IV Diagnosis SCID I Depression cl
SCID I Hypo-/Mania cl
SCID I Psychosis cl
SCID I Suicidality cl cl cl cl
Neuropsychology (cognitive tests) Trail-Making-Test b b b b
Verbal digit span b b b b
Digit-symbol-test b b b b
MWT-B b
VLMT b b b
Rating scales PANSS b b b b
IDS-C30 b b b b
YMRS b b b b
CGI cl cl cl cl
GAF b b b b
OPCRIT item 90 cl
Questionnaires SF-12 cn cn cn cn
CAPE-42 cn
Religious beliefs b b4
Med. adherence cl cl cl cl
CTS b
BDI-II b b b b
ASRM b b b b
MSS b b b b
LEQ b b b b
WHOQOL-BREF b b b b
Personality b

In- and exclusion criteria of the study

Briefly, adult (minimum age 18 years) control and clinical participants were recruited. Clinical participants with the following ICD-10 diagnoses were recruited: schizophrenia (F20.X), acute and transient psychotic disorder (F23.X), schizoaffective disorder (F25.X), bipolar disorder (F31.X), manic episode (F30.X) and recurrent depressive disorder (F33.X). After conducting the SCID Interview (part of the first study visit), we ascertained that participants met one of the following corresponding DSM-IV diagnoses: schizophrenia (295.1/.2/.3/.6/.9), schizophreniform disorder (295.4), brief psychotic disorder (298.8), schizoaffective disorder (295.7), bipolar disorder (296.X [bipolar disorders incl. manic episode]) or recurrent major depression (296.3). If the DSM-IV diagnosis ascertained through SCID interview differed from the aforementioned DSM-IV diagnostic categories, the participant was excluded. Control participants were excluded from the study if they had ever been treated as inpatient for one of the investigated ICD-10 diagnoses.

Functions that are needed to recode variables from the original dataset to the described variables are defined here. This section is not relevant for people that want only to analyze data.

Load libraries

library("sjmisc") #neccessary for row_sum function

Define functions for descriptive analyses

desc <- function(x) {
  noquote(cbind(c("No. cases", "Percent"),rbind(summary(x), 
  round(summary(x)/length(x)*100,1)),
  c(length(x),sum(summary(x)/length(x)*100))))
  }
descT <- function(x) {
  noquote(cbind(c("No. cases", "Percent"),rbind(table(x, useNA="ifany"),
  round(table(x,useNA="ifany")/length(x)*100,1)),
  c(length(x),sum(table(x, useNA="ifany")/length(x)*100))))
  }
as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}

Define functions to autmatically recode items from specific tests

CAPE-42 A and B items

#First argument w/o quotes, second with quotes
v1_cape_recode <- function(cape_old_name,cape_new_name) {
    
    itm_cape<-ifelse((is.na(v1_con$v1_cape_cape_korrekt) | v1_con$v1_cape_cape_korrekt!=2),
                  cape_old_name,NA) 
    
    all_itm_cape<-c(rep(-999,dim(v1_clin)[1]),itm_cape) #add -999 for clinical subjects
    
    assign(cape_new_name,all_itm_cape,envir=.GlobalEnv)
    
    descT(all_itm_cape)}

SF-12 items, Visit 1

#First argument w/o quotes, second with quotes
v1_sf12_recode <- function(v1_sf12_old_name,v1_sf12_new_name) {
    
    itm_sf12<-ifelse((is.na(v1_con$v1_sf12_sf12_korrekt) | v1_con$v1_sf12_sf12_korrekt!=2),
                  v1_sf12_old_name,NA) 
    
    v1_all_itm_sf12<-c(rep(-999,dim(v1_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v1_sf12_new_name,v1_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v1_all_itm_sf12)}

SF-12 items, Visit 2

#First argument w/o quotes, second with quotes
v2_sf12_recode <- function(v2_sf12_old_name,v2_sf12_new_name) {
    
    itm_sf12<-ifelse((is.na(v2_con$v2_sf12_sf12_korrekt) | v2_con$v2_sf12_sf12_korrekt!=2),
                  v2_sf12_old_name,NA) 
    
    v2_all_itm_sf12<-c(rep(-999,dim(v2_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v2_sf12_new_name,v2_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v2_all_itm_sf12)}

SF-12 items, Visit 3

#First argument w/o quotes, second with quotes
v3_sf12_recode <- function(v3_sf12_old_name,v3_sf12_new_name) {
    
    itm_sf12<-ifelse((is.na(v3_con$v3_sf12_sf12_korrekt) | v3_con$v3_sf12_sf12_korrekt!=2),
                  v3_sf12_old_name,NA) 
    
    v3_all_itm_sf12<-c(rep(-999,dim(v3_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v3_sf12_new_name,v3_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v3_all_itm_sf12)}

SF-12 items, Visit 4

#First argument w/o quotes, second with quotes
v4_sf12_recode <- function(v4_sf12_old_name,v4_sf12_new_name) {
   
    itm_sf12<-ifelse((is.na(v4_con$v4_sf12_sf12_korrekt) | v4_con$v4_sf12_sf12_korrekt!=2),
                  v4_sf12_old_name,NA) 
    
    v4_all_itm_sf12<-c(rep(-999,dim(v4_clin)[1]),itm_sf12) #add -999 for clinical subjects
    
    assign(v4_sf12_new_name,v4_all_itm_sf12,envir=.GlobalEnv)
    
    descT(v4_all_itm_sf12)}

BDI-2 items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v1_itm_bdi2_chk_clin<-v1_clin$v1_bdi2_s1_verwer_fragebogen
    v1_itm_bdi2_chk_con<-v1_con$v1_bdi2_s1_bdi_korrekt
    
    v1_itm_bdi2_clin<-ifelse((is.na(v1_itm_bdi2_chk_clin) | v1_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v1_itm_bdi2_con<-ifelse((is.na(v1_itm_bdi2_chk_con) | v1_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v1_all_itm_bdi2<-factor(c(v1_itm_bdi2_clin,v1_itm_bdi2_con),ordered=T)
    
    assign(bdi2_new_name,v1_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v1_all_itm_bdi2)}

BDI-2 items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v2_itm_bdi2_chk_clin<-v2_clin$v2_bdi2_s1_verwer_fragebogen
    v2_itm_bdi2_chk_con<-v2_con$v2_bdi2_s1_bdi_korrekt
    
    v2_itm_bdi2_clin<-ifelse((is.na(v2_itm_bdi2_chk_clin) | v2_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v2_itm_bdi2_con<-ifelse((is.na(v2_itm_bdi2_chk_con) | v2_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v2_all_itm_bdi2<-factor(c(v2_itm_bdi2_clin,v2_itm_bdi2_con),ordered=T) 
    
    assign(bdi2_new_name,v2_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v2_all_itm_bdi2)}

BDI-2 items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v3_itm_bdi2_chk_clin<-v3_clin$v3_bdi2_s1_verwer_fragebogen
    v3_itm_bdi2_chk_con<-v3_con$v3_bdi2_s1_bdi_korrekt
    
    v3_itm_bdi2_clin<-ifelse((is.na(v3_itm_bdi2_chk_clin) | v3_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v3_itm_bdi2_con<-ifelse((is.na(v3_itm_bdi2_chk_con) | v3_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v3_all_itm_bdi2<-factor(c(v3_itm_bdi2_clin,v3_itm_bdi2_con),ordered=T) 
    
    assign(bdi2_new_name,v3_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v3_all_itm_bdi2)}

BDI-2 items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_bdi2_recode <- function(bdi2_clin_old_name,bdi2_con_old_name,bdi2_new_name) {
    
    v4_itm_bdi2_chk_clin<-v4_clin$v4_bdi2_s1_verwer_fragebogen
    v4_itm_bdi2_chk_con<-v4_con$v4_bdi2_s1_bdi_korrekt
    
    v4_itm_bdi2_clin<-ifelse((is.na(v4_itm_bdi2_chk_clin) | v4_itm_bdi2_chk_clin!=2),
                  bdi2_clin_old_name,NA) 
    
    v4_itm_bdi2_con<-ifelse((is.na(v4_itm_bdi2_chk_con) | v4_itm_bdi2_chk_con!=2),
                  bdi2_con_old_name,NA)               
    
    v4_all_itm_bdi2<-factor(c(v4_itm_bdi2_clin,v4_itm_bdi2_con),ordered=T) 
    
    assign(bdi2_new_name,v4_all_itm_bdi2,envir=.GlobalEnv)
    
    descT(v4_all_itm_bdi2)}

ASRM items, Visit 1

#First and second arguments w/o quotes, second with quotes
v1_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v1_itm_asrm_chk_clin<-v1_clin$v1_asrm_verwer_fragebogen
    v1_itm_asrm_chk_con<-v1_con$v1_asrm_asrm_korrekt
    
    v1_itm_asrm_clin<-ifelse((is.na(v1_itm_asrm_chk_clin) | v1_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v1_itm_asrm_con<-ifelse((is.na(v1_itm_asrm_chk_con) | v1_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v1_all_itm_asrm<-factor(c(v1_itm_asrm_clin,v1_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v1_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v1_all_itm_asrm)}

ASRM items, Visit 2

#First and second arguments w/o quotes, second with quotes
v2_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v2_itm_asrm_chk_clin<-v2_clin$v2_asrm_verwer_fragebogen
    v2_itm_asrm_chk_con<-v2_con$v2_asrm_asrm_korrekt
    
    v2_itm_asrm_clin<-ifelse((is.na(v2_itm_asrm_chk_clin) | v2_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v2_itm_asrm_con<-ifelse((is.na(v2_itm_asrm_chk_con) | v2_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v2_all_itm_asrm<-factor(c(v2_itm_asrm_clin,v2_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v2_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v2_all_itm_asrm)}

ASRM items, Visit 3

#First and second arguments w/o quotes, second with quotes
v3_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v3_itm_asrm_chk_clin<-v3_clin$v3_asrm_verwer_fragebogen
    v3_itm_asrm_chk_con<-v3_con$v3_asrm_asrm_korrekt
    
    v3_itm_asrm_clin<-ifelse((is.na(v3_itm_asrm_chk_clin) | v3_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v3_itm_asrm_con<-ifelse((is.na(v3_itm_asrm_chk_con) | v3_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v3_all_itm_asrm<-factor(c(v3_itm_asrm_clin,v3_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v3_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v3_all_itm_asrm)}

ASRM items, Visit 4

#First and second arguments w/o quotes, second with quotes
v4_asrm_recode <- function(asrm_clin_old_name,asrm_con_old_name,asrm_new_name) {
    
    v4_itm_asrm_chk_clin<-v4_clin$v4_asrm_verwer_fragebogen
    v4_itm_asrm_chk_con<-v4_con$v4_asrm_asrm_korrekt
    
    v4_itm_asrm_clin<-ifelse((is.na(v4_itm_asrm_chk_clin) | v4_itm_asrm_chk_clin!=2),
                  asrm_clin_old_name,NA) 
    
    v4_itm_asrm_con<-ifelse((is.na(v4_itm_asrm_chk_con) | v4_itm_asrm_chk_con!=2),
                  asrm_con_old_name,NA)               
    
    v4_all_itm_asrm<-factor(c(v4_itm_asrm_clin,v4_itm_asrm_con),ordered=T) 
    
    assign(asrm_new_name,v4_all_itm_asrm,envir=.GlobalEnv)
    
    descT(v4_all_itm_asrm)}

MSS items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v1_itm_mss_chk_clin<-v1_clin$v1_mss_s1_verwer_fragebogen
    v1_itm_mss_chk_con<-v1_con$v1_mss_s1_mss_korrekt
    
    v1_itm_mss_clin<-ifelse((is.na(v1_itm_mss_chk_clin) | v1_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v1_itm_mss_con<-ifelse((is.na(v1_itm_mss_chk_con) | v1_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v1_all_itm_mss<-c(v1_itm_mss_clin,v1_itm_mss_con)
    v1_all_itm_mss<-factor(ifelse(v1_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v1_all_itm_mss,envir=.GlobalEnv)
    
    descT(v1_all_itm_mss)}

MSS items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v2_itm_mss_chk_clin<-v2_clin$v2_mss_s1_verwer_fragebogen
    v2_itm_mss_chk_con<-v2_con$v2_mss_s1_mss_korrekt
    
    v2_itm_mss_clin<-ifelse((is.na(v2_itm_mss_chk_clin) | v2_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v2_itm_mss_con<-ifelse((is.na(v2_itm_mss_chk_con) | v2_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v2_all_itm_mss<-c(v2_itm_mss_clin,v2_itm_mss_con)
    v2_all_itm_mss<-factor(ifelse(v2_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v2_all_itm_mss,envir=.GlobalEnv)
    
    descT(v2_all_itm_mss)}

MSS items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v3_itm_mss_chk_clin<-v3_clin$v3_mss_s1_verwer_fragebogen
    v3_itm_mss_chk_con<-v3_con$v3_mss_s1_mss_korrekt
    
    v3_itm_mss_clin<-ifelse((is.na(v3_itm_mss_chk_clin) | v3_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v3_itm_mss_con<-ifelse((is.na(v3_itm_mss_chk_con) | v3_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v3_all_itm_mss<-c(v3_itm_mss_clin,v3_itm_mss_con)
    v3_all_itm_mss<-factor(ifelse(v3_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v3_all_itm_mss,envir=.GlobalEnv)
    
    descT(v3_all_itm_mss)}

MSS items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_mss_recode <- function(mss_clin_old_name,mss_con_old_name,mss_new_name) {
    
    v4_itm_mss_chk_clin<-v4_clin$v4_mss_s1_verwer_fragebogen
    v4_itm_mss_chk_con<-v4_con$v4_mss_s1_mss_korrekt
    
    v4_itm_mss_clin<-ifelse((is.na(v4_itm_mss_chk_clin) | v4_itm_mss_chk_clin!=2),
                  mss_clin_old_name,NA) 
    
    v4_itm_mss_con<-ifelse((is.na(v4_itm_mss_chk_con) | v4_itm_mss_chk_con!=2),
                  mss_con_old_name,NA)               
    
    v4_all_itm_mss<-c(v4_itm_mss_clin,v4_itm_mss_con)
    v4_all_itm_mss<-factor(ifelse(v4_all_itm_mss==1,"Y","N"))
    
    assign(mss_new_name,v4_all_itm_mss,envir=.GlobalEnv)
    
    descT(v4_all_itm_mss)}

LEQ A Items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
  
    v1_itm_leq_chk_clin<-v1_clin$v1_leq_a_verwer_fragebogen
    v1_itm_leq_chk_con<-v1_con$v1_leq_a_leq_korrekt
    
    v1_itm_leq_clin<-rep(NA,dim(v1_clin)[1])
    v1_itm_leq_con<-rep(NA,dim(v1_con)[1])
    
    v1_itm_leq_clin<-ifelse(((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),
                  leq_clin_old_name, 
                      ifelse(((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v1_itm_leq_clin))
    
    v1_itm_leq_con<-ifelse(((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),
                  leq_con_old_name,
                      ifelse(((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v1_itm_leq_con))    
    
    v1_all_itm_leq<-c(v1_itm_leq_clin,v1_itm_leq_con)
    
    v1_all_itm_leq[v1_all_itm_leq==1]<-"good"
    v1_all_itm_leq[v1_all_itm_leq==0]<-"bad"
    v1_all_itm_leq<-factor(v1_all_itm_leq)
    
    assign(leq_new_name,v1_all_itm_leq,envir=.GlobalEnv)
    
    descT(v1_all_itm_leq)}

LEQ A Items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v2_itm_leq_chk_clin<-v2_clin$v2_leq_a_verwer_fragebogen
    v2_itm_leq_chk_con<-v2_con$v2_leq_a_leq_korrekt
    
    v2_itm_leq_clin<-rep(NA,dim(v2_clin)[1])
    v2_itm_leq_con<-rep(NA,dim(v2_con)[1])
    
    v2_itm_leq_clin<-ifelse(is.na(v2_clin$v2_ausschluss1_rekr_datum), NA,
                      ifelse(((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),leq_clin_old_name,
                       ifelse(((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v2_itm_leq_clin)))
    
    v2_itm_leq_con<-ifelse(is.na(v2_con$v2_rekru_visit_rekr_datum), NA,
                     ifelse(((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),leq_con_old_name,
                      ifelse(((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v2_itm_leq_con)))
    
    v2_all_itm_leq<-c(v2_itm_leq_clin,v2_itm_leq_con)
    
    v2_all_itm_leq[v2_all_itm_leq==1]<-"good"
    v2_all_itm_leq[v2_all_itm_leq==0]<-"bad"
    v2_all_itm_leq<-factor(v2_all_itm_leq)
    
    assign(leq_new_name,v2_all_itm_leq,envir=.GlobalEnv)
    
    descT(v2_all_itm_leq)}

LEQ A Items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v3_itm_leq_chk_clin<-v3_clin$v3_leq_a_verwer_fragebogen
    v3_itm_leq_chk_con<-v3_con$v3_leq_a_leq_korrekt
    
    v3_itm_leq_clin<-rep(NA,dim(v3_clin)[1])
    v3_itm_leq_con<-rep(NA,dim(v3_con)[1])
    
    v3_itm_leq_clin<-ifelse(is.na(v3_clin$v3_ausschluss1_rekr_datum), NA,
                      ifelse(((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),leq_clin_old_name,
                       ifelse(((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v3_itm_leq_clin)))
    
    v3_itm_leq_con<-ifelse(is.na(v3_con$v3_rekru_visit_rekr_datum), NA,
                     ifelse(((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),leq_con_old_name,
                      ifelse(((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v3_itm_leq_con)))
    
    v3_all_itm_leq<-c(v3_itm_leq_clin,v3_itm_leq_con)
    
    v3_all_itm_leq[v3_all_itm_leq==1]<-"good"
    v3_all_itm_leq[v3_all_itm_leq==0]<-"bad"
    v3_all_itm_leq<-factor(v3_all_itm_leq)
    
    assign(leq_new_name,v3_all_itm_leq,envir=.GlobalEnv)
    
    descT(v3_all_itm_leq)}

LEQ A Items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_leq_a_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v4_itm_leq_chk_clin<-v4_clin$v4_leq_a_verwer_fragebogen
    v4_itm_leq_chk_con<-v4_con$v4_leq_a_leq_korrekt
    
    v4_itm_leq_clin<-rep(NA,dim(v4_clin)[1])
    v4_itm_leq_con<-rep(NA,dim(v4_con)[1])
    
    v4_itm_leq_clin<-ifelse(is.na(v4_clin$v4_ausschluss1_rekr_datum), NA,
                      ifelse(((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)==F),leq_clin_old_name,
                       ifelse(((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name)), -999, v4_itm_leq_clin)))
    
    v4_itm_leq_con<-ifelse(is.na(v4_con$v4_rekru_visit_rekr_datum), NA,
                     ifelse(((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & is.na(leq_con_old_name)==F),leq_con_old_name,
                      ifelse(((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & is.na(leq_con_old_name)), -999, v4_itm_leq_con)))
    
    v4_all_itm_leq<-c(v4_itm_leq_clin,v4_itm_leq_con)
    
    v4_all_itm_leq[v4_all_itm_leq==1]<-"good"
    v4_all_itm_leq[v4_all_itm_leq==0]<-"bad"
    v4_all_itm_leq<-factor(v4_all_itm_leq)
    
    assign(leq_new_name,v4_all_itm_leq,envir=.GlobalEnv)
    
    descT(v4_all_itm_leq)}

LEQ B Items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v1_itm_leq_chk_clin<-v1_clin$v1_leq_a_verwer_fragebogen
    v1_itm_leq_chk_con<-v1_con$v1_leq_a_leq_korrekt
    
    v1_itm_leq_b_clin<-rep(NA,dim(v1_clin)[1])
    v1_itm_leq_b_con<-rep(NA,dim(v1_con)[1])
    
    v1_itm_leq_b_clin<-ifelse((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) & 
                 is.na(leq_clin_old_name), -999,   
                #data present but this LEQ item empty -> -999
                ifelse((is.na(v1_itm_leq_chk_clin) | v1_itm_leq_chk_clin!=2) &
                        !is.na(leq_clin_old_name),leq_clin_old_name,NA))
    
    v1_itm_leq_b_con<-ifelse((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) & 
                 is.na(leq_con_old_name), -999,   
                #data present but this LEQ item empty -> -999
                ifelse((is.na(v1_itm_leq_chk_con) | v1_itm_leq_chk_con!=2) &
                        !is.na(leq_con_old_name),leq_con_old_name,NA))
    
    v1_all_itm_leq_b<-factor(c(v1_itm_leq_b_clin,v1_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v1_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v1_all_itm_leq_b)}

LEQ B Items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
  
    v2_itm_leq_chk_clin<-v2_clin$v2_leq_a_verwer_fragebogen
    v2_itm_leq_chk_con<-v2_con$v2_leq_a_leq_korrekt
    
    v2_itm_leq_b_clin<-rep(NA,dim(v2_clin)[1])
    v2_itm_leq_b_con<-rep(NA,dim(v2_con)[1])
    
    v2_itm_leq_b_clin<-ifelse(is.na(v2_clin$v2_ausschluss1_rekr_datum), NA,
                        ifelse((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name), -999, #data but LEQ item empty -> -999
                          ifelse((is.na(v2_itm_leq_chk_clin) | v2_itm_leq_chk_clin!=2) & !is.na(leq_clin_old_name),leq_clin_old_name,NA)))
    
    v2_itm_leq_b_con<-ifelse(is.na(v2_con$v2_rekru_visit_rekr_datum), NA,
                       ifelse((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & is.na(leq_con_old_name), -999, #data but LEQ item empty -> -999
                        ifelse((is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con!=2) & !is.na(leq_con_old_name),leq_con_old_name,NA)))
    
    v2_all_itm_leq_b<-factor(c(v2_itm_leq_b_clin,v2_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v2_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v2_all_itm_leq_b)}

LEQ B Items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v3_itm_leq_chk_clin<-v3_clin$v3_leq_a_verwer_fragebogen
    v3_itm_leq_chk_con<-v3_con$v3_leq_a_leq_korrekt
    
    v3_itm_leq_b_clin<-rep(NA,dim(v3_clin)[1])
    v3_itm_leq_b_con<-rep(NA,dim(v3_con)[1])
    
    v3_itm_leq_b_clin<-ifelse(is.na(v3_clin$v3_ausschluss1_rekr_datum), NA,
                        ifelse((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name), -999, #data but LEQ item empty -> -999
                          ifelse((is.na(v3_itm_leq_chk_clin) | v3_itm_leq_chk_clin!=2) & !is.na(leq_clin_old_name),leq_clin_old_name,NA)))
    
    v3_itm_leq_b_con<-ifelse(is.na(v3_con$v3_rekru_visit_rekr_datum), NA,
                       ifelse((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & is.na(leq_con_old_name), -999, #data but LEQ item empty -> -999
                        ifelse((is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con!=2) & !is.na(leq_con_old_name),leq_con_old_name,NA)))
    
    v3_all_itm_leq_b<-factor(c(v3_itm_leq_b_clin,v3_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v3_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v3_all_itm_leq_b)}

LEQ B Items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_leq_b_recode <- function(leq_clin_old_name,leq_con_old_name,leq_new_name) {
    
    v4_itm_leq_chk_clin<-v4_clin$v4_leq_a_verwer_fragebogen
    v4_itm_leq_chk_con<-v4_con$v4_leq_a_leq_korrekt
    
    v4_itm_leq_b_clin<-rep(NA,dim(v4_clin)[1])
    v4_itm_leq_b_con<-rep(NA,dim(v4_con)[1])
    
    v4_itm_leq_b_clin<-ifelse(is.na(v4_clin$v4_ausschluss1_rekr_datum), NA,
                        ifelse((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & is.na(leq_clin_old_name), -999, #data but LEQ item empty -> -999
                          ifelse((is.na(v4_itm_leq_chk_clin) | v4_itm_leq_chk_clin!=2) & !is.na(leq_clin_old_name),leq_clin_old_name,NA)))
    
    v4_itm_leq_b_con<-ifelse(is.na(v4_con$v4_rekru_visit_rekr_datum), NA,
                       ifelse((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & is.na(leq_con_old_name), -999, #data but LEQ item empty -> -999
                        ifelse((is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con!=2) & !is.na(leq_con_old_name),leq_con_old_name,NA)))
    
    v4_all_itm_leq_b<-factor(c(v4_itm_leq_b_clin,v4_itm_leq_b_con),ordered=T)

    assign(leq_new_name,v4_all_itm_leq_b,envir=.GlobalEnv)
    
    descT(v4_all_itm_leq_b)}

WHOQOL-BREF Items, Visit 1

#First and second arguments w/o quotes, third with quotes
v1_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v1_itm_quol_chk_clin<-v1_clin$v1_whoqol_bref_verwer_fragebogen
    v1_itm_quol_chk_con<-v1_con$v1_whoqol_bref_whoqol_korrekt
    
    v1_itm_quol_clin<-ifelse((is.na(v1_itm_quol_chk_clin) | v1_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v1_itm_quol_con<-ifelse((is.na(v1_itm_quol_chk_con) | v1_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v1_all_itm_quol<-factor(c(v1_itm_quol_clin,v1_itm_quol_con),ordered=T)}
    else          {v1_all_itm_quol<-factor(6-c(v1_itm_quol_clin,v1_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v1_all_itm_quol,envir=.GlobalEnv)
    
    desc(v1_all_itm_quol)}

WHOQOL-BREF Items, Visit 2

#First and second arguments w/o quotes, third with quotes
v2_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v2_itm_quol_chk_clin<-v2_clin$v2_whoqol_bref_verwer_fragebogen
    v2_itm_quol_chk_con<-v2_con$v2_whoqol_bref_whoqol_korrekt
    
    v2_itm_quol_clin<-ifelse((is.na(v2_itm_quol_chk_clin) | v2_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v2_itm_quol_con<-ifelse((is.na(v2_itm_quol_chk_con) | v2_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v2_all_itm_quol<-factor(c(v2_itm_quol_clin,v2_itm_quol_con),ordered=T)}
    else          {v2_all_itm_quol<-factor(6-c(v2_itm_quol_clin,v2_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v2_all_itm_quol,envir=.GlobalEnv)
    
    desc(v2_all_itm_quol)}

WHOQOL-BREF Items, Visit 3

#First and second arguments w/o quotes, third with quotes
v3_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v3_itm_quol_chk_clin<-v3_clin$v3_whoqol_bref_verwer_fragebogen
    v3_itm_quol_chk_con<-v3_con$v3_whoqol_bref_whoqol_korrekt
    
    v3_itm_quol_clin<-ifelse((is.na(v3_itm_quol_chk_clin) | v3_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v3_itm_quol_con<-ifelse((is.na(v3_itm_quol_chk_con) | v3_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v3_all_itm_quol<-factor(c(v3_itm_quol_clin,v3_itm_quol_con),ordered=T)}
    else          {v3_all_itm_quol<-factor(6-c(v3_itm_quol_clin,v3_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v3_all_itm_quol,envir=.GlobalEnv)
    
    desc(v3_all_itm_quol)}

WHOQOL-BREF Items, Visit 4

#First and second arguments w/o quotes, third with quotes
v4_quol_recode <- function(quol_clin_old_name,quol_con_old_name,quol_new_name,recode) {
    
    v4_itm_quol_chk_clin<-v4_clin$v4_whoqol_bref_verwer_fragebogen
    v4_itm_quol_chk_con<-v4_con$v4_whoqol_bref_whoqol_korrekt
    
    v4_itm_quol_clin<-ifelse((is.na(v4_itm_quol_chk_clin) | v4_itm_quol_chk_clin!=2),
                  quol_clin_old_name,NA) 
    
    v4_itm_quol_con<-ifelse((is.na(v4_itm_quol_chk_con) | v4_itm_quol_chk_con!=2),
                  quol_con_old_name,NA)               
    
    if(recode==0) {v4_all_itm_quol<-factor(c(v4_itm_quol_clin,v4_itm_quol_con),ordered=T)}
    else          {v4_all_itm_quol<-factor(6-c(v4_itm_quol_clin,v4_itm_quol_con),ordered=T)}
                      
    assign(quol_new_name,v4_all_itm_quol,envir=.GlobalEnv)
    
    desc(v4_all_itm_quol)}

Big Five Personality Items

#First and second arguments w/o quotes, third with quotes
big_five_recode <- function(big_five_clin_old_name,big_five_con_old_name,big_five_new_name,recode) {
    
    itm_big_five_chk_clin<-v1_clin$v1_bfi_10_verwer_fragebogen
    itm_big_five_chk_con<-v1_con$v1_bfi_10_bfi_korrekt
    
    itm_big_five_clin<-ifelse((is.na(itm_big_five_chk_clin) | itm_big_five_chk_clin!=2),
                  big_five_clin_old_name,NA) 
    
    itm_big_five_con<-ifelse((is.na(itm_big_five_chk_con) | itm_big_five_chk_con!=2),
                  big_five_con_old_name,NA)               
    
    if(recode==0) {all_itm_big_five<-factor(c(itm_big_five_clin,itm_big_five_con),ordered=T)}
    else          {all_itm_big_five<-factor(6-c(itm_big_five_clin,itm_big_five_con),ordered=T)}
                      
    assign(big_five_new_name,all_itm_big_five,envir=.GlobalEnv)
    
    desc(all_itm_big_five)}

Life event between study visits, LEQ item number, Visit 2

leq_event_recode_v2<- function(leq_ev_old_name,leq_ev_new_name) {
  
  leq_itm_no<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
  
  leq_itm_no<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v2_con)[1])))==F,
                            c(leq_ev_old_name,rep(-999,dim(v2_con)[1])),
                            
                        ifelse((v2_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v2_con)[1])))) |              
                                 v2_clin_ill_ep_snc_lst=="N" | v2_clin_ill_ep_snc_lst=="C",-999,leq_itm_no))

  leq_itm_no<-factor(leq_itm_no)
  assign(leq_ev_new_name,leq_itm_no,envir=.GlobalEnv)
  descT(leq_itm_no)}

Life event between study visits, LEQ item number, Visit 3

leq_event_recode_v3<- function(leq_ev_old_name,leq_ev_new_name) {
  
  leq_itm_no<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
  
  leq_itm_no<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v3_con)[1])))==F,
                            c(leq_ev_old_name,rep(-999,dim(v3_con)[1])),
                            
                        ifelse((v3_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v3_con)[1])))) |              
                                 v3_clin_ill_ep_snc_lst=="N" | v3_clin_ill_ep_snc_lst=="C",-999,leq_itm_no))

  leq_itm_no<-factor(leq_itm_no)
  assign(leq_ev_new_name,leq_itm_no,envir=.GlobalEnv)
  descT(leq_itm_no)}

Life event between study visits, LEQ item number, Visit 4

leq_event_recode_v4<- function(leq_ev_old_name,leq_ev_new_name) {
  
  leq_itm_no<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
  
  leq_itm_no<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v4_con)[1])))==F,
                            c(leq_ev_old_name,rep(-999,dim(v4_con)[1])),
                            
                        ifelse((v4_clin_ill_ep_snc_lst=="Y" & is.na(c(leq_ev_old_name,rep(-999,dim(v4_con)[1])))) |              
                                 v4_clin_ill_ep_snc_lst=="N" | v4_clin_ill_ep_snc_lst=="C",-999,leq_itm_no))

  leq_itm_no<-factor(leq_itm_no)
  assign(leq_ev_new_name,leq_itm_no,envir=.GlobalEnv)
  descT(leq_itm_no)}

Life events between study visits, occurred before illness episode, Visit 2

#First argument w/o quotes, second with quotes
b4_event_recode_v2<- function(between_clin_old_name,between_new_name) {
  
  itm_btw<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
  
  itm_btw<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v2_con)[1]))==1,"Y",
                         ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v2_con)[1]))==2,"N",-999))
                              
  itm_btw<-factor(itm_btw)
  assign(between_new_name,itm_btw,envir=.GlobalEnv)
  descT(itm_btw)}

Life events between study visits, occurred before illness episode, Visit 3

#First argument w/o quotes, second with quotes
b4_event_recode_v3<- function(between_clin_old_name,between_new_name) {
  
  itm_btw<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
  
  itm_btw<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v3_con)[1]))==1,"Y",
                         ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v3_con)[1]))==2,"N",-999))
                              
  itm_btw<-factor(itm_btw)
  assign(between_new_name,itm_btw,envir=.GlobalEnv)
  descT(itm_btw)}

Life events between study visits, occurred before illness episode, Visit 4

#First argument w/o quotes, second with quotes
b4_event_recode_v4<- function(between_clin_old_name,between_new_name) {
  
  itm_btw<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
  
  itm_btw<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v4_con)[1]))==1,"Y",
                         ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(between_clin_old_name,rep(-999,dim(v4_con)[1]))==2,"N",-999))
                              
  itm_btw<-factor(itm_btw)
  assign(between_new_name,itm_btw,envir=.GlobalEnv)
  descT(itm_btw)}

Life events between study visits, was a preciptitating factor, Visit 2

#First argument w/o quotes, second with quotes
prcp_event_recode_v2<- function(prcp_clin_old_name,prcp_new_name) {
  
  itm_prcp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
  
  itm_prcp<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v2_con)[1]))==1,"Y",
                         ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v2_con)[1]))==2,"N",
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v2_con)[1]))==3,"U",-999))) 
                                
  itm_prcp<-factor(itm_prcp)
  assign(prcp_new_name,itm_prcp,envir=.GlobalEnv)
  descT(itm_prcp)}

Life events between study visits, was a preciptitating factor, Visit 3

#First argument w/o quotes, second with quotes
prcp_event_recode_v3<- function(prcp_clin_old_name,prcp_new_name) {
  
  itm_prcp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
  
  itm_prcp<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v3_con)[1]))==1,"Y",
                         ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v3_con)[1]))==2,"N",
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v3_con)[1]))==3,"U",-999))) 
                                
  itm_prcp<-factor(itm_prcp)
  assign(prcp_new_name,itm_prcp,envir=.GlobalEnv)
  descT(itm_prcp)}

Life events between study visits, was a preciptitating factor, Visit 4

#First argument w/o quotes, second with quotes
prcp_event_recode_v4<- function(prcp_clin_old_name,prcp_new_name) {
  
  itm_prcp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
  
  itm_prcp<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v4_con)[1]))==1,"Y",
                         ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v4_con)[1]))==2,"N",
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(prcp_clin_old_name,rep(-999,dim(v4_con)[1]))==3,"U",-999))) 
                                
  itm_prcp<-factor(itm_prcp)
  assign(prcp_new_name,itm_prcp,envir=.GlobalEnv)
  descT(itm_prcp)}

Childhood Trauma Screener

#First and second arguments w/o quotes, third with quotes
cts_recode <- function(cts_clin_old_name,cts_con_old_name,cts_new_name,recode) {
    
    itm_cts_chk_clin<-v3_clin$v3_chidlhood_verwer_fragebogen
    itm_cts_chk_con<-v3_con$v3_chidlhood_childhood_korrekt
    
    itm_cts_clin<-ifelse((is.na(itm_cts_chk_clin) | itm_cts_chk_clin!=2),
                  cts_clin_old_name,NA) 
    
    itm_cts_clin[itm_cts_clin==0]<-NA #important only with clinical participants!
    
    itm_cts_con<-ifelse((is.na(itm_cts_chk_con) | itm_cts_chk_con!=2),
                  cts_con_old_name,NA)               
    
    if(recode==0) {all_itm_cts<-factor(c(itm_cts_clin,itm_cts_con),ordered=T)}
    else          {all_itm_cts<-factor(6-c(itm_cts_clin,itm_cts_con),ordered=T)}
                      
    assign(cts_new_name,all_itm_cts,envir=.GlobalEnv)
    
    desc(all_itm_cts)}

Visit 1: Data preparation

Read in data of clinical participants and show number

## [1] 1223

Read in data of control participants and show number

## [1] 320

Visit 1: Recruitment data (code partly not shown)

Create participant identity variable (categorical [id], v1_id)

Create clinical/control status variable (categorical [stat], v1_stat)

Create date of interview variable (categorical [year-month-day], v1_interv_date)

Create recruitment center variable (categorical [see below], v1_center)

Clinical participants

Control participants

Conbine clincal and control participants Code as factor

v1_center<-factor(c(v1_clin_center,v1_con_center),ordered=F)

Number of participants (clinical and control combined) by recruitment center

descT(v1_center)
##                1   2   3   4   5   6  7   10  11  12  13  14   16  18  19 
## [1,] No. cases 19  39  11  5   41  62 32  7   13  36  13  257  370 101 100
## [2,] Percent   1.2 2.5 0.7 0.3 2.7 4  2.1 0.5 0.8 2.3 0.8 16.7 24  6.5 6.5
##      20  21  22  23      
## [1,] 147 148 98  44  1543
## [2,] 9.5 9.6 6.4 2.9 100
par(mar=c(5.1,4.1,2.1,0))
ctr<-barplot(table(v1_center),las=2,ylim=c(0,400),lwd=2,horiz=F,axisnames=F, ylab="Number of baseline interviews")
nmctr<-names(table(v1_center))
text(ctr, par("usr")[3], labels=nmctr, srt = 45, adj = c(1.1,1.1), xpd = TRUE, cex=.8)

Interviewers (categorical, v1_tstlt)

Interviewers were de-identified due to data protection requirements.

Clinical participants

Control participants

Conbine clincal and control participants

v1_tstlt<-as.factor(c(as.character(v1_clin_int),as.character(v1_con_int)))

Determine number of interviewers (teams counted as separate raters)

length(unique(v1_tstlt)) 
## [1] 86

Determine number of rater teams

length(unique(v1_tstlt)) #(NB: rater teams counted as separate interviewer)
## [1] 86

Create dataset

v1_rec<-data.frame(v1_stat,v1_center,v1_tstlt,v1_interv_date)

Visit 1: Demographic information

Sex (dichotomous [M,F], v1_sex)

v1_clin_sex<-ifelse(v1_clin$v1_demogr_s1_dem1_ses01_geschl==1,"M","F")
v1_con_sex<-ifelse(v1_con$v1_demo1_sex==1,"M","F")
v1_sex<-c(v1_clin_sex,v1_con_sex)
v1_sex<-as.factor(v1_sex)

descT(v1_sex) 
##                F    M        
## [1,] No. cases 758  785  1543
## [2,] Percent   49.1 50.9 100

Age at first interview (continuous [years], v1_age)

v1_age_years_clin<-as.numeric(substr(v1_clin$v1_ausschluss_rekr_datum,1,4))-as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))
v1_age_years_clin[v1_age_years_clin==-54]<-46 #correct age of one participant, typo in phenotype database

v1_age_years_con<-as.numeric(substr(v1_con$v1_rek_rekrdat,1,4))-as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v1_age_years<-c(v1_age_years_clin,v1_age_years_con)

v1_age<-ifelse(c(as.numeric(substr(v1_clin$v1_ausschluss_rekr_datum,5,6)),as.numeric(substr(v1_con$v1_rek_rekrdat,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v1_age_years-1,v1_age_years)
summary(v1_age) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    18.0    29.5    42.0    41.6    52.0    86.0

Year of birth (continuous [year], v1_yob)

v1_yob_clin<-as.integer(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))
v1_yob_con<-as.integer(substr(v1_con$v1_demo1_gebdat,1,4))
v1_yob<-c(v1_yob_clin,v1_yob_con)
summary(v1_yob) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1931    1962    1972    1973    1985    1998

Season of birth (categorical [spring, summer, fall, winter], v1_seas_birth)

v1_seas_birth_clin<-rep(NA,dim(v1_clin)[1])

v1_seas_birth_clin<-ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("03","04","05"), "Spring", 
                   ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("06","07","08"), "Summer",  
                      ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("09","10","11"), "Fall", 
                         ifelse(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6) %in% c("12","01","02"),"Winter", v1_seas_birth_clin))))

table(v1_seas_birth_clin)
## v1_seas_birth_clin
##   Fall Spring Summer Winter 
##    319    311    294    299
v1_seas_birth_con<-rep(NA,dim(v1_con)[1])

v1_seas_birth_con<-ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("03","04","05"), "Spring", 
                   ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("06","07","08"), "Summer",  
                      ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("09","10","11"), "Fall", 
                         ifelse(substr(v1_con$v1_demo1_gebdat,5,6) %in% c("12","01","02"),"Winter", v1_seas_birth_con))))

table(v1_seas_birth_con)
## v1_seas_birth_con
##   Fall Spring Summer Winter 
##     74     99     77     70
v1_seas_birth<-c(v1_seas_birth_clin,v1_seas_birth_con)

v1_seas_birth<-as.factor(v1_seas_birth)
table(v1_seas_birth)
## v1_seas_birth
##   Fall Spring Summer Winter 
##    393    410    371    369

Age of parents at birth

Age of mother at birth (continuous [years], v1_age_m_birth)

v1_age_m_birth_clin<-as.integer(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))-as.integer(
  substr(v1_clin$v1_demogr_s1_dem4_geb_m,1,4))

v1_age_m_birth_clin[v1_age_m_birth_clin==0]<-NA #set one case of 0 to NA

v1_age_m_birth_con<-as.integer(substr(v1_con$v1_demo1_gebdat,1,4))-as.integer(substr(v1_con$v1_demo1_mgebdat,1,4))

v1_age_m_birth<-c(v1_age_m_birth_clin,v1_age_m_birth_con)
summary(v1_age_m_birth)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   12.00   24.00   28.00   28.11   31.00   49.00     118

Age of father at birth (continuous [years], v1_age_f_birth)

v1_age_f_birth_clin<-as.integer(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))-as.integer(substr(v1_clin$v1_demogr_s1_dem5_geb_v,1,4))
v1_age_f_birth_con<-as.integer(substr(v1_con$v1_demo1_gebdat,1,4))-as.integer(substr(v1_con$v1_demo1_vgebdat,1,4))

v1_age_f_birth<-c(v1_age_f_birth_clin,v1_age_f_birth_con)
summary(v1_age_f_birth)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    13.0    27.0    31.0    31.9    36.0    62.0     195

Marital status (categorical [married, married but living separately, single, divorced, widowed], v1_marital_stat)

v1_mar_clin<-v1_clin$v1_demogr_s1_dem6_ses12_famstand
v1_mar_con<-v1_con$v1_demo1_famstand

cat_mar<-c("Married","Married_living_sep","Single","Divorced","Widowed")

v1_marital_stat_clin<-cat_mar[v1_mar_clin]
v1_marital_stat_con<-cat_mar[v1_mar_con]

v1_marital_stat<-as.factor(c(v1_marital_stat_clin,v1_marital_stat_con))
descT(v1_marital_stat)
##                Divorced Married Married_living_sep Single Widowed <NA>
## [1,] No. cases 213      343     67                 881    21      18  
## [2,] Percent   13.8     22.2    4.3                57.1   1.4     1.2 
##          
## [1,] 1543
## [2,] 100

Relationship status

“Do you currently have a partner?” (dichotomous, v1_partner)

v1_clin_partner<-ifelse(v1_clin$v1_demogr_s1_dem9_ses13_partner==1,"Y","N")
v1_con_partner<-ifelse(v1_con$v1_demo1_partner==1,"Y","N")

v1_partner<-as.factor(c(v1_clin_partner,v1_con_partner))
desc(v1_partner)
##                N    Y    NA's     
## [1,] No. cases 759  742  42   1543
## [2,] Percent   49.2 48.1 2.7  100

Children

Biological (continuous [number], v1_no_bio_chld)

v1_no_bio_chld<-c(v1_clin$v1_demogr_s1_dem10_ses15a_lkind,v1_con$v1_demo1_lkind)
descT(v1_no_bio_chld)
##                0    1    2    3   4   5   <NA>     
## [1,] No. cases 939  265  195  89  26  3   26   1543
## [2,] Percent   60.9 17.2 12.6 5.8 1.7 0.2 1.7  100

Non-biological

Adoptive children (continuous [number], v1_no_adpt_chld)

v1_no_adpt_chld<-c(v1_clin$v1_demogr_s1_dem11_ses15b_akind,v1_con$v1_demo1_adkind)
descT(v1_no_adpt_chld)  
##                0    1   2   <NA>     
## [1,] No. cases 1496 2   2   43   1543
## [2,] Percent   97   0.1 0.1 2.8  100

Step children (continuous [number], v1_stp_chld)

v1_stp_chld<-c(v1_clin$v1_demogr_s1_dem12_skind,v1_con$v1_demo1_stkind)
descT(v1_stp_chld)      
##                0    1   2   3   4   <NA>     
## [1,] No. cases 1439 24  26  7   2   45   1543
## [2,] Percent   93.3 1.6 1.7 0.5 0.1 2.9  100

Siblings

Full siblings

Brothers (continuous [number], v1_brothers)

v1_brothers<-c(v1_clin$v1_demogr_s1_dem13_brueder,v1_con$v1_demo1_bruder)
descT(v1_brothers)      
##                0    1   2    3  4   5   6   7   <NA>     
## [1,] No. cases 583  586 198  62 20  7   2   1   84   1543
## [2,] Percent   37.8 38  12.8 4  1.3 0.5 0.1 0.1 5.4  100

Sisters (continuous [number], v1_sisters)

v1_sisters<-c(v1_clin$v1_demogr_s1_dem13_schwestern,v1_con$v1_demo1_schwester)
descT(v1_sisters)       
##                0   1    2    3   4   5   6   7   8   <NA>     
## [1,] No. cases 648 543  182  49  7   11  2   2   1   98   1543
## [2,] Percent   42  35.2 11.8 3.2 0.5 0.7 0.1 0.1 0.1 6.4  100

Half-siblings

Half-brothers (continuous [number], v1_hlf_brthrs)

v1_hlf_brthrs<-c(v1_clin$v1_demogr_s1_dem13_halbbrueder,v1_con$v1_demo1_hbruder)
descT(v1_hlf_brthrs)    
##                0    1   2   3  4   5   6   <NA>     
## [1,] No. cases 1193 100 34  16 2   1   1   196  1543
## [2,] Percent   77.3 6.5 2.2 1  0.1 0.1 0.1 12.7 100

Half-sisters (continuous [number], v1_hlf_sstrs)

v1_hlf_sstrs<-c(v1_clin$v1_demogr_s1_dem13_halbschwestern,v1_con$v1_demo1_hschwester)
descT(v1_hlf_sstrs) 
##                0    1   2   3   4   5   6   9   <NA>     
## [1,] No. cases 1184 111 33  9   6   3   1   1   195  1543
## [2,] Percent   76.7 7.2 2.1 0.6 0.4 0.2 0.1 0.1 12.6 100

Non-biological siblings

Step-brothers (continuous [number], v1_stp_brthrs)

v1_stp_brthrs<-c(v1_clin$v1_demogr_s1_dem13_as_brueder,v1_con$v1_demo1_adbrueder)
descT(v1_stp_brthrs) 
##                0    1   2   3   <NA>     
## [1,] No. cases 1297 29  8   1   208  1543
## [2,] Percent   84.1 1.9 0.5 0.1 13.5 100

Step-sisters (continuous [number], v1_stp_sstrs)

v1_stp_sstrs<-c(v1_clin$v1_demogr_s1_dem13_as_schwestern,v1_con$v1_demo1_adschwester)
descT(v1_stp_sstrs)
##                0    1   2   3   <NA>     
## [1,] No. cases 1292 29  10  2   210  1543
## [2,] Percent   83.7 1.9 0.6 0.1 13.6 100

Twins

“Do you have a first degree relative who is a twin?” (dichotomous, v1_twin_fam)

v1_clin_twin_fam<-ifelse(v1_clin$v1_demogr_s1_dem14_zwillinge==1,"Y","N")   
v1_con_twin_fam<-ifelse(v1_con$v1_demo1_zwillinge==1,"Y","N")   
v1_twin_fam<-as.factor(c(v1_clin_twin_fam,v1_con_twin_fam))

descT(v1_twin_fam)
##                N    Y   <NA>     
## [1,] No. cases 1355 153 35   1543
## [2,] Percent   87.8 9.9 2.3  100

“Are you a twin yourself?” (dichotomous, v1_twin_slf)

v1_clin_twin_slf<-ifelse(v1_clin$v1_demogr_s1_dem15_selbst_zwill==1,"Y","N")
v1_con_twin_slf<-ifelse(v1_con$v1_demo1_szwilling==1,"Y","N")
v1_twin_slf<-as.factor(c(v1_clin_twin_slf,v1_con_twin_slf))

descT(v1_twin_slf)
##                N    Y  <NA>     
## [1,] No. cases 1464 47 32   1543
## [2,] Percent   94.9 3  2.1  100

Living alone (dichotomous, v1_liv_aln)

v1_clin_liv_aln<-ifelse(v1_clin$v1_demogr_s1_dem17_allein==1,"Y","N")   
v1_con_liv_aln<-ifelse(v1_con$v1_demo1_allein==1,"Y","N")   
v1_liv_aln<-as.factor(c(v1_clin_liv_aln,v1_con_liv_aln))

descT(v1_liv_aln)
##                N    Y        
## [1,] No. cases 958  585  1543
## [2,] Percent   62.1 37.9 100

Education

Status in the German educational system is assessed in detail. However, many specialized types of German schools are unknown to English-speaking investigators and detailed information does not seem to play a role. Furthermore, high-school and professional education are assessed seperately in the interview. In order to combine the aforementioned types of education, we have transformed both scales to values that can be added together to form an “Educational status” variable. High-school level education was transformed into an ordinal scale from 0 to 3 (people still in high school at the time of the interview are give NA). Professional eduction is also transformed into an ordinal scale from 0 to 3. These two scales are added together to give an ordinal educational status scale ranging from 0 to 6.

High-school level education (ordinal [0,1,2,3], v1_school)

The following transformation was used: “no information”/“missing”-NA, “no graduation”-0, “Hauptschule”-1, “Realschule”-2, “Polytechnische Oberschule”-2, “Fachhochschule”-3, “Allgemeine Hochschulreife”-3; still in school“/”other degree“- -999.

NB: Transformation to ordered factor below, after creation of v1_ed_status variable.

v1_clin_school<-rep(NA,dim(v1_clin)[1])
v1_con_school<-rep(NA,dim(v1_con)[1])

v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(1:2),v1_clin$v1_demogr_s2_dem18_ses18_sabschl-1,v1_clin_school)
v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(3:4),2,v1_clin_school)
v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(5:6),3,v1_clin_school)
v1_clin_school<-ifelse(v1_clin$v1_demogr_s2_dem18_ses18_sabschl %in% c(7:8),-999,v1_clin_school)

v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(1:2),v1_con$v1_demo2_abschl-1,v1_con_school)
v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(3:4),2,v1_con_school)
v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(5:6),3,v1_con_school)
v1_con_school<-ifelse(v1_con$v1_demo2_abschl %in% c(7:8),-999,v1_con_school)

v1_school<-c(v1_clin_school,v1_con_school)
descT(v1_school)
##                -999 0   1   2   3    <NA>     
## [1,] No. cases 17   24  278 371 838  15   1543
## [2,] Percent   1.1  1.6 18  24  54.3 1    100

Professional education (ordinal [0,1,2,3], v1_prof_dgr)

The following transformation was used: missing or no information-NA, “no professional education”/“beruflich-betriebliche Anlernzeit, aber keine Lehre; Teilfacharbeiterabschluss”/“in professional education”-0, “Lehre”-1, “beruflich-betriebliche Ausbildung”/“beruflich-schulische Ausbildung”-2, “Fachhochschul-/Universitätsabschluss”-3, “other professional degree” - -999,

NB: Transformation to ordered factor below, after creation of v1_ed_status variable.

v1_clin_prof_dgr<-rep(NA,dim(v1_clin)[1])
v1_con_prof_dgr<-rep(NA,dim(v1_con)[1])

v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_aba==1,-999,v1_clin_prof_dgr) 
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_kba==1,0,v1_clin_prof_dgr)    
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_anlern==1,0,v1_clin_prof_dgr) 
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_in_ausb==1,0,v1_clin_prof_dgr)
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_lehre==1,1,v1_clin_prof_dgr)  
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_ausb==1,2,v1_clin_prof_dgr)   
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_fachs==1,2,v1_clin_prof_dgr)  
v1_clin_prof_dgr<-ifelse(v1_clin$v1_demogr_s2_dem19_ses19_fh_uni==1,3,v1_clin_prof_dgr)

v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_a_babschl==1,-999,v1_con_prof_dgr)  
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_kba==1,0,v1_con_prof_dgr)   
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_anlern==1,0,v1_con_prof_dgr)    
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_ausbild==1,0,v1_con_prof_dgr)
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_lehre==1,1,v1_con_prof_dgr) 
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_ausb==1,2,v1_con_prof_dgr)  
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_fachs==1,2,v1_con_prof_dgr) 
v1_con_prof_dgr<-ifelse(v1_con$v1_demo2_fhuni==1,3,v1_con_prof_dgr)

v1_prof_dgr<-c(v1_clin_prof_dgr,v1_con_prof_dgr)
descT(v1_prof_dgr)
##                -999 0    1    2    3    <NA>     
## [1,] No. cases 9    398  406  332  379  19   1543
## [2,] Percent   0.6  25.8 26.3 21.5 24.6 1.2  100

Important: more than one answer is possible, as people may have several professional degrees. The order of the commands above makes sure higher professional degrees overwrite lower ones.

Educational status scale (ordinal [0,1,2,3,4,5,6], v1_ed_status)

As describe above, this scale was newly created.

v1_ed_status<-v1_school+v1_prof_dgr
v1_ed_status[v1_ed_status<0]<-NA
v1_ed_status<-factor(v1_ed_status, ordered=T)

descT(v1_ed_status)
##                0   1   2    3    4    5    6    <NA>     
## [1,] No. cases 19  82  239  390  236  164  366  47   1543
## [2,] Percent   1.2 5.3 15.5 25.3 15.3 10.6 23.7 3    100

Transform school and professional degree variables to factors.

v1_school<-factor(v1_school, ordered=T)
v1_prof_dgr<-factor(v1_prof_dgr, ordered=T)

Employment

Currently paid employment (dichotomous, v1_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v1_clin_curr_paid_empl<-rep(NA,dim(v1_clin)[1])
v1_con_curr_paid_empl<-rep(NA,dim(v1_con)[1])

v1_clin_curr_paid_empl<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_erwbtaet %in% c(1:5,7:10),"Y",v1_clin_curr_paid_empl)
v1_clin_curr_paid_empl<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_erwbtaet %in% c(6,11),"N",v1_clin_curr_paid_empl)   

v1_con_curr_paid_empl<-ifelse(v1_con$v1_demo2_erwerb %in% c(1:5,7:10),"Y",v1_con_curr_paid_empl)
v1_con_curr_paid_empl<-ifelse(v1_con$v1_demo2_erwerb %in% c(6,11),"N",v1_con_curr_paid_empl)    

v1_curr_paid_empl<-c(v1_clin_curr_paid_empl,v1_con_curr_paid_empl)
v1_curr_paid_empl<-as.factor(v1_curr_paid_empl)
descT(v1_curr_paid_empl)
##                N    Y    <NA>     
## [1,] No. cases 815  706  22   1543
## [2,] Percent   52.8 45.8 1.4  100

Disability pension due to psychological/psychiatric illness (dichotomous, v1_disabl_pens)

v1_clin_disabl_pens<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_rente==1,"Y","N")      
v1_con_disabl_pens<-ifelse(v1_con$v1_demo2_rente==1,"Y","N")        

v1_disabl_pens<-as.factor(c(v1_clin_disabl_pens,v1_con_disabl_pens))
descT(v1_disabl_pens)
##                N    Y    <NA>     
## [1,] No. cases 764  381  398  1543
## [2,] Percent   49.5 24.7 25.8 100

Employed in workshop for handicapped persons (dichotomous, v1_spec_emp)

v1_clin_spec_emp<-ifelse(v1_clin$v1_demogr_s2_dem20_ses20_werk==1,"Y","N")          
v1_con_spec_emp<-ifelse(v1_con$v1_demo2_wfbm==1,"Y","N")            

v1_spec_emp<-as.factor(c(v1_clin_spec_emp,v1_con_spec_emp))
descT(v1_spec_emp)
##                N    Y   <NA>     
## [1,] No. cases 549  74  920  1543
## [2,] Percent   35.6 4.8 59.6 100

Months of work absence due to psychological distress in past five years (continuous [months], v1_wrk_abs_pst_5_yrs)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >60 months are set to -999.

v1_clin_wrk_abs_pst_5_yrs<-ifelse((v1_clin$v1_demogr_s2_dem23_unbekannt==1 | v1_clin$v1_demogr_s2_dem23_rente==1 | v1_clin$v1_demogr_s2_dem23_arbeitsausf>60),-999, v1_clin$v1_demogr_s2_dem23_arbeitsausf)

v1_con_wrk_abs_pst_5_yrs<-ifelse((v1_con$v1_demo2_ausfallu==1 | v1_con$v1_demo2_rente==1 | v1_con$v1_demo2_ausfallm>60),-999, v1_con$v1_demo2_ausfallm) 

v1_wrk_abs_pst_5_yrs<-c(v1_clin_wrk_abs_pst_5_yrs,v1_con_wrk_abs_pst_5_yrs)
descT(v1_wrk_abs_pst_5_yrs)
##                -999 0    1   2   3   4   5  6   7   8   9   10  11  12 
## [1,] No. cases 507  334  51  65  43  32  31 50  9   20  13  19  3   40 
## [2,] Percent   32.9 21.6 3.3 4.2 2.8 2.1 2  3.2 0.6 1.3 0.8 1.2 0.2 2.6
##      13  14  15  16  17  18  20  21  22  23  24 25  26  27  28  30  33 
## [1,] 6   3   8   5   3   23  7   1   3   1   31 1   1   2   1   13  1  
## [2,] 0.4 0.2 0.5 0.3 0.2 1.5 0.5 0.1 0.2 0.1 2  0.1 0.1 0.1 0.1 0.8 0.1
##      35  36  38  42  45  48  50  52  54  60  <NA>     
## [1,] 2   14  1   3   1   13  2   1   1   39  139  1543
## [2,] 0.1 0.9 0.1 0.2 0.1 0.8 0.1 0.1 0.1 2.5 9    100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v1_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v1_clin_cur_work_restr<-ifelse(v1_clin$v1_demogr_s2_dem24_arbeitseinschr==1,"Y","N")    
v1_con_cur_work_restr<-ifelse(v1_con$v1_demo2_psyeinsch==1,"Y","N") 
v1_cur_work_restr<-as.factor(c(v1_clin_cur_work_restr,v1_con_cur_work_restr))

descT(v1_cur_work_restr)
##                N    Y    <NA>     
## [1,] No. cases 652  798  93   1543
## [2,] Percent   42.3 51.7 6    100

Create dataset

v1_dem<-data.frame(v1_sex,v1_age,v1_yob,v1_seas_birth,v1_age_m_birth,v1_age_f_birth,
                   v1_marital_stat,v1_partner,v1_no_bio_chld,v1_no_adpt_chld,
                   v1_stp_chld,v1_brothers,v1_sisters,v1_hlf_brthrs,v1_hlf_sstrs,
                   v1_stp_brthrs,v1_stp_sstrs,v1_twin_fam,v1_twin_slf,v1_liv_aln,
                   v1_school,v1_prof_dgr,v1_ed_status,v1_curr_paid_empl,
                   v1_disabl_pens,v1_spec_emp,v1_wrk_abs_pst_5_yrs,v1_cur_work_restr)

Visit 1: Ethnicity

Country of birth (categorical [country], v1_cntr_brth)

v1_clin_cntr_brth<-v1_clin$v1_demogr_s2_dem25_ses03a_lst_lnd
v1_clin_cntr_brth<-ifelse(is.na(v1_clin_cntr_brth) & v1_clin$v1_demogr_s2_dem25_ses03a_land_st==1, 
                     "Deutschland",as.character(v1_clin_cntr_brth))

v1_con_cntr_brth<-v1_con$v1_demo2_land1 
v1_con_cntr_brth<-ifelse(is.na(v1_con_cntr_brth) & v1_con$v1_demo2_gebort==1, 
                     "Deutschland",as.character(v1_con$v1_con_cntr_brth))
v1_cntr_brth<-as.factor(c(v1_clin_cntr_brth,v1_con_cntr_brth))
descT(v1_cntr_brth)
##                Afghanistan Ägypten anderes Land Argentinien Äthiopien
## [1,] No. cases 1           1       1            1           2        
## [2,] Percent   0.1         0.1     0.1          0.1         0.1      
##      Australien Belarus (Weißrussland) Bosnien und Herzegowina Deutschland
## [1,] 2          1                      1                       1233       
## [2,] 0.1        0.1                    0.1                     79.9       
##      Eritrea Estland Finnland Frankreich Irak Iran, Islamische Republik
## [1,] 2       1       1        2          1    3                        
## [2,] 0.1     0.1     0.1      0.1        0.1  0.2                      
##      Italien Kasachstan Kirgisistan Kroatien Marokko
## [1,] 1       9          2           3        1      
## [2,] 0.1     0.6        0.1         0.2      0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Niederlande Nigeria
## [1,] 2                                        1        1           1      
## [2,] 0.1                                      0.1      0.1         0.1    
##      Österreich Pakistan Polen Rumänien Russische Föderation Senegal
## [1,] 134        1        25    10       11                   1      
## [2,] 8.7        0.1      1.6   0.6      0.7                  0.1    
##      Serbien Slowakei Slowenien Sri Lanka Tadschikistan Thailand
## [1,] 7       1        1         1         1             1       
## [2,] 0.5     0.1      0.1       0.1       0.1           0.1     
##      Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 3                     8      1       2      1         
## [2,] 0.2                   0.5    0.1     0.1    0.1       
##      Vereinigte Staaten von Amerika
## [1,] 1                             
## [2,] 0.1                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 2                                                    57   1543
## [2,] 0.1                                                  3.7  100

Country of birth mother (categorical [country], v1_cntr_brth_m)

v1_clin_cntr_brth_m<-v1_clin$v1_demogr_s2_dem26_ses06_lm_lnd
v1_clin_cntr_brth_m<-ifelse(is.na(v1_clin_cntr_brth_m) & v1_clin$v1_demogr_s2_dem26_ses06_land_m==1, 
                       "Deutschland",as.character(v1_clin_cntr_brth_m))

v1_con_cntr_brth_m<-v1_con$v1_demo2_land2
v1_con_cntr_brth_m<-ifelse(is.na(v1_con_cntr_brth_m) & v1_con$v1_demo2_gebortm==1, 
                       "Deutschland",as.character(v1_con_cntr_brth_m))
v1_cntr_brth_m<-as.factor(c(v1_clin_cntr_brth_m,v1_con_cntr_brth_m))
descT(v1_cntr_brth_m)
##                Afghanistan Algerien anderes Land Argentinien Äthiopien
## [1,] No. cases 1           1        2            1           2        
## [2,] Percent   0.1         0.1      0.1          0.1         0.1      
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Brasilien
## [1,] 2                      1       5                       1        
## [2,] 0.1                    0.1     0.3                     0.1      
##      Bulgarien Chile Dänemark Deutschland Eritrea Estland Finnland
## [1,] 2         2     1        1047        2       2       1       
## [2,] 0.1       0.1   0.1      67.9        0.1     0.1     0.1     
##      Frankreich Griechenland Indien Indonesien Irak
## [1,] 2          1            1      1          1   
## [2,] 0.1        0.1          0.1    0.1        0.1 
##      Iran, Islamische Republik Irland Israel Italien Japan Kasachstan
## [1,] 5                         1      1      7       1     10        
## [2,] 0.3                       0.1    0.1    0.5     0.1   0.6       
##      Kirgisistan Korea, Republik (Südkorea) Kroatien
## [1,] 1           1                          10      
## [2,] 0.1         0.1                        0.6     
##      Libysch-Arabische Dschamahirija (Libyen) Luxemburg Marokko
## [1,] 1                                        3         1      
## [2,] 0.1                                      0.2       0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3                                        1        2       1          
## [2,] 0.2                                      0.1      0.1     0.1        
##      Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 136        1        95    14       25                  
## [2,] 8.8        0.1      6.2   0.9      1.6                 
##      Schweiz (Confoederatio Helvetica) Senegal Serbien Singapur Slowakei
## [1,] 2                                 1       13      1        3       
## [2,] 0.1                               0.1     0.8     0.1      0.2     
##      Slowenien Spanien Sri Lanka Thailand Tschechische Republik Türkei
## [1,] 5         3       3         1        24                    19    
## [2,] 0.3       0.2     0.2       0.1      1.6                   1.2   
##      Ukraine Ungarn Usbekistan
## [1,] 10      11     1         
## [2,] 0.6     0.7    0.1       
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 2                                                    45   1543
## [2,] 0.1                                                  2.9  100

Country of birth father (categorical [country], v1_cntr_brth_f)

v1_clin_cntr_brth_f<-v1_clin$v1_demogr_s2_dem27_ses07_lv_lnd
v1_clin_cntr_brth_f<-ifelse(is.na(v1_clin_cntr_brth_f)==T & v1_clin$v1_demogr_s2_dem27_ses07_land_v==1, 
                       "Deutschland",as.character(v1_clin_cntr_brth_f))

v1_con_cntr_brth_f<-v1_con$v1_demo2_land3
v1_con_cntr_brth_f<-ifelse(is.na(v1_con_cntr_brth_f)==T & v1_con$v1_demo2_gebortv==1, 
                       "Deutschland",as.character(v1_con_cntr_brth_f))

v1_cntr_brth_f<-as.factor(c(v1_clin_cntr_brth_f,v1_con_cntr_brth_f))
descT(v1_cntr_brth_f)
##                Afghanistan Ägypten anderes Land Argentinien Armenien
## [1,] No. cases 1           1       4            3           1       
## [2,] Percent   0.1         0.1     0.3          0.2         0.1     
##      Äthiopien Australien Belarus (Weißrussland) Belgien
## [1,] 1         2          1                      2      
## [2,] 0.1       0.1        0.1                    0.1    
##      Bosnien und Herzegowina Bulgarien Chile Deutschland
## [1,] 3                       3         3     1008       
## [2,] 0.2                     0.2       0.2   65.3       
##      Dominikanische Republik Eritrea Estland Frankreich Griechenland
## [1,] 1                       3       2       5          1           
## [2,] 0.1                     0.2     0.1     0.3        0.1         
##      Indien Indonesien Irak Iran, Islamische Republik Israel Italien Japan
## [1,] 1      1          1    5                         1      9       2    
## [2,] 0.1    0.1        0.1  0.3                       0.1    0.6     0.1  
##      Jemen Kasachstan Korea, Republik (Südkorea) Kroatien Libanon
## [1,] 1     9          1                          5        1      
## [2,] 0.1   0.6        0.1                        0.3      0.1    
##      Luxemburg Marokko Mazedonien, ehem. jugoslawische Republik Mongolei
## [1,] 1         2       3                                        1       
## [2,] 0.1       0.1     0.2                                      0.1     
##      Namibia Nigeria Norwegen Österreich Pakistan
## [1,] 2       1       1        143        2       
## [2,] 0.1     0.1     0.1      9.3        0.1     
##      Palästinensische Autonomiegebiete Polen Rumänien Russische Föderation
## [1,] 1                                 111   13       32                  
## [2,] 0.1                               7.2   0.8      2.1                 
##      Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Tadschikistan
## [1,] 1       9       2        3         1       2         1            
## [2,] 0.1     0.6     0.1      0.2       0.1     0.1       0.1          
##      Thailand Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 1        22                    23     8       6      1         
## [2,] 0.1      1.4                   1.5    0.5     0.4    0.1       
##      Vereinigte Staaten von Amerika
## [1,] 5                             
## [2,] 0.3                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 4                                                    59   1543
## [2,] 0.3                                                  3.8  100

Country of birth grandmother, maternal side (categorical [country], v1_cntr_brth_gmm)

v1_clin_cntr_brth_gmm<-v1_clin$v1_demogr_s2_dem28_land_gm_ms_lnd
v1_clin_cntr_brth_gmm<-ifelse(is.na(v1_clin_cntr_brth_gmm)==T & v1_clin$v1_demogr_s2_dem28_land_gm_ms==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gmm))

v1_con_cntr_brth_gmm<-v1_con$v1_demo2_land4
v1_con_cntr_brth_gmm<-ifelse(is.na(v1_con_cntr_brth_gmm)==T & v1_con$v1_demo2_gebortgmm==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gmm))

v1_cntr_brth_gmm<-as.factor(c(v1_clin_cntr_brth_gmm,v1_con_cntr_brth_gmm))
descT(v1_cntr_brth_gmm)
##                Afghanistan anderes Land Argentinien Äthiopien
## [1,] No. cases 1           2            1           1        
## [2,] Percent   0.1         0.1          0.1         0.1      
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien
## [1,] 2                      1       6                       1        
## [2,] 0.1                    0.1     0.4                     0.1      
##      Chile China, Volksrepublik Dänemark Deutschland Eritrea Estland
## [1,] 2     1                    2        867         2       2      
## [2,] 0.1   0.1                  0.1      56.2        0.1     0.1    
##      Finnland Frankreich Georgien Griechenland Indien Indonesien Irak
## [1,] 2        1          1        3            2      1          1   
## [2,] 0.1      0.1        0.1      0.2          0.1    0.1        0.1 
##      Iran, Islamische Republik Israel Italien Japan Kasachstan Kolumbien
## [1,] 5                         2      7       1     5          1        
## [2,] 0.3                       0.1    0.5     0.1   0.3        0.1      
##      Korea, Republik (Südkorea) Kroatien Litauen Luxemburg Marokko
## [1,] 1                          10       2       3         1      
## [2,] 0.1                        0.6      0.1     0.2       0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3                                        1        1       3          
## [2,] 0.2                                      0.1      0.1     0.2        
##      Österreich Pakistan Polen Rumänien Russische Föderation Schweden
## [1,] 127        1        124   10       23                   1       
## [2,] 8.2        0.1      8     0.6      1.5                  0.1     
##      Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei Slowenien
## [1,] 3                                 1       13      2        6        
## [2,] 0.2                               0.1     0.8     0.1      0.4      
##      Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 4       3         1        32                    17     15     
## [2,] 0.3     0.2       0.1      2.1                   1.1    1      
##      Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 13     1          1                             
## [2,] 0.8    0.1        0.1                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 2                                                    195  1543
## [2,] 0.1                                                  12.6 100

Country of birth grandfather, maternal side (catergorical [country], v1_cntr_brth_gfm)

v1_clin_cntr_brth_gfm<-v1_clin$v1_demogr_s2_dem29_land_gv_ms_lnd
v1_clin_cntr_brth_gfm<-ifelse(is.na(v1_clin_cntr_brth_gfm)==T & v1_clin$v1_demogr_s2_dem29_land_gv_ms==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gfm))

v1_con_cntr_brth_gfm<-v1_con$v1_demo2_land5
v1_con_cntr_brth_gfm<-ifelse(is.na(v1_con_cntr_brth_gfm)==T & v1_con$v1_demo2_gebortgmv==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gfm))

v1_cntr_brth_gfm<-as.factor(c(v1_clin_cntr_brth_gfm,v1_con_cntr_brth_gfm))
descT(v1_cntr_brth_gfm)
##                Afghanistan anderes Land Argentinien Aserbaidschan
## [1,] No. cases 1           2            1           1            
## [2,] Percent   0.1         0.1          0.1         0.1          
##      Äthiopien Belarus (Weißrussland) Belgien Bosnien und Herzegowina
## [1,] 1         3                      1       6                      
## [2,] 0.1       0.2                    0.1     0.4                    
##      Bulgarien Chile China, Volksrepublik Dänemark Deutschland Eritrea
## [1,] 1         2     2                    1        798         2      
## [2,] 0.1       0.1   0.1                  0.1      51.7        0.1    
##      Estland Finnland Frankreich Georgien Griechenland Indien Indonesien
## [1,] 2       1        3          1        4            2      1         
## [2,] 0.1     0.1      0.2        0.1      0.3          0.1    0.1       
##      Irak Iran, Islamische Republik Israel Italien Japan Kasachstan
## [1,] 1    5                         2      6       1     5         
## [2,] 0.1  0.3                       0.1    0.4     0.1   0.3       
##      Korea, Demokratische Volksrepublik (Nordkorea)
## [1,] 1                                             
## [2,] 0.1                                           
##      Korea, Republik (Südkorea) Kroatien Luxemburg Marokko
## [1,] 1                          10       3         1      
## [2,] 0.1                        0.6      0.2       0.1    
##      Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3                                        1        2       4          
## [2,] 0.2                                      0.1      0.1     0.3        
##      Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 123        1        114   9        27                  
## [2,] 8          0.1      7.4   0.6      1.7                 
##      Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei Slowenien
## [1,] 2                                 1       12      3        5        
## [2,] 0.1                               0.1     0.8     0.2      0.3      
##      Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 4       3         1        23                    18     8      
## [2,] 0.3     0.2       0.1      1.5                   1.2    0.5    
##      Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 14     1          1                             
## [2,] 0.9    0.1        0.1                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 1                                                    290  1543
## [2,] 0.1                                                  18.8 100

Country of birth grandmother, paternal side (catergorical [country], v1_cntr_brth_gmf)

v1_clin_cntr_brth_gmf<-v1_clin$v1_demogr_s2_dem30_land_gm_vs_lnd
v1_clin_cntr_brth_gmf<-ifelse(is.na(v1_clin_cntr_brth_gmf)==T & v1_clin$v1_demogr_s2_dem30_land_gm_vs==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gmf))

v1_con_cntr_brth_gmf<-v1_con$v1_demo2_land6
v1_con_cntr_brth_gmf<-ifelse(is.na(v1_con_cntr_brth_gmf)==T & v1_con$v1_demo2_gebortgvm==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gmf))

v1_cntr_brth_gmf<-as.factor(c(v1_clin_cntr_brth_gmf,v1_con_cntr_brth_gmf))
descT(v1_cntr_brth_gmf)
##                Afghanistan Ägypten anderes Land Argentinien Australien
## [1,] No. cases 1           1       5            1           1         
## [2,] Percent   0.1         0.1     0.3          0.1         0.1       
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien
## [1,] 1                      1       3                       2        
## [2,] 0.1                    0.1     0.2                     0.1      
##      Chile Dänemark Deutschland Dominikanische Republik Eritrea Estland
## [1,] 3     1        790         1                       3       2      
## [2,] 0.2   0.1      51.2        0.1                     0.2     0.1    
##      Frankreich Ghana Griechenland Indien Irak Iran, Islamische Republik
## [1,] 6          1     3            2      1    5                        
## [2,] 0.4        0.1   0.2          0.1    0.1  0.3                      
##      Israel Italien Japan Jemen Kasachstan Korea, Republik (Südkorea)
## [1,] 1      9       2     1     3          1                         
## [2,] 0.1    0.6     0.1   0.1   0.2        0.1                       
##      Kroatien Libanon Luxemburg Marokko
## [1,] 4        2       1         1      
## [2,] 0.3      0.1     0.1       0.1    
##      Mazedonien, ehem. jugoslawische Republik Moldawien (Republik Moldau)
## [1,] 3                                        2                          
## [2,] 0.2                                      0.1                        
##      Mongolei Namibia Niederlande Nigeria Norwegen Österreich Pakistan
## [1,] 2        1       1           1       1        135        1       
## [2,] 0.1      0.1     0.1         0.1     0.1      8.7        0.1     
##      Polen Rumänien Russische Föderation Schweiz (Confoederatio Helvetica)
## [1,] 107   11       31                   1                                
## [2,] 6.9   0.7      2                    0.1                              
##      Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Südafrika
## [1,] 1       10      2        4         3       2         1        
## [2,] 0.1     0.6     0.1      0.3       0.2     0.1       0.1      
##      Thailand Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 1        28                    20     8       8      1         
## [2,] 0.1      1.8                   1.3    0.5     0.5    0.1       
##      Vereinigte Staaten von Amerika
## [1,] 4                             
## [2,] 0.3                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 4                                                    290  1543
## [2,] 0.3                                                  18.8 100

Country of birth grandfather, paternal side (catergorical [country], v1_cntr_brth_gff)

v1_clin_cntr_brth_gff<-v1_clin$v1_demogr_s2_dem31_land_gv_vs_lnd
v1_clin_cntr_brth_gff<-ifelse(is.na(v1_clin_cntr_brth_gff)==T & v1_clin$v1_demogr_s2_dem31_land_gv_vs==1, 
                         "Deutschland",as.character(v1_clin_cntr_brth_gff))

v1_con_cntr_brth_gff<-v1_con$v1_demo2_land7
v1_con_cntr_brth_gff<-ifelse(is.na(v1_con_cntr_brth_gff)==T & v1_con$v1_demo2_gebortgvv==1, 
                         "Deutschland",as.character(v1_con_cntr_brth_gff))

v1_cntr_brth_gff<-as.factor(c(v1_clin_cntr_brth_gff,v1_con_cntr_brth_gff))
descT(v1_cntr_brth_gff)
##                Afghanistan Ägypten Algerien anderes Land Australien
## [1,] No. cases 1           1       1        5            1         
## [2,] Percent   0.1         0.1     0.1      0.3          0.1       
##      Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien
## [1,] 1                      2       3                       2        
## [2,] 0.1                    0.1     0.2                     0.1      
##      Chile Deutschland Eritrea Estland Frankreich Ghana Griechenland
## [1,] 4     819         3       1       5          1     1           
## [2,] 0.3   53.1        0.2     0.1     0.3        0.1   0.1         
##      Indien Irak Iran, Islamische Republik Israel Italien Japan Jemen
## [1,] 2      1    6                         1      7       2     1    
## [2,] 0.1    0.1  0.4                       0.1    0.5     0.1   0.1  
##      Kasachstan Korea, Republik (Südkorea) Kroatien Litauen Luxemburg
## [1,] 3          1                          5        2       1        
## [2,] 0.2        0.1                        0.3      0.1     0.1      
##      Marokko Mazedonien, ehem. jugoslawische Republik Mongolei Namibia
## [1,] 1       3                                        1        2      
## [2,] 0.1     0.2                                      0.1      0.1    
##      Niederlande Nigeria Norwegen Österreich Pakistan
## [1,] 1           1       1        136        1       
## [2,] 0.1         0.1     0.1      8.8        0.1     
##      Palästinensische Autonomiegebiete Polen Rumänien Russische Föderation
## [1,] 2                                 107   11       31                  
## [2,] 0.1                               6.9   0.7      2                   
##      Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Thailand
## [1,] 1       9       3        5         3       2         1       
## [2,] 0.1     0.6     0.2      0.3       0.2     0.1       0.1     
##      Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 25                    21     7       8      1         
## [2,] 1.6                   1.4    0.5     0.5    0.1       
##      Vereinigte Staaten von Amerika
## [1,] 5                             
## [2,] 0.3                           
##      Vereinigtes Königreich Großbritannien und Nordirland <NA>     
## [1,] 3                                                    268  1543
## [2,] 0.2                                                  17.4 100

Create dataset

v1_eth<-data.frame(v1_cntr_brth,v1_cntr_brth_m,v1_cntr_brth_f,v1_cntr_brth_gmm,
                  v1_cntr_brth_gfm,v1_cntr_brth_gmf,v1_cntr_brth_gff)

Visit 1: Psychiatric history

Current psychiatric treatment (ordinal [1,2,3,4], v1_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v1_clin_cur_psy_trm<-rep(NA,dim(v1_clin)[1])
v1_con_cur_psy_trm<-rep(NA,dim(v1_con)[1])

v1_clin_cur_psy_trm<-ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==0,"1",
                        ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==3,"2", 
                          ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==2,"3",
                            ifelse(v1_clin$v1_psy_vorg_akt_pva1_akt_behand==1,"4",v1_clin_cur_psy_trm)))) 

v1_con_cur_psy_trm<-ifelse(v1_con$v1_psyaktuel_aktbehand==0,"1",
                      ifelse(v1_con$v1_psyaktuel_aktbehand==3,"2",
                        ifelse(v1_con$v1_psyaktuel_aktbehand==2,"3",
                          ifelse(v1_con$v1_psyaktuel_aktbehand==1,"4",v1_con_cur_psy_trm))))

v1_cur_psy_trm<-ordered(as.factor(c(v1_clin_cur_psy_trm,v1_con_cur_psy_trm)))
descT(v1_cur_psy_trm)
##                1    2    3   4    <NA>     
## [1,] No. cases 352  585  73  513  20   1543
## [2,] Percent   22.8 37.9 4.7 33.2 1.3  100

Ever treated as outpatient due to any mental health reason (ordinal [1,2,3,4], v1_outpat_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, consultation or short treatment”-2, “yes, continuous treatment for six months or numerous short episodes”-3, “yes, continuous treatment for several years or many short episodes”-4. The option “No information” was coded as “-999”.

v1_clin_outpat_psy_trm<-ifelse(v1_clin$v1_psy_vorg_akt_pva2_jemals_behand==0,-999,v1_clin$v1_psy_vorg_akt_pva2_jemals_behand)
v1_con_outpat_psy_trm<-ifelse(v1_con$v1_psyaktuel_jembehand==0,-999,v1_con$v1_psyaktuel_jembehand)

v1_outpat_psy_trm<-factor(c(v1_clin_outpat_psy_trm,v1_con_outpat_psy_trm),ordered=T)
summary(v1_outpat_psy_trm)
## -999    1    2    3    4 NA's 
##   13  320  152  134  909   15

Age at first ambulatory treatment due to any mental health reason (continuous [years], v1_age_1st_out_trm)

If controls were never treated for any mental health reason, they are coded as -999.

v1_age_1st_out_trm<-c(v1_clin$v1_psy_vorg_akt_pva3_alter_jahre,
                      ifelse(is.na(v1_con$v1_psyaktuel_alterj),-999,v1_con$v1_psyaktuel_alterj))
summary(v1_age_1st_out_trm)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -999.0    16.0    23.0  -166.9    32.0    73.0     103

Ever treated as inpatient or daypatient due to any mental health reason (dichotomous, v1_daypat_inpat_trm)

v1_clin_daypat_inpat_trm<-ifelse(v1_clin$v1_psy_vorg_akt_pva4_teilst_behand==1, "Y","N") 
v1_con_daypat_inpat_trm<-ifelse(v1_con$v1_psyaktuel_tstbehand==1, "Y","N") 
                           
v1_daypat_inpat_trm<-factor(c(v1_clin_daypat_inpat_trm,v1_con_daypat_inpat_trm))
descT(v1_daypat_inpat_trm)
##                N    Y    <NA>     
## [1,] No. cases 338  1187 18   1543
## [2,] Percent   21.9 76.9 1.2  100

Age at first inpatient treatment due to any mental health reason (continuous [years], v1_age_1st_inpat_trm)

v1_age_1st_inpat_trm<-c(v1_clin$v1_psy_vorg_akt_pva5_alter_jahre,
                      ifelse(is.na(v1_con$v1_psyaktuel_stalterj),-999,v1_con$v1_psyaktuel_stalterj))

summary(v1_age_1st_inpat_trm[v1_age_1st_inpat_trm>0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.00   22.00   27.00   30.27   37.50   73.00      67

Duration of illness (continuous [years], v1_dur_illness)

This is a newly created variable by subtracting age at first inpatient treatment from age at first visit (unit is years). Please note that this assumes that the patient was hospitalized for the diagnosis given in here and may therefore not be entirely accurate. In control individuals this was set to -999.

v1_dur_illness_clin<-v1_age[v1_stat=="CLINICAL"]-v1_age_1st_inpat_trm[v1_stat=="CLINICAL"]
table(v1_dur_illness_clin)
## v1_dur_illness_clin
##  -1   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16 
##   8 103  69  55  51  41  50  36  43  45  44  43  52  37  47  33  32  41 
##  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34 
##  24  17  20  21  19  26  13  14  16  12  21  13  17   8  14   7   5   9 
##  35  36  37  38  39  40  41  42  43  44  45  46  47  50  51  52  53 
##   4   5   4   4   5   9   1   4   3   2   2   1   2   1   1   1   1
#Set negative values to NA
v1_dur_illness_clin[v1_dur_illness_clin<0]<-NA 
table(v1_dur_illness_clin)
## v1_dur_illness_clin
##   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17 
## 103  69  55  51  41  50  36  43  45  44  43  52  37  47  33  32  41  24 
##  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35 
##  17  20  21  19  26  13  14  16  12  21  13  17   8  14   7   5   9   4 
##  36  37  38  39  40  41  42  43  44  45  46  47  50  51  52  53 
##   5   4   4   5   9   1   4   3   2   2   1   2   1   1   1   1
#Set controls to -999
v1_dur_illness_con<-rep(-999,dim(v1_con)[1])

v1_dur_illness<-c(v1_dur_illness_clin,v1_dur_illness_con)
table(v1_dur_illness)
## v1_dur_illness
## -999    0    1    2    3    4    5    6    7    8    9   10   11   12   13 
##  320  103   69   55   51   41   50   36   43   45   44   43   52   37   47 
##   14   15   16   17   18   19   20   21   22   23   24   25   26   27   28 
##   33   32   41   24   17   20   21   19   26   13   14   16   12   21   13 
##   29   30   31   32   33   34   35   36   37   38   39   40   41   42   43 
##   17    8   14    7    5    9    4    5    4    4    5    9    1    4    3 
##   44   45   46   47   50   51   52   53 
##    2    2    1    2    1    1    1    1

First-episode patient (only in clinical participants) (dichotomous, v1_1st_ep)

This is a newly created variable. Clinical participants with duration of illness of zero years are labeled as first-episode. Control participants have “-999”.

v1_1st_ep_con<-rep(-999,dim(v1_con)[1])
v1_1st_ep_clin<-ifelse(v1_dur_illness_clin==0,"Y","N")

v1_1st_ep<-factor(c(v1_1st_ep_clin,v1_1st_ep_con))
descT(v1_1st_ep)
##                -999 N    Y   <NA>     
## [1,] No. cases 320  1045 103 75   1543
## [2,] Percent   20.7 67.7 6.7 4.9  100

Times treated as day- or inpatient (continuous [times], v1_tms_daypat_outpat_trm)

Interviewers were instructed to use the lowest number if information is imprecise and to use “99” if too many treatments occurred. If the participant is currently treated as in- or daypatient, the number given contains this treatment.

v1_clin_tms_daypat_outpat_trm<-v1_clin$v1_psy_vorg_akt_pva6_anz_psy_behand
v1_con_tms_daypat_outpat_trm<-v1_con$v1_psyaktuel_anzahl

v1_tms_daypat_outpat_trm<-c(v1_clin_tms_daypat_outpat_trm,v1_con_tms_daypat_outpat_trm)
summary(v1_tms_daypat_outpat_trm)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0     2.0     4.0     6.4     7.0    99.0     392

Times treated as day- or inpatient (ordinal [1,2,3,4], v1_cat_daypat_outpat_trm)

We have decided to transform this variable into an ordinal variable using the following gradings: “smaller or equal five times”-1, “six to ten times”-2, “eleven to fourteen times”-3, “fifteen or more times”-4.

v1_clin_cat_daypat_outpat_trm<-rep(NA,dim(v1_clin)[1])
v1_con_cat_daypat_outpat_trm<-rep(NA,dim(v1_con)[1])

v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm<=5,"1",v1_clin_cat_daypat_outpat_trm)
v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm %in% c(6:10),"2",v1_clin_cat_daypat_outpat_trm)
v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm %in% c(11:13),"3",v1_clin_cat_daypat_outpat_trm)
v1_clin_cat_daypat_outpat_trm<-ifelse(v1_clin_tms_daypat_outpat_trm>=15,"4",v1_clin_cat_daypat_outpat_trm)

v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm<=5,"1",v1_con_cat_daypat_outpat_trm)
v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm %in% c(6:10),"2",v1_con_cat_daypat_outpat_trm)
v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm %in% c(11:13),"3",v1_con_cat_daypat_outpat_trm)
v1_con_cat_daypat_outpat_trm<-ifelse(v1_con_tms_daypat_outpat_trm>=15,"4",v1_con_cat_daypat_outpat_trm)

v1_cat_daypat_outpat_trm<-factor(c(v1_clin_cat_daypat_outpat_trm,v1_con_cat_daypat_outpat_trm),ordered=T)
descT(v1_cat_daypat_outpat_trm)
##                1    2    3   4   <NA>     
## [1,] No. cases 766  257  37  82  401  1543
## [2,] Percent   49.6 16.7 2.4 5.3 26   100

Create dataset

v1_psy_trtmt<-data.frame(v1_cur_psy_trm,v1_outpat_psy_trm,v1_age_1st_out_trm,v1_daypat_inpat_trm,v1_age_1st_inpat_trm,
                        v1_dur_illness,v1_1st_ep,v1_tms_daypat_outpat_trm,v1_cat_daypat_outpat_trm)

Visit 1: Medication

PsyCourse 3.1 now contains medication data. The code below creates the following variables for each person:

Number of antidepressants prescribed (continuous [number], v1_Antidepressants) Number of antipsychotics prescribed (continuous [number], v1_Antipsychotics) Number of mood stabilizers prescribed (continuous [number], v1_Mood_Stabilizers) Number of tranquilizers prescribed (continuous [number], v1_Tranquilizers) Number of other psychiatric medications (continuous [number], v1_Other_psychiatric)

Clinical participants

#get the following variables from v1_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v1_clin_medication_variables_1<-as.data.frame(v1_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v1_clin))])
dim(v1_clin_medication_variables_1) #[1] 1223   61
## [1] 1223   61
#recode the variables that are coded as characters/logicals in the "v1_clin_medication_variables_1" as factors
v1_clin_medication_variables_1$v1_medikabehand3_med_medi_199998_17<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_med_medi_199998_17)

v1_clin_medication_variables_1$v1_medikabehand3_med_kategorie_199998_17<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_med_kategorie_199998_17)

v1_clin_medication_variables_1$v1_medikabehand3_bedarf_medi_199584_10<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_bedarf_medi_199584_10)

v1_clin_medication_variables_1$v1_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v1_clin_medication_variables_1$v1_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v1_clin_medications_duplicated_1<-as.data.frame(t(apply(v1_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v1_clin_medications_duplicated_1) 
## [1] 1223   30
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_clin, not as character 
v1_clin_medication_variables_1[,!c(TRUE, FALSE)][v1_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v1_clin_medication_variables_1) 
## [1] 1223   61
#bind columns id and medication names, but not categories together 
v1_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v1_clin_medication_variables_1[,1], v1_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v1_clin_medication_name_1) 
## [1] 1223   31
#get the medication categories from the "_medication_variables_1" dataframe
v1_clin_medication_categories_1<-as.data.frame(v1_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v1_clin_medication_categories_1) 
## [1] 1223   31
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_clin, not as character 
#Important: v1_clin_medication_name_1=="NA" replaced with is.na(v1_clin_medication_name_1)
v1_clin_medication_categories_1[is.na(v1_clin_medication_name_1)] <- NA
#write.csv(v1_clin_medication_categories_1, file="v1_clin_medication_group_1.csv") 

#Make a count table of medications
v1_clin_med_table<-data.frame("mnppsd"=v1_clin$mnppsd)
v1_clin_med_table$v1_Antidepressants<-rowSums(v1_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v1_clin_med_table$v1_Antipsychotics<-rowSums(v1_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v1_clin_med_table$v1_Mood_stabilizers<-rowSums(v1_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v1_clin_med_table$v1_Tranquilizers<-rowSums(v1_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v1_clin_med_table$v1_Other_psychiatric<-rowSums(v1_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v1_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v1_con_medication_variables_1<-as.data.frame(v1_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v1_con))])
dim(v1_con_medication_variables_1) 
## [1] 320  29
#recode the variables that are coded as characters/logicals in the "v1_con_medication_variables_1" as factors
v1_con_medication_variables_1$v1_medikabehand3_depot_medi_201224_2<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_depot_medi_201224_2)

v1_con_medication_variables_1$v1_medikabehand3_depot_kategorie_201224_2<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_depot_kategorie_201224_2)

v1_con_medication_variables_1$v1_medikabehand3_bedarf_medi_201187_4<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_bedarf_medi_201187_4)

v1_con_medication_variables_1$v1_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v1_con_medication_variables_1$v1_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v1_con_medications_duplicated_1<-as.data.frame(t(apply(v1_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v1_con_medications_duplicated_1) #320  14
## [1] 320  14
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_con, not as character 
v1_con_medication_variables_1[,!c(TRUE, FALSE)][v1_con_medications_duplicated_1=="TRUE"] <- NA
dim(v1_con_medication_variables_1) #320   29
## [1] 320  29
#bind columns id and medication names, but not categories together 
v1_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v1_con_medication_variables_1[,1], v1_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v1_con_medication_name_1) #320  15
## [1] 320  15
#get the medication categories from the "_medication_variables_1" dataframe
v1_con_medication_categories_1<-as.data.frame(v1_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v1_con_medication_categories_1) #320  15
## [1] 320  15
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v1_con, not as character
#Important: v1_con_medication_name_1=="NA" replaced with is.na(v1_con_medication_name_1)
v1_con_medication_categories_1[is.na(v1_con_medication_name_1)] <- NA
#write.csv(v1_con_medication_categories_1, file="v1_con_medication_group_1.csv")

#Make a count table of medications
v1_con_med_table<-data.frame("mnppsd"=v1_con$mnppsd)
v1_con_med_table$v1_Antidepressants<-rowSums(v1_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v1_con_med_table$v1_Antipsychotics<-rowSums(v1_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v1_con_med_table$v1_Mood_stabilizers<-rowSums(v1_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v1_con_med_table$v1_Tranquilizers<-rowSums(v1_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v1_con_med_table$v1_Other_psychiatric<-rowSums(v1_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v1_clin and v1_con together by rows

v1_drugs<-rbind(v1_clin_med_table,v1_con_med_table)
dim(v1_drugs) #1543    6
## [1] 1543    6
#check if the id column of v1_drugs and v1_id match
table(droplevels(v1_drugs[,1])==v1_id)
## 
## TRUE 
## 1543

Adverse events under current medication (only in clinical participants) (dichotomous, v1_adv)

In control participants, this item is coded “-999”, as it was not assessed.

v1_clin_adv<-ifelse(v1_clin$v1_medikabehand_medi2_nebenwirk==1,"Y","N")
v1_con_adv<-rep("-999",dim(v1_con)[1])
v1_adv<-factor(c(v1_clin_adv,v1_con_adv))
descT(v1_adv)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  302  553  368  1543
## [2,] Percent   20.7 19.6 35.8 23.8 100

Psychiatric medication change during the past six months (dichotomous, v1_medchange)

In control participants, this item is coded “-999”, as it was not assessed.

v1_clin_medchange<-rep(NA,dim(v1_clin)[1])
v1_clin_medchange<-ifelse(v1_clin$v1_medikabehand_medi3_mediaenderung==1,"Y","N")
v1_con_medchange<-rep("-999",dim(v1_con)[1])

v1_medchange<-as.factor(c(v1_clin_medchange,v1_con_medchange))
descT(v1_medchange)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  254  592  377  1543
## [2,] Percent   20.7 16.5 38.4 24.4 100

Lithium

The following two items on medication with lithium are asked in all study vists, in order not to miss participants eligible for the ALDA scale, which is assessed at the last study visit.

“Did you ever take lithium?” (dichotomous, v1_lith)

v1_clin_lith<-rep(NA,dim(v1_clin)[1])
v1_clin_lith<-ifelse(v1_clin$v1_medikabehand_med_zusatz_lithium==1,"Y","N")
v1_con_lith<-rep("-999",dim(v1_con)[1])

v1_lith<-as.factor(c(v1_clin_lith,v1_con_lith))
v1_lith<-as.factor(v1_lith)

descT(v1_lith)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  529  246  448  1543
## [2,] Percent   20.7 34.3 15.9 29   100

“If yes, for how long?” (ordinal [1,2,3], v1_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3. Clinical paricipants not treated with lithium or control participants have “-999” here.

v1_clin_lith_prd<-rep(NA,dim(v1_clin)[1])
v1_con_lith_prd<-rep(-999,dim(v1_con)[1])

v1_clin_lith_prd<-ifelse(v1_clin_lith=="N", -999, ifelse(v1_clin$v1_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v1_clin$v1_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v1_clin$v1_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v1_lith_prd<-factor(c(v1_clin_lith_prd,v1_con_lith_prd))
descT(v1_lith_prd)
##                -999 1   2   3   <NA>     
## [1,] No. cases 849  103 26  117 448  1543
## [2,] Percent   55   6.7 1.7 7.6 29   100

Create dataset

v1_med<-data.frame(v1_drugs[,2:6],v1_adv,v1_medchange,v1_lith,v1_lith_prd)

Visit 1: Family history of psychiatric illness

Family member ever affected by psychiatric disorder (dichotomous, v1_fam_hist)

The option “Participant does not know” is coded as “NA”. The option “Participant does not want to disclose information” is coded as “-999”.

v1_clin_fam_hist<-rep(NA,dim(v1_clin)[1])
v1_con_fam_hist<-rep(NA,dim(v1_con)[1])

v1_clin_fam_hist<-ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==0,"N",
                    ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==1,"Y",
                    ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==2,"-999",
                    ifelse(v1_clin$v1_psy_famanam_pfa_angeh_pe==3,NA,v1_clin_fam_hist)))) 

v1_con_fam_hist<-ifelse(v1_con$v1_famanam_psyangeh==0,"N",
                    ifelse(v1_con$v1_famanam_psyangeh==1,"Y",
                    ifelse(v1_con$v1_famanam_psyangeh==2,"-999",
                    ifelse(v1_con$v1_famanam_psyangeh==3,NA,v1_con_fam_hist))))                     


v1_fam_hist<-as.factor(c(v1_clin_fam_hist,v1_con_fam_hist))
descT(v1_fam_hist)
##                -999 N    Y    <NA>     
## [1,] No. cases 10   436  1034 63   1543
## [2,] Percent   0.6  28.3 67   4.1  100

Also, in the original assessment of psychiatric history, each affected family member is listed, together with an indication whther the diagnosis is certain or not. This detailed information is available on request.

Visit 1: Physical measures and somatic diseases

The method of assessment of physical measures and somatic diseases changed during the course of the study. Selected diseases are now assessed specifically as dichotomous items. Before, it was only assessed whether a participant was ever affected by a group of diseases. If possble, these different phenotypes were harmonized. More detailed information on specific diseases (e.g. “Which type of cancer?”) may be available.

IMPORTANT:

Height (continuous [centimeters], v1_height)

v1_clin_height<-ifelse(is.na(v1_clin$v1_medwkeii_groesse),v1_clin$v1_mwke_mw1_groesse,v1_clin$v1_medwkeii_groesse)
v1_con_height<-ifelse(is.na(v1_con$v1_mwke_groesse),v1_con$v1_medwkeii_groesse,v1_con$v1_mwke_groesse)

v1_height<-c(v1_clin_height,v1_con_height)
summary(v1_height)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   114.0   167.0   174.0   173.7   180.0   203.0      15

Weight (continuous [kilograms], v1_weight)

v1_clin_weight<-ifelse(is.na(v1_clin$v1_medwkeii_gewicht),v1_clin$v1_mwke_mw2_gewicht,v1_clin$v1_medwkeii_gewicht)
v1_con_weight<-ifelse(is.na(v1_con$v1_medwkeii_gewicht),v1_con$v1_mwke_gewicht,v1_con$v1_medwkeii_gewicht)

v1_weight<-c(v1_clin_weight,v1_con_weight)
summary(v1_weight)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   45.00   69.00   80.00   82.95   94.00  190.00      23

Waist circumference (continouos [centimeters], v1_waist) This item was only recorded in a subset of individuals, because the question was introduced while the study was running.

v1_clin_waist<-v1_clin$v1_medwkeii_mw1_taillenumfang
v1_con_waist<-v1_con$v1_medwkeii_mw1_taillenumfang

v1_waist<-c(v1_clin_waist,v1_con_waist)
summary(v1_waist)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   61.00   76.00   86.00   89.01  100.00  149.00    1127

Body mass index (BMI, continuous [BMI], v1_bmi) We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v1_bmi<-round(v1_weight/(v1_height/100)^2,2)
summary(v1_bmi)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.18   23.05   26.22   27.41   30.41   75.41      23

Elevated cholesterol or triglyceride levels (dichotomous,v1_chol_trig) Phenotype was harmonized with the previous system.

v1_clin_chol_trig<-ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==1 & 
      grepl("[Tt]riglyceride|[Cc]holesterin|[Hh]ypercholesterin|[Hh]ypertriglycerid|Hyperlipoproteinämie|Hypercholestrinomie",
      v1_clin$v1_mwke_ke2_an090_stoffw_det),"Y","N"))

v1_clin_chol_trig<-ifelse(is.na(v1_clin_chol_trig) & v1_clin$v1_medwkeii_medwkeii_ecot==1,"Y",
                   ifelse(is.na(v1_clin_chol_trig) & v1_clin$v1_medwkeii_medwkeii_ecot==2,"N",v1_clin_chol_trig))
 
v1_con_chol_trig<-ifelse(v1_con$v1_mwke_stoffw==2,"N", 
                  ifelse(v1_con$v1_mwke_stoffw==1 & 
      grepl("[Tt]riglyceride|[Cc]holesterin|[Hh]ypercholesterin|[Hh]ypertriglycerid|Hyperlipoproteinämie|Hypercholestrinomie",
      v1_con$v1_mwke_stoffw_t),"Y","N"))

v1_con_chol_trig<-ifelse(is.na(v1_con_chol_trig) & v1_con$v1_medwkeii_medwkeii_ecot==1,"Y",
                   ifelse(is.na(v1_con_chol_trig) & v1_con$v1_medwkeii_medwkeii_ecot==2,"N",v1_con_chol_trig))

v1_chol_trig<-factor(c(v1_clin_chol_trig,v1_con_chol_trig))
descT(v1_chol_trig)
##                N    Y    <NA>     
## [1,] No. cases 1339 177  27   1543
## [2,] Percent   86.8 11.5 1.7  100

Hypertension (dichotomous, v1_hyperten) Phenotype was harmonized with the previous system.

v1_clin_hyperten<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 & 
      grepl("[Hh]yperton|[Hh]oher Blutdruck|[Bb]luthochdruck",
      v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))

v1_clin_hyperten<-ifelse(is.na(v1_clin_hyperten) & v1_clin$v1_medwkeii_medwkeii_hyperton==1,"Y",
                   ifelse(is.na(v1_clin_hyperten) & v1_clin$v1_medwkeii_medwkeii_hyperton==2,"N",v1_clin_hyperten))
 
v1_con_hyperten<-ifelse(v1_con$v1_mwke_hke==2,"N", 
                  ifelse(v1_con$v1_mwke_hke==1 & 
      grepl("[Hh]yperton|[Hh]oher Blutdruck|[Bb]luthochdruck",
      v1_con$v1_mwke_hke_t),"Y","N"))

v1_con_hyperten<-ifelse(is.na(v1_con_hyperten) & v1_con$v1_medwkeii_medwkeii_hyperton==1,"Y",
                   ifelse(is.na(v1_con_hyperten) & v1_con$v1_medwkeii_medwkeii_hyperton==2,"N",v1_con_hyperten))

v1_hyperten<-factor(c(v1_clin_hyperten,v1_con_hyperten))
descT(v1_hyperten)
##                N    Y    <NA>     
## [1,] No. cases 1255 264  24   1543
## [2,] Percent   81.3 17.1 1.6  100

Angina pectoris (dichotomous, v1_ang_pec) Phenotype was harmonized with the previous system.

v1_clin_ang_pec<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 & grepl("[Aa]ngina [Pp]ectoris",v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))

v1_clin_ang_pec<-ifelse(is.na(v1_clin_ang_pec) & v1_clin$v1_medwkeii_medwkeii_angpec==1,"Y",
                   ifelse(is.na(v1_clin_ang_pec) & v1_clin$v1_medwkeii_medwkeii_angpec==2,"N",v1_clin_ang_pec))
 
v1_con_ang_pec<-ifelse(v1_con$v1_mwke_hke==2,"N", 
                  ifelse(v1_con$v1_mwke_hke==1 & grepl("[Aa]ngina [Pp]ectoris",v1_con$v1_mwke_hke_t),"Y","N"))

v1_con_ang_pec<-ifelse(is.na(v1_con_ang_pec) & v1_con$v1_medwkeii_medwkeii_angpec==1,"Y",
                   ifelse(is.na(v1_con_ang_pec) & v1_con$v1_medwkeii_medwkeii_angpec==2,"N",v1_con_ang_pec))

v1_ang_pec<-factor(c(v1_clin_ang_pec,v1_con_ang_pec))
descT(v1_ang_pec)
##                N    Y  <NA>     
## [1,] No. cases 1503 16 24   1543
## [2,] Percent   97.4 1  1.6  100

Heart attack (dichotomous, v1_heart_att) Phenotype was harmonized with the previous system.

v1_clin_heart_att<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 & grepl("[Hh]erzinfarkt|[Mm]yokardinfarkt",v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))

v1_clin_heart_att<-ifelse(is.na(v1_clin_heart_att) & v1_clin$v1_medwkeii_medwkeii_herzinf==1,"Y",
                   ifelse(is.na(v1_clin_heart_att) & v1_clin$v1_medwkeii_medwkeii_herzinf==2,"N",v1_clin_heart_att))
 
v1_con_heart_att<-ifelse(v1_con$v1_mwke_hke==2,"N", 
                  ifelse(v1_con$v1_mwke_hke==1 & grepl("[Hh]erzinfarkt|[Mm]yokardinfarkt",v1_con$v1_mwke_hke_t),"Y","N"))

v1_con_heart_att<-ifelse(is.na(v1_con_heart_att) & v1_con$v1_medwkeii_medwkeii_herzinf==1,"Y",
                   ifelse(is.na(v1_con_heart_att) & v1_con$v1_medwkeii_medwkeii_herzinf==2,"N",v1_con_heart_att))

v1_heart_att<-factor(c(v1_clin_heart_att,v1_con_heart_att))
descT(v1_heart_att)
##                N    Y  <NA>     
## [1,] No. cases 1502 15 26   1543
## [2,] Percent   97.3 1  1.7  100

Stroke (dichotomous, v1_stroke) Phenotype was harmonized with the previous system.

v1_clin_stroke<-ifelse(v1_clin$v1_mwke_ke1_an010_hke==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke1_an010_hke==1 & grepl("[Ss]chlaganfall|[Hh]irnblutung|[Gg]ehirnblutung",v1_clin$v1_mwke_ke1_an010_hke_det),"Y","N"))

v1_clin_stroke<-ifelse(is.na(v1_clin_stroke) & v1_clin$v1_medwkeii_medwkeii_schlag==1,"Y",
                   ifelse(is.na(v1_clin_stroke) & v1_clin$v1_medwkeii_medwkeii_schlag==2,"N",v1_clin_stroke))
 
v1_con_stroke<-ifelse(v1_con$v1_mwke_hke==2,"N", 
                  ifelse(v1_con$v1_mwke_hke==1 & grepl("[Ss]chlaganfall|[Hh]irnblutung|[Gg]ehirnblutung",v1_con$v1_mwke_hke_t),"Y","N"))

v1_con_stroke<-ifelse(is.na(v1_con_stroke) & v1_con$v1_medwkeii_medwkeii_schlag==1,"Y",
                   ifelse(is.na(v1_con_stroke) & v1_con$v1_medwkeii_medwkeii_schlag==2,"N",v1_con_stroke))

v1_stroke<-factor(c(v1_clin_stroke,v1_con_stroke))
descT(v1_stroke)
##                N    Y   <NA>     
## [1,] No. cases 1507 11  25   1543
## [2,] Percent   97.7 0.7 1.6  100

Diabetes (dichotomous, v1_diabetes) Phenotype was harmonized with the previous system.

v1_clin_diabetes<-ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke2_an090_stoffw==1 & 
      grepl("[Dd]iabetes|[Zz]ucker",v1_clin$v1_mwke_ke2_an090_stoffw_det),"Y","N"))

v1_clin_diabetes<-ifelse(is.na(v1_clin_diabetes) & v1_clin$v1_medwkeii_medwkeii_zuck==1,"Y",
                   ifelse(is.na(v1_clin_diabetes) & v1_clin$v1_medwkeii_medwkeii_zuck==2,"N",v1_clin_diabetes))
 
v1_con_diabetes<-ifelse(v1_con$v1_mwke_stoffw==2,"N", 
                  ifelse(v1_con$v1_mwke_stoffw==1 & grepl("[Dd]iabetes|[Zz]ucker",v1_con$v1_mwke_stoffw_t),"Y","N"))

v1_con_diabetes<-ifelse(is.na(v1_con_diabetes) & v1_con$v1_medwkeii_medwkeii_zuck==1,"Y",
                   ifelse(is.na(v1_con_diabetes) & v1_con$v1_medwkeii_medwkeii_zuck==2,"N",v1_con_diabetes))

v1_diabetes<-factor(c(v1_clin_diabetes,v1_con_diabetes))
descT(v1_diabetes)
##                N    Y   <NA>     
## [1,] No. cases 1408 108 27   1543
## [2,] Percent   91.3 7   1.7  100

Hyperthyroidism (dichotomous, v1_hyperthy) Phenotype was harmonized with the previous system.

v1_clin_hyperthy<-ifelse(v1_clin$v1_mwke_ke3_an11_schildd==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke3_an11_schildd==1 & 
    grepl("[Üü]berfunktion|[Uu]eberfunktion|[Ss]childdrüsenüberfunktion|[Hh]yperthyreose",v1_clin$v1_mwke_ke3_an11_schildd_det),"Y","N"))

v1_clin_hyperthy<-ifelse(is.na(v1_clin_hyperthy) & v1_clin$v1_medwkeii_medwkeii_hypothy==1,"Y",
                   ifelse(is.na(v1_clin_hyperthy) & v1_clin$v1_medwkeii_medwkeii_hypothy==2,"N",v1_clin_hyperthy))
 
v1_con_hyperthy<-ifelse(v1_con$v1_mwke_schildd==2,"N", 
                  ifelse(v1_con$v1_mwke_schildd==1 & 
   grepl("[Üü]berfunktion|[Uu]eberfunktion|[Ss]childdrüsenüberfunktion|[Hh]yperthyreose",v1_con$v1_mwke_schildd_t),"Y","N"))

v1_con_hyperthy<-ifelse(is.na(v1_con_hyperthy) & v1_con$v1_medwkeii_medwkeii_hypothy==1,"Y",
                   ifelse(is.na(v1_con_hyperthy) & v1_con$v1_medwkeii_medwkeii_hypothy==2,"N",v1_con_hyperthy))

v1_hyperthy<-factor(c(v1_clin_hyperthy,v1_con_hyperthy))
descT(v1_hyperthy)
##                N    Y   <NA>     
## [1,] No. cases 1402 111 30   1543
## [2,] Percent   90.9 7.2 1.9  100

Hypothyroidism (dichotomous, v1_hypothy) Phenotype was harmonized with the previous system.

v1_clin_hypothy<-ifelse(v1_clin$v1_mwke_ke3_an11_schildd==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke3_an11_schildd==1 & 
grepl("[Uu]nterfunktion|[Ss]childdrüsenunterfunktion|Hashimoto|[Hh]ypothyreose|[Tt]hyreoiditis|[Ee]ntfernt",v1_clin$v1_mwke_ke3_an11_schildd_det),"Y","N"))

v1_clin_hypothy<-ifelse(is.na(v1_clin_hypothy) & v1_clin$v1_medwkeii_medwkeii_hypothy==1,"Y",
                   ifelse(is.na(v1_clin_hypothy) & v1_clin$v1_medwkeii_medwkeii_hypothy==2,"N",v1_clin_hypothy))
 
v1_con_hypothy<-ifelse(v1_con$v1_mwke_schildd==2,"N", 
                  ifelse(v1_con$v1_mwke_schildd==1 & 
   grepl("[Uu]nterfunktion|[Ss]childdrüsenunterfunktion|Hashimoto|[Hh]ypothyreose|[Tt]hyreoiditis|[Ee]ntfernt",v1_con$v1_mwke_schildd_t),"Y","N"))

v1_con_hypothy<-ifelse(is.na(v1_con_hypothy) & v1_con$v1_medwkeii_medwkeii_hypothy==1,"Y",
                   ifelse(is.na(v1_con_hypothy) & v1_con$v1_medwkeii_medwkeii_hypothy==2,"N",v1_con_hypothy))

v1_hypothy<-factor(c(v1_clin_hypothy,v1_con_hypothy))
descT(v1_hypothy)
##                N    Y    <NA>     
## [1,] No. cases 1260 253  30   1543
## [2,] Percent   81.7 16.4 1.9  100

Osteoporosis (dichotomous, v1_osteopor) Phenotype was harmonized with the previous system.

v1_clin_osteopor<-ifelse(v1_clin$v1_mwke_ke4_an130_rme==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke4_an130_rme==1 & 
grepl("[Oo]steoporose|[Kk]nochenschwund",v1_clin$v1_mwke_ke4_an130_rme_det),"Y","N"))

v1_clin_osteopor<-ifelse(is.na(v1_clin_osteopor) & v1_clin$v1_medwkeii_medwkeii_osterop==1,"Y",
                   ifelse(is.na(v1_clin_osteopor) & v1_clin$v1_medwkeii_medwkeii_osterop==2,"N",v1_clin_osteopor))
 
v1_con_osteopor<-ifelse(v1_con$v1_mwke_rme==2,"N", 
                  ifelse(v1_con$v1_mwke_rme==1 & 
   grepl("[Oo]steoporose|[Kk]nochenschwund",v1_con$v1_mwke_rme_t),"Y","N"))

v1_con_osteopor<-ifelse(is.na(v1_con_osteopor) & v1_con$v1_medwkeii_medwkeii_osterop==1,"Y",
                   ifelse(is.na(v1_con_osteopor) & v1_con$v1_medwkeii_medwkeii_osterop==2,"N",v1_con_osteopor))

v1_osteopor<-factor(c(v1_clin_osteopor,v1_con_osteopor))
descT(v1_osteopor)
##                N    Y   <NA>     
## [1,] No. cases 1489 26  28   1543
## [2,] Percent   96.5 1.7 1.8  100

Asthma (dichotomous, v1_asthma) Phenotype was harmonized with the previous system.

v1_clin_asthma<-ifelse(v1_clin$v1_mwke_ke5_an180_lunge==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke5_an180_lunge==1 & 
grepl("[Aa]sthma",v1_clin$v1_mwke_ke5_an180_lunge_det),"Y","N"))

v1_clin_asthma<-ifelse(is.na(v1_clin_asthma) & v1_clin$v1_medwkeii_medwkeii_asthma==1,"Y",
                   ifelse(is.na(v1_clin_asthma) & v1_clin$v1_medwkeii_medwkeii_asthma==2,"N",v1_clin_asthma))
 
v1_con_asthma<-ifelse(v1_con$v1_mwke_lunge==2,"N", 
                  ifelse(v1_con$v1_mwke_lunge==1 & 
   grepl("[Aa]sthma",v1_con$v1_mwke_lunge_t),"Y","N"))

v1_con_asthma<-ifelse(is.na(v1_con_asthma) & v1_con$v1_medwkeii_medwkeii_asthma==1,"Y",
                   ifelse(is.na(v1_con_asthma) & v1_con$v1_medwkeii_medwkeii_asthma==2,"N",v1_con_asthma))

v1_asthma<-factor(c(v1_clin_asthma,v1_con_asthma))
descT(v1_asthma)
##                N    Y   <NA>     
## [1,] No. cases 1399 120 24   1543
## [2,] Percent   90.7 7.8 1.6  100

COPD/chronic Bronchitis (dichotomous, v1_copd) Phenotype was harmonized with the previous system.

v1_clin_copd<-ifelse(v1_clin$v1_mwke_ke5_an180_lunge==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke5_an180_lunge==1 & 
grepl("COPD|[Cc]hronische Bronchitis|[Cc]hron. Bronchitis|Chronisch obstruktive Atemwegserkrankung",v1_clin$v1_mwke_ke5_an180_lunge_det),"Y","N"))

v1_clin_copd<-ifelse(is.na(v1_clin_copd) & v1_clin$v1_medwkeii_medwkeii_copd==1,"Y",
                   ifelse(is.na(v1_clin_copd) & v1_clin$v1_medwkeii_medwkeii_copd==2,"N",v1_clin_copd))
 
v1_con_copd<-ifelse(v1_con$v1_mwke_lunge==2,"N", 
                  ifelse(v1_con$v1_mwke_lunge==1 & 
   grepl("COPD|[Cc]hronische Bronchitis|[Cc]hron. Bronchitis|Chronisch obstruktive Atemwegserkrankung",v1_con$v1_mwke_lunge_t),"Y","N"))

v1_con_copd<-ifelse(is.na(v1_con_copd) & v1_con$v1_medwkeii_medwkeii_copd==1,"Y",
                   ifelse(is.na(v1_con_copd) & v1_con$v1_medwkeii_medwkeii_copd==2,"N",v1_con_copd))

v1_copd<-factor(c(v1_clin_copd,v1_con_copd))
descT(v1_copd)
##                N    Y   <NA>     
## [1,] No. cases 1451 68  24   1543
## [2,] Percent   94   4.4 1.6  100

Allergies (dichotomus, v1_allerg) Phenotype was harmonized with the previous system.

v1_clin_allerg<-ifelse(v1_clin$v1_mwke_ke6_an200_allerg==1,"Y","N")
v1_clin_allerg<-ifelse(is.na(v1_clin_allerg) & v1_clin$v1_medwkeii_medwkeii_allerg==1,"Y",
                       ifelse(is.na(v1_clin_allerg) & v1_clin$v1_medwkeii_medwkeii_allerg==2,"N",v1_clin_allerg))

v1_con_allerg<-ifelse(v1_con$v1_mwke_allerg==1,"Y","N")
v1_con_allerg<-ifelse(is.na(v1_con_allerg) & v1_con$v1_medwkeii_medwkeii_allerg==1,"Y",
                       ifelse(is.na(v1_con_allerg) & v1_con$v1_medwkeii_medwkeii_allerg==2,"N",v1_con_allerg))

v1_allerg<-factor(c(v1_clin_allerg,v1_con_allerg))
descT(v1_allerg)
##                N    Y   <NA>     
## [1,] No. cases 888  633 22   1543
## [2,] Percent   57.6 41  1.4  100

Neurodermatitis (dichotomous, v1_neuroder) Phenotype was harmonized with the previous system.

v1_clin_neuroder<-ifelse(v1_clin$v1_mwke_ke9_an330_haut==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke9_an330_haut==1 & 
grepl("[Nn]eurodermitis",v1_clin$v1_mwke_ke9_an330_haut_det),"Y","N"))

v1_clin_neuroder<-ifelse(is.na(v1_clin_neuroder) & v1_clin$v1_medwkeii_medwkeii_neurod==1,"Y",
                   ifelse(is.na(v1_clin_neuroder) & v1_clin$v1_medwkeii_medwkeii_neurod==2,"N",v1_clin_neuroder))
 
v1_con_neuroder<-ifelse(v1_con$v1_mwke_haut==2,"N", 
                  ifelse(v1_con$v1_mwke_haut==1 & 
   grepl("[Nn]eurodermitis",v1_con$v1_mwke_haut_t),"Y","N"))

v1_con_neuroder<-ifelse(is.na(v1_con_neuroder) & v1_con$v1_medwkeii_medwkeii_neurod==1,"Y",
                   ifelse(is.na(v1_con_neuroder) & v1_con$v1_medwkeii_medwkeii_neurod==2,"N",v1_con_neuroder))

v1_neuroder<-factor(c(v1_clin_neuroder,v1_con_neuroder))
descT(v1_neuroder)
##                N    Y   <NA>     
## [1,] No. cases 1420 96  27   1543
## [2,] Percent   92   6.2 1.7  100

Psoriasis (dichotomous, v1_psoriasis) Phenotype was harmonized with the previous system.

v1_clin_psoriasis<-ifelse(v1_clin$v1_mwke_ke9_an330_haut==2,"N", 
                   ifelse(v1_clin$v1_mwke_ke9_an330_haut==1 & 
grepl("[Pp]soriasis|[Ss][Cc]huppenflechte",v1_clin$v1_mwke_ke9_an330_haut_det),"Y","N"))

v1_clin_psoriasis<-ifelse(is.na(v1_clin_psoriasis) & v1_clin$v1_medwkeii_medwkeii_neurod==1,"Y",
                   ifelse(is.na(v1_clin_psoriasis) & v1_clin$v1_medwkeii_medwkeii_neurod==2,"N",v1_clin_psoriasis))
 
v1_con_psoriasis<-ifelse(v1_con$v1_mwke_haut==2,"N", 
                  ifelse(v1_con$v1_mwke_haut==1 & 
   grepl("[Ss][Cc]huppenflechte|[Ss][Cc]huppenflechte",v1_con$v1_mwke_haut_t),"Y","N"))

v1_con_psoriasis<-ifelse(is.na(v1_con_psoriasis) & v1_con$v1_medwkeii_medwkeii_neurod==1,"Y",
                   ifelse(is.na(v1_con_psoriasis) & v1_con$v1_medwkeii_medwkeii_neurod==2,"N",v1_con_psoriasis))

v1_psoriasis<-factor(c(v1_clin_psoriasis,v1_con_psoriasis))
descT(v1_psoriasis)
##                N    Y   <NA>     
## [1,] No. cases 1444 72  27   1543
## [2,] Percent   93.6 4.7 1.7  100

Autoimmune diseases (dichotomous, v1_autoimm)

v1_clin_autoimm_yn<-ifelse(v1_clin$v1_medwkeii_medwkeii_autoim==1,"Y","N")
v1_con_autoimm_yn<-ifelse(v1_con$v1_medwkeii_medwkeii_autoim==1,"Y","N")
 
v1_autoimm<-factor(c(v1_clin_autoimm_yn,v1_con_autoimm_yn))
descT(v1_autoimm)
##                N    Y   <NA>     
## [1,] No. cases 466  25  1052 1543
## [2,] Percent   30.2 1.6 68.2 100

Cancer (dichotomous, v1_cancer) Similar question asked in previous system.Information on the type of cancer is available on request.

v1_clin_cancer<-ifelse(v1_clin$v1_mwke_ke14_an56_krebs==1,"Y","N")
v1_clin_cancer<-ifelse(is.na(v1_clin_cancer) & v1_clin$v1_medwkeii_medwkeii_kreberk==1,"Y",
                   ifelse(is.na(v1_clin_cancer) & v1_clin$v1_medwkeii_medwkeii_kreberk==2,"N",v1_clin_cancer))

v1_con_cancer<-ifelse(v1_con$v1_mwke_krebs==1,"Y","N")
v1_con_cancer<-ifelse(is.na(v1_con_cancer) & v1_con$v1_medwkeii_medwkeii_kreberk==1,"Y",
                   ifelse(is.na(v1_con_cancer) & v1_con$v1_medwkeii_medwkeii_kreberk==2,"N",v1_con_cancer))

v1_cancer<-factor(c(v1_clin_cancer,v1_con_cancer))
descT(v1_cancer)
##                N    Y   <NA>     
## [1,] No. cases 1443 75  25   1543
## [2,] Percent   93.5 4.9 1.6  100

Stomach ulcer (dichotomous, v1_stom_ulc) Phenotype was harmonized with the previous system.

v1_clin_stom_ulc<-ifelse(v1_clin$v1_medwkeii_medwkeii_maggesch==1,"Y","N")
v1_clin_stom_ulc<-ifelse(is.na(v1_clin_stom_ulc) & v1_clin$v1_mwke_ke7_an270_mde==2,"N",
                  ifelse(is.na(v1_clin_stom_ulc) & v1_clin$v1_mwke_ke7_an270_mde==1 & 
                      grepl("[Mm]agengeschwür",v1_clin$v1_mwke_ke7_an270_mde_det),"Y",v1_clin_stom_ulc))

v1_con_stom_ulc<-ifelse(v1_con$v1_medwkeii_medwkeii_maggesch==1,"Y","N")
v1_con_stom_ulc<-ifelse(is.na(v1_con_stom_ulc) & v1_con$v1_mwke_mde==2,"N",
                  ifelse(is.na(v1_con_stom_ulc) & v1_con$v1_mwke_mde==1 & 
                      grepl("[Mm]agengeschwür",v1_con$v1_mwke_mde_t),"Y",v1_con_stom_ulc))
  
v1_stom_ulc<-factor(c(v1_clin_stom_ulc,v1_con_stom_ulc))
descT(v1_stom_ulc)
##                N    Y   <NA>     
## [1,] No. cases 1315 29  199  1543
## [2,] Percent   85.2 1.9 12.9 100

Kidney failure (dichotomous, v1_kid_fail)

v1_clin_kid_fail<-ifelse(v1_clin$v1_medwkeii_medwkeii_nierver==1,"Y","N")
v1_clin_kid_fail<-ifelse(is.na(v1_clin_kid_fail) & v1_clin$v1_mwke_ke10_an360_nibl==2,"N",
                  ifelse(is.na(v1_clin_kid_fail) & v1_clin$v1_mwke_ke10_an360_nibl==1 & 
                      grepl("[Nn]ierenversagen",v1_clin$v1_mwke_ke10_an360_nibl_det),"Y",v1_clin_kid_fail))

v1_con_kid_fail<-ifelse(v1_con$v1_medwkeii_medwkeii_nierver==1,"Y","N")
v1_con_kid_fail<-ifelse(is.na(v1_con_kid_fail) & v1_con$v1_mwke_nibl==2,"N",
                  ifelse(is.na(v1_con_kid_fail) & v1_con$v1_mwke_nibl==1 & 
                      grepl("[Nn]ierenversagen",v1_con$v1_mwke_nibl_t),"Y",v1_con_kid_fail))
  
v1_kid_fail<-factor(c(v1_clin_kid_fail,v1_con_kid_fail))
descT(v1_kid_fail)
##                N    Y   <NA>     
## [1,] No. cases 1390 4   149  1543
## [2,] Percent   90.1 0.3 9.7  100

Kidney-/Gallstone (dichotomous, v1_stone)

v1_clin_stone<-ifelse(v1_clin$v1_medwkeii_medwkeii_nierga==1,"Y","N")
v1_clin_stone<-ifelse(is.na(v1_clin_stone) & v1_clin$v1_mwke_ke10_an360_nibl==2,"N",
                  ifelse(is.na(v1_clin_stone) & (v1_clin$v1_mwke_ke10_an360_nibl==1 | v1_clin$v1_mwke_ke8_an300_galleb) & 
                      (grepl("[Nn]ierenstein|[Nn]ierensteine",v1_clin$v1_mwke_ke10_an360_nibl_det) |
                       grepl("[Gg]allenstein|[Gg]allensteine",v1_clin$v1_mwke_ke8_an300_galleb_det)),"Y",v1_clin_stone))

v1_con_stone<-ifelse(v1_con$v1_medwkeii_medwkeii_nierga==1,"Y","N")
v1_con_stone<-ifelse(is.na(v1_con_stone) & v1_con$v1_mwke_nibl==2,"N",
                  ifelse(is.na(v1_con_stone) & (v1_con$v1_mwke_nibl==1 | v1_con$v1_mwke_galle==1) & 
                     (grepl("[Nn]ierenstein|[Nn]ierensteine",v1_con$v1_mwke_nibl_t) |
                       grepl("[Gg]allenstein|[Gg]allensteine",v1_con$v1_mwke_galle_t)),"Y",v1_con_stone))      
                          
v1_stone<-factor(c(v1_clin_stone,v1_con_stone))
descT(v1_stone)
##                N    Y   <NA>     
## [1,] No. cases 1370 43  130  1543
## [2,] Percent   88.8 2.8 8.4  100

Epilepsy (dichotomous, v1_epilepsy)

v1_clin_epilepsy<-ifelse(v1_clin$v1_medwkeii_medwkeii_epile==1,"Y","N")
v1_clin_epilepsy<-ifelse(is.na(v1_clin_epilepsy) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
                  ifelse(is.na(v1_clin_epilepsy) & v1_clin$v1_mwke_ke11_an400_npsy==1 & 
                      grepl("[Ee]pilepsie",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_epilepsy))

v1_con_epilepsy<-ifelse(v1_con$v1_medwkeii_medwkeii_epile==1,"Y","N")
v1_con_epilepsy<-ifelse(is.na(v1_con_epilepsy) & v1_con$v1_mwke_npsy==2,"N",
                  ifelse(is.na(v1_con_epilepsy) & v1_con$v1_mwke_npsy==1 & 
                     grepl("[Ee]pilepsie",v1_con$v1_mwke_npsy_t),"Y",v1_con_epilepsy))      
                          
v1_epilepsy<-factor(c(v1_clin_epilepsy,v1_con_epilepsy))
descT(v1_epilepsy)
##                N    Y   <NA>     
## [1,] No. cases 1266 19  258  1543
## [2,] Percent   82   1.2 16.7 100

Migraine (dichotomous, v1_migraine)

v1_clin_migraine<-ifelse(v1_clin$v1_medwkeii_medwkeii_mig==1,"Y","N")
v1_clin_migraine<-ifelse(is.na(v1_clin_migraine) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
                  ifelse(is.na(v1_clin_migraine) & v1_clin$v1_mwke_ke11_an400_npsy==1 & 
                      grepl("[Mm]igräne | Mirgäne | mIGRÄNE",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_migraine))

v1_con_migraine<-ifelse(v1_con$v1_medwkeii_medwkeii_mig==1,"Y","N")
v1_con_migraine<-ifelse(is.na(v1_con_migraine) & v1_con$v1_mwke_npsy==2,"N",
                  ifelse(is.na(v1_con_migraine) & v1_con$v1_mwke_npsy==1 & 
                     grepl("[Mm]igräne | Mirgäne | mIGRÄNE",v1_con$v1_mwke_npsy_t),"Y",v1_con_migraine))      
                          
v1_migraine<-factor(c(v1_clin_migraine,v1_con_migraine))
descT(v1_migraine)
##                N    Y  <NA>     
## [1,] No. cases 1216 62 265  1543
## [2,] Percent   78.8 4  17.2 100

Parkinson syndrome (dichotomous, v1_parkinson)

v1_clin_parkinson<-ifelse(v1_clin$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_clin_parkinson<-ifelse(is.na(v1_clin_parkinson) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
                  ifelse(is.na(v1_clin_parkinson) & v1_clin$v1_mwke_ke11_an400_npsy==1 & 
                      grepl("[Pp]arkinson",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_parkinson))

                  
v1_con_parkinson<-ifelse(v1_con$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_con_parkinson<-ifelse(is.na(v1_con_parkinson) & v1_con$v1_mwke_npsy==2,"N",
                  ifelse(is.na(v1_con_parkinson) & v1_con$v1_mwke_npsy==1 & 
                     grepl("[Pp]arkinson",v1_con$v1_mwke_npsy_t),"Y",v1_con_parkinson))      
                          
v1_parkinson<-factor(c(v1_clin_parkinson,v1_con_parkinson))
descT(v1_parkinson)
##                N    Y   <NA>     
## [1,] No. cases 1269 8   266  1543
## [2,] Percent   82.2 0.5 17.2 100

Liver cirrhosis or inflammation (dichotomous, v1_liv_cir_inf)

v1_clin_liv_cir_inf<-ifelse(v1_clin$v1_medwkeii_medwkeii_leberz==1,"Y","N")
v1_clin_liv_cir_inf<-ifelse(is.na(v1_clin_liv_cir_inf) & v1_clin$v1_mwke_ke8_an300_galleb==2,"N",
                  ifelse(is.na(v1_clin_liv_cir_inf) & v1_clin$v1_mwke_ke8_an300_galleb==1 & 
                      grepl("[Ll]eberzerose | [Ll]eberzirrhose |   
                            [Ll]eberentzündung",v1_clin$v1_mwke_ke8_an300_galleb_det),"Y",v1_clin_liv_cir_inf))

v1_con_liv_cir_inf<-ifelse(v1_con$v1_medwkeii_medwkeii_leberz==1,"Y","N")
v1_con_liv_cir_inf<-ifelse(is.na(v1_con_liv_cir_inf) & v1_con$v1_mwke_galle==2,"N",
                  ifelse(is.na(v1_con_liv_cir_inf) & v1_con$v1_mwke_galle==1 & 
                     grepl("[Ll]eberzerose | [Ll]eberzirrhose |   
                            [Ll]eberentzündung",v1_con$v1_mwke_galle_t),"Y",v1_con_liv_cir_inf))      
                          
v1_liv_cir_inf<-factor(c(v1_clin_liv_cir_inf,v1_con_liv_cir_inf))
descT(v1_liv_cir_inf)
##                N    Y   <NA>     
## [1,] No. cases 1397 4   142  1543
## [2,] Percent   90.5 0.3 9.2  100

Traumatic brain injury (dichotomous, v1_tbi)

v1_clin_tbi<-ifelse(v1_clin$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_clin_tbi<-ifelse(is.na(v1_clin_tbi) & v1_clin$v1_mwke_ke11_an400_npsy==2,"N",
                  ifelse(is.na(v1_clin_tbi) & v1_clin$v1_mwke_ke11_an400_npsy==1 & 
                      grepl("[Ss]chädel-Hirn-Trauma",v1_clin$v1_mwke_ke11_an400_npsy_det),"Y",v1_clin_tbi))

v1_con_tbi<-ifelse(v1_con$v1_medwkeii_medwkeii_parks==1,"Y","N")
v1_con_tbi<-ifelse(is.na(v1_con_tbi) & v1_con$v1_mwke_npsy==2,"N",
                  ifelse(is.na(v1_con_tbi) & v1_con$v1_mwke_npsy==1 & 
                     grepl("[Ss]chädel-Hirn-Trauma",v1_con$v1_mwke_npsy_t),"Y",v1_con_tbi))      
                          
v1_tbi<-factor(c(v1_clin_tbi,v1_con_tbi))
descT(v1_tbi)
##                N    Y   <NA>     
## [1,] No. cases 1269 4   270  1543
## [2,] Percent   82.2 0.3 17.5 100

Disorders of the eyes or ears (dichotomous, v1_eyear) This item was also asked in the previous method of assessment, with three answering alternatives: “Yes,”No“, and”Don’t know“. The option”Don’t know" was coded as NA, to harmonize it with the new methode of assessment. Near- or farsightedness, according to our guidelines, is not a disease and should not be coded as such. There is more information on the type of disease, available on request.

v1_clin_eyear<-ifelse(v1_clin$v1_mwke_ke12_an440_auohr==1,"Y",ifelse(v1_clin$v1_mwke_ke12_an440_auohr==2,"N",NA))
v1_clin_eyear<-ifelse(is.na(v1_clin_eyear) & v1_clin$v1_medwkeii_medwkeii_auo==1,"Y",
                      ifelse(is.na(v1_clin_eyear) & v1_clin$v1_medwkeii_medwkeii_auo==2,"N",v1_clin_eyear))

v1_con_eyear<-ifelse(v1_con$v1_mwke_auohr==1,"Y",ifelse(v1_con$v1_mwke_auohr==2,"N",NA))
v1_con_eyear<-ifelse(is.na(v1_con_eyear) & v1_con$v1_medwkeii_medwkeii_auo==1,"Y",
                      ifelse(is.na(v1_con_eyear) & v1_con$v1_medwkeii_medwkeii_auo==2,"N",v1_con_eyear))
                        
v1_eyear<-factor(c(v1_clin_eyear,v1_con_eyear))
descT(v1_eyear)
##                N    Y    <NA>     
## [1,] No. cases 1304 208  31   1543
## [2,] Percent   84.5 13.5 2    100

Infectious diseases (dichotomous, v1_inf) As the assessment of this item was modified in the course of the study (at some point interviewers were instructed not to code benign childhood diseases such as chickenpox), this item is NOT included in this version of the dataset anymore. The variable name remains in the codebook, but not in the dataset.

Create dataset

v1_som_dsrdr<-data.frame(v1_height,
                         v1_weight,
                         v1_waist,
                         v1_bmi,
                         v1_chol_trig,
                         v1_hyperten,
                         v1_ang_pec,
                         v1_heart_att,
                         v1_stroke,
                         v1_diabetes,
                         v1_hyperthy,
                         v1_hypothy,
                         v1_osteopor,
                         v1_asthma,
                         v1_copd,
                         v1_allerg,
                         v1_neuroder,
                         v1_psoriasis,
                         v1_autoimm,
                         v1_cancer,
                         v1_stom_ulc,
                         v1_kid_fail,
                         v1_stone,
                         v1_epilepsy,
                         v1_migraine,
                         v1_parkinson,
                         v1_liv_cir_inf,
                         v1_tbi,
                         v1_eyear)

Visit 1: Substance abuse

Tobacco

There is some more information available (whether smokers have stopped smoking more than one year, if yes how many years, how many pipes, cigars and cigarillos), not part of the present dataset.

Did you ever smoke tobacco products? (categorical [Y,N,F], v1_ever_smkd)

This is a categorical item with three optional answers: “never”-N, “yes”-Y, and “former” (having smoked in the past but not now)-F. It is required to have stopped smoking for at least three months to qualify as former smoker.

v1_clin_ever_smkd<-ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==1,"N",
                     ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==2,"Y",
                            ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==3,"F",NA))) 

v1_con_ever_smkd<-ifelse(v1_con$v1_tabalk_tabak1==1,"N",
                     ifelse(v1_con$v1_con$v1_tabalk_tabak1==2,"Y",
                            ifelse(v1_con$v1_tabalk_tabak1==3,"F",NA))) 

v1_ever_smkd<-factor(c(v1_clin_ever_smkd,v1_con_ever_smkd))
descT(v1_ever_smkd)
##                F    N    Y    <NA>     
## [1,] No. cases 208  527  657  151  1543
## [2,] Percent   13.5 34.2 42.6 9.8  100

At what age did you start smoking? (continuous [age], v1_age_smk)

The original item has three optional answers (age unknown, age or alternatively year in which smoking started). Here, we give age at which smoking started. In cases in which the year in which smoking started was given, we calculated this as follows: year in which smoking stated minus (year of interview minus age at interview). For people who never smoked, this question is coded as “-999”.

v1_clin_age_smk<-ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==1, -999, ifelse(is.na(v1_clin$v1_tabalk_ta2_alter_jahre)==T,
          (v1_clin$v1_tabalk_ta2_anf_jahr-(as.numeric(format(v1_interv_date,'%Y'))-v1_clin$v1_ageBL)),v1_clin$v1_tabalk_ta2_alter_jahre))

v1_con_age_smk<-ifelse(v1_con$v1_tabalk_tabak1, -999, ifelse(is.na(v1_con$v1_tabalk_tabak2_alter)==T,
            (v1_con$v1_tabalk_tabak2_jahr-(as.numeric(format(interv_date,'%Y'))-v1_ageBL)),v1_con$v1_tabalk_ta2_alter_jahre))

v1_age_smk<-factor(c(v1_clin_age_smk,v1_con_age_smk))
summary(v1_age_smk)
## -999    5    6    7    8    9   10   11   12   13   14   15   16   17   18 
##  662    2    2    2    1    9    4   10   35   51  109  118  167   58   83 
##   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33 
##   27   41   23   10   11    6   11    2    3    5    5   11    2    2    1 
##   35   36   38   40   44   45   48   53   57   61 NA's 
##    4    1    1    1    1    1    1    1    1    1   57

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v1_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please note that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur. For people presently do not smoke this question is coded as “-999”.

v1_clin_no_cig<-ifelse(v1_clin$v1_tabalk_ta1_jemals_ger==1, -999, 
                  ifelse(v1_clin$v1_tabalk_ta3_zig_pro_zeit==1, v1_clin$v1_tabalk_ta3_anz_zig*365,
                  ifelse(v1_clin$v1_tabalk_ta3_zig_pro_zeit==2, v1_clin$v1_tabalk_ta3_anz_zig*52,
                  ifelse(v1_clin$v1_tabalk_ta3_zig_pro_zeit==3, v1_clin$v1_tabalk_ta3_anz_zig*12,NA))))       

v1_con_no_cig<-ifelse(v1_con$v1_tabalk_tabak1==1, -999, 
                  ifelse(v1_con$v1_tabalk_tabak3_zeit==1, v1_con$v1_tabalk_ta3_anz_zig*365,
                  ifelse(v1_con$v1_tabalk_tabak3_zeit==2, v1_con$v1_tabalk_ta3_anz_zig*52,
                  ifelse(v1_con$v1_tabalk_tabak3_zeit==3, v1_con$v1_tabalk_ta3_anz_zig*12,NA))))                         

v1_no_cig<-c(v1_clin_no_cig,v1_con_no_cig)
summary(v1_no_cig)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    -999    -999    1825    3384    7300   23725     380

Alcohol

“How often did you consume alcoholic beverages during the past twelve months?”" (ordinal [1,2,3,4,5,6,7], v1_alc_pst12_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v1_alc_pst12_mths<-factor(c(v1_clin$v1_tabalk_ta9_alkkonsum,v1_con$v1_tabalk_alkohol9), ordered=T)
descT(v1_alc_pst12_mths)
##                1    2    3    4    5    6   7   <NA>     
## [1,] No. cases 275  332  182  352  189  67  72  74   1543
## [2,] Percent   17.8 21.5 11.8 22.8 12.2 4.3 4.7 4.8  100

“On how many occasions during the past twelve months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?” (ordinal [1,2,3,4,5,6,7,8,9], v1_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v1_clin_alc_5orm<-ifelse((v1_clin$v1_tabalk_ta9_alkkonsum==1 | v1_clin$v1_tabalk_ta9_alkkonsum==2 | v1_clin$v1_tabalk_ta9_alkkonsum==3), 
                    -999,ifelse(is.na(v1_clin$v1_tabalk_ta10_alk_haeufigk_m)==T, 
                    v1_clin$v1_tabalk_ta11_alk_haeufigk_f,v1_clin$v1_tabalk_ta10_alk_haeufigk_m))

v1_con_alc_5orm<-ifelse((v1_con$v1_tabalk_alkohol9==1 | v1_con$v1_tabalk_alkohol9==2 | v1_con$v1_tabalk_alkohol9==3), 
                    -999,ifelse(is.na(v1_con$v1_tabalk_alkohol10)==T, 
                    v1_con$v1_tabalk_alkohol11,v1_con$v1_tabalk_alkohol10))

v1_alc_5orm<-factor(c(v1_clin_alc_5orm,v1_con_alc_5orm), ordered=T)
descT(v1_alc_5orm)
##                -999 1    2   3  4   5  6   7   8   9   <NA>     
## [1,] No. cases 789  213  98  93 64  61 70  43  17  14  81   1543
## [2,] Percent   51.1 13.8 6.4 6  4.1 4  4.5 2.8 1.1 0.9 5.2  100

Lifetime alcohol dependence? (dichotomous, v1_lftm_alc_dep)

The criteria for alcohol dependence are checked, resulting in a dichotomous assessment whether lifetime alcohol dependence is present.

v1_clin_lftm_alc_dep<-ifelse(v1_clin$v1_tabalk_ta12_alk_vorhanden==1,"N",ifelse(v1_clin$v1_tabalk_ta12_alk_vorhanden==3,"Y",NA))
v1_con_lftm_alc_dep<-ifelse(v1_con$v1_tabalk_alkohol_abhaengig==1,"N",ifelse(v1_con$v1_tabalk_alkohol_abhaengig==3,"Y",NA))

v1_lftm_alc_dep<-factor(c(v1_clin_lftm_alc_dep,v1_con_lftm_alc_dep))
descT(v1_lftm_alc_dep)
##                N    Y   <NA>     
## [1,] No. cases 1267 127 149  1543
## [2,] Percent   82.1 8.2 9.7  100

Illicit drugs

In the PsyCourse Study, much information on illigit drugs was collected. Specifically, at the first visit, it was assessed whether the participant ever consumed illicit drugs, and, if yes:

  • The name of each illicit drug ever consumed
  • The category of each illicit drug ever consumed
  • The frequency of consumption of each illicit drug during the period in which the drug was most heavily used
  • The time period over which each illicit drug was consumed
  • Whether each illicit drug was consumed in the past six months
  • How frequently each drug was consumed per month
  • Whether the individual developed tolerance to the drug

In the present dataset, only a few variables (see below and follow-up visits) are included if you want to undertake specific studies on illicit drugs, we can provide more specialized information.

Preparations for preparing data on illicit drugs

Check whether for each illicit drug, only one category is ticked (code and results not shown).

Clinical participants

Control participants

Recode several original items because they are wrongly coded in secuTrial exports (the graphical user interface and the exports do not match).

Assessment of frequency consumed when most frequently consumed

#clinical
#define function that recodes
v1_clin_mst_oft_recode <- function(drg_no) {

attach(v1_clin) 
v1_clin_ill_mst_frq_oftn_mod<-ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_s_dga_haeufigk_30043_",drg_no,sep="")))==1,2,NA)))))
detach(v1_clin)   

assign(noquote(paste("v1_clin_drg_",drg_no,"_mst_frq_oftn",sep="")),v1_clin_ill_mst_frq_oftn_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 9)
for(no in c(1:9)){v1_clin_mst_oft_recode(no)}
  
#control
#define function that recodes
v1_con_mst_oft_recode <- function(drg_no) {

attach(v1_con) 
v1_con_ill_mst_frq_oftn_mod<-ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_droge_haeufig_117983_",drg_no,sep="")))==1,2,NA)))))
detach(v1_con)  
assign(noquote(paste("v1_con_drg_",drg_no,"_mst_frq_oftn",sep="")),v1_con_ill_mst_frq_oftn_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 8)
for(no in c(1:8)){v1_con_mst_oft_recode(no)}

Assessment of frequency consumed in the past six months

v1_clin_pst_six_frq_recode <- function(drg_no) {

attach(v1_clin)
v1_clin_ill_pst_six_frq_mod<-ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_s_dge_l6m_haeufig_30043_",drg_no,sep="")))==1,2,NA)))))
detach(v1_clin)

assign(noquote(paste("v1_clin_drg_",drg_no,"_pst_six_oftn",sep="")),v1_clin_ill_pst_six_frq_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 9)
for(no in c(1:9)){v1_clin_pst_six_frq_recode(no)}


v1_con_pst_six_frq_recode <- function(drg_no) {

attach(v1_con)
v1_con_ill_pst_six_frq_mod<-ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==5,1,
                               ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==4,5,
                                ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==3,4,
                                 ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==2,3,
                                  ifelse(eval(as.name(paste("v1_drogen_droge_letzte6m_117983_",drg_no,sep="")))==1,2,NA)))))
detach(v1_con) 
assign(noquote(paste("v1_con_drg_",drg_no,"_pst_six_oftn",sep="")),v1_con_ill_pst_six_frq_mod, envir=globalenv())
}

#apply function to all drugs (maximal number: 8)
for(no in c(1:8)){v1_con_pst_six_frq_recode(no)}

Prepare the illicit drug items contained in this dataset

“Have you ever taken illicit drugs?” (dichotomous, v1_evr_ill_drg)

v1_clin_evr_ill_drg<-ifelse(v1_clin$v1_drogen_dg1_konsum==2, "Y", "N")
v1_con_evr_ill_drg<-ifelse(v1_con$v1_drogen_drogenkonsum==2, "Y", "N")

v1_evr_ill_drg<-factor(c(v1_clin_evr_ill_drg,v1_con_evr_ill_drg))
descT(v1_evr_ill_drg)
##                N   Y    <NA>     
## [1,] No. cases 818 659  66   1543
## [2,] Percent   53  42.7 4.3  100

Make datasets containing only information on illicit drugs

v1_drg_clin<-v1_clin[,881:1023]
v1_drg_con<-v1_con[,505:631]

Create vectors containg the different names of the different drug categories (and their dataframes; see below)

v1_drg_cat<-c("sti","can","opi","kok","hal","inh","tra","var")
v1_drg_cat_df_clin<-c("v1_sti_cli","v1_can_cli","v1_opi_cli","v1_kok_cli","v1_hal_cli","v1_inh_cli","v1_tra_cli","v1_var_cli")
v1_drg_cat_df_con<-c("v1_sti_con","v1_can_con","v1_opi_con","v1_kok_con","v1_hal_con","v1_inh_con","v1_tra_con","v1_var_con")

names(v1_drg_cat_df_clin)<-c(1:8)
names(v1_drg_cat_df_con)<-c(1:8)

The following variables are created below:

“Number of stimulants drugs ever consumed” (continuous, v1_sti)
“Number of cannabis drugs ever consumed” (continuous, v1_can)
“Number of opioid drugs ever consumed” (continuous, v1_opi)
“Number of cocaine drugs ever consumed” (continuous, v1_kok)
“Number of hallucinogenic drugs ever consumed” (continuous, v1_hal)
“Number of inhalant drugs ever consumed” (continuous, v1_inh)
“Number of tranquillizer drugs ever consumed” (continuous, v1_tra)
“Number of other drugs ever consumed” (continuous, v1_var)

The category of each drug is classified by checking a checkbox, corresponding to the category (e.g. stimulants). Here, for each individual drug ever taken, I select each checkbox corresponding to the same category, and sum across these, resulting in the number of drugs from the category (e.g. “stimulants”) that was ever taken. The same is done for all other categories.

Create dataframes, each for a different drug category, separately for clinical and control individuals. The last column of each of these dataframes contains the count across the rows of each dataframe.

for(i in c(1:8)){
   assign(v1_drg_cat_df_clin[i],row_sums(v1_drg_clin[,grep(paste("v1_drogen_s_dg_drogekt",i,"_30043_",sep=""),names(v1_drg_clin))],var="v1_clin_evr",n=1))
   assign(v1_drg_cat_df_con[i],row_sums(v1_drg_con[,grep(paste("v1_drogen_droge",i,"_117983_",sep=""),names(v1_drg_con))],var="v1_con_evr",n=1))
}

Bind each last column of the dataframes created above together. This results in a dataframe, in which the number of drugs from each category a participant has EVER TAKEN are listed.

Clinical participants: Combine into one dataframe

v1_drg_evr_cats_clin<-data.frame(v1_sti_cli[,dim(v1_sti_cli)[2]],
                         v1_can_cli[,dim(v1_can_cli)[2]],
                         v1_opi_cli[,dim(v1_opi_cli)[2]],
                         v1_kok_cli[,dim(v1_kok_cli)[2]],
                         v1_hal_cli[,dim(v1_hal_cli)[2]],
                         v1_inh_cli[,dim(v1_inh_cli)[2]],
                         v1_tra_cli[,dim(v1_tra_cli)[2]],
                         v1_var_cli[,dim(v1_var_cli)[2]])

names(v1_drg_evr_cats_clin)<-paste("v1_",v1_drg_cat,"_cat_evr",sep="")

Control participants: Combine into one dataframe

v1_drg_evr_cats_con<-data.frame(v1_sti_con[,dim(v1_sti_con)[2]],
                         v1_can_con[,dim(v1_can_con)[2]],
                         v1_opi_con[,dim(v1_opi_con)[2]],
                         v1_kok_con[,dim(v1_kok_con)[2]],
                         v1_hal_con[,dim(v1_hal_con)[2]],
                         v1_inh_con[,dim(v1_inh_con)[2]],
                         v1_tra_con[,dim(v1_tra_con)[2]],
                         v1_var_con[,dim(v1_var_con)[2]])

names(v1_drg_evr_cats_con)<-names(v1_drg_evr_cats_clin)

**Combine clinical and control drug datasets into dataset drg_evr_cats

drg_evr_cats<-rbind(v1_drg_evr_cats_clin,v1_drg_evr_cats_con)

**“Was the participant ever a HEAVY USER of ANY DRUG? (dichotomous, v1_evr_hvy_usr)**

clin_evr_hvy_usr<-ifelse(apply(data.frame(v1_clin_drg_1_mst_frq_oftn,
                                          v1_clin_drg_2_mst_frq_oftn,
                                          v1_clin_drg_3_mst_frq_oftn,
                                          v1_clin_drg_4_mst_frq_oftn,
                                          v1_clin_drg_5_mst_frq_oftn,
                                          v1_clin_drg_6_mst_frq_oftn,
                                          v1_clin_drg_7_mst_frq_oftn,
                                          v1_clin_drg_8_mst_frq_oftn,
                                          v1_clin_drg_9_mst_frq_oftn), 1, function(m) any(m %in% c(4,5))),"Y","N")

con_evr_hvy_usr<-ifelse(apply(data.frame(v1_con_drg_1_mst_frq_oftn,
                                          v1_con_drg_2_mst_frq_oftn,
                                          v1_con_drg_3_mst_frq_oftn,
                                          v1_con_drg_4_mst_frq_oftn,
                                          v1_con_drg_5_mst_frq_oftn,
                                          v1_con_drg_6_mst_frq_oftn,
                                          v1_con_drg_7_mst_frq_oftn,
                                          v1_con_drg_8_mst_frq_oftn), 1, function(m) any(m %in% c(4,5))),"Y","N")

drg_evr_cats$evr_hvy_usr<-c(clin_evr_hvy_usr,con_evr_hvy_usr)

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v1_pst6_ill_drg)

v1_clin_lst6_any<-ifelse(apply(v1_drg_clin[,grep("v1_drogen_s_dgd_letzte6m_30043_",names(v1_drg_clin))], 1, function(r) any(r %in% 1)),"Y","N")
v1_con_lst6_any<-ifelse(apply(v1_drg_con[,grep("v1_drogen_droge_letzte6m_117983_",names(v1_drg_con))], 1, function(r) any(r %in% 1)),"Y","N")

v1_pst6_ill_drg<-c(v1_clin_lst6_any,v1_con_lst6_any)
v1_pst6_ill_drg[is.na(v1_evr_ill_drg)]<-NA #make NA if this item was not assessed

IMPORTANT: Make all row in df drg_evr_cats NA in individuals in which the drug consumption section was NOT assessed.

drg_evr_cats[is.na(v1_evr_ill_drg),]<-NA

Create dataset

v1_subst<-data.frame(v1_ever_smkd,
                     v1_age_smk,
                     v1_no_cig,
                     v1_alc_pst12_mths,
                     v1_alc_5orm,
                     v1_lftm_alc_dep,
                     v1_evr_ill_drg,
                     drg_evr_cats,
                     v1_pst6_ill_drg)

Visit 1: DSM-IV Diagnosis (only in clinical participants)

Parts of the Structured Clinical Interview for DSM Disorders (SCID) were carried out (sections A Affective Syndromes, B Psychotic and Associated Symptoms, X Suicide attempts and suicidal ideation, and either section C (Differential diagnosis of Psychotic disorders) or section D (Differential diagnosis of Affective disorders). The most important variables of sections A, B, ans X are inclueded in this dataset, usually coded “Y”-yes, “N”-no and “U”-unknown.

Control participants have “-999” in every SCID variable.

Diagnosis according to DSM-IV

DSM-IV number diagnosis (categorical [295.10, 295.20, 295.30, 295.40, 295.60, 295.70, 295.90, 296.X, 296.3, 296.89, 298.80], v1_scid_dsm_dx)

We have included the DSM-IV diagnosis resulting from the combined efforts of a SCID interview and a screening of medical records (if available). Difficult cases were resolved by a discussion of experts.

v1_clin_scid_dsm_dx<-v1_clin$v1_skid_deckblatt_dsmiv_konsensusdiag_34407_1
v1_clin_scid_dsm_dx<-as.character(v1_clin_scid_dsm_dx)

v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="295.3"]<-"295.30"
v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="296.x"]<-"296.X"
v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="269.89"]<-"296.89"
v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="298.3"]<-"296.3"
v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="296.33"]<-"296.3"
v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="296.44"]<-"296.X"
v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="296.43"]<-"296.X"
v1_clin_scid_dsm_dx[v1_clin_scid_dsm_dx=="296.40"]<-"296.X"

v1_con_scid_dsm_dx<-rep("-999",dim(v1_con)[1])

v1_scid_dsm_dx<-c(v1_clin_scid_dsm_dx,v1_con_scid_dsm_dx)
descT(v1_scid_dsm_dx)
##                -999 295.10 295.20 295.30 295.40 295.60 295.70 295.90 296.3
## [1,] No. cases 320  14     10     436    12     4      101    12     91   
## [2,] Percent   20.7 0.9    0.6    28.3   0.8    0.3    6.5    0.8    5.9  
##      296.89 296.X 298.80     
## [1,] 110    427   6      1543
## [2,] 7.1    27.7  0.4    100

DSM-IV diagnosis categories (categorical [Depression, Schizophrenia, Schizoaffective Disorder, Brief Psychotic Disorder, Bipolar-I Disorder, Bipolar-II Disorder], v1_scid_dsm_dx_cat)

DSM-IV diagnoses in human readable form for non-clinical researchers. Please note the following: schizophrenia and schizoaffective disorder are often grouped together under the label schizophrenia, although these are two different disorders. Schizophreniform disorder is diagnosed when symptoms of schizophrenia are not (yet) present for the full six months required for a proper schizophrenia diagnosis. Brief psychotic disorder is distinct from schizophrenia. Bipolar-I disorder is diagnosed when both manic and depressive phases are present in an individual; a distinct diagnosis is bipolar-II in which “only” hypomanic and depressive phases are present. Bipolar-I and bipolar-II are both proper bipolar disorders. Control subjects are labeled as “Control”.

v1_clin_scid_dsm_dx_cat<-rep("NA",dim(v1_clin)[1])
v1_con_scid_dsm_dx_cat<-rep("NA",dim(v1_con)[1])

v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("295.10", "295.20", "295.30", "295.60", 
                               "295.90")]<-"Schizophrenia"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("295.70")]<-"Schizoaffective Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("295.40")]<-"Schizophreniform Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("298.80")]<-"Brief Psychotic Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("296.X")]<-"Bipolar-I Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("296.89")]<-"Bipolar-II Disorder"
v1_clin_scid_dsm_dx_cat[v1_clin_scid_dsm_dx %in% c("296.3")]<-"Depression"

v1_con_scid_dsm_dx_cat<-rep("Control",dim(v1_con)[1])

v1_scid_dsm_dx_cat<-c(v1_clin_scid_dsm_dx_cat,v1_con_scid_dsm_dx_cat)
descT(v1_scid_dsm_dx_cat)
##                Bipolar-I Disorder Bipolar-II Disorder
## [1,] No. cases 427                110                
## [2,] Percent   27.7               7.1                
##      Brief Psychotic Disorder Control Depression Schizoaffective Disorder
## [1,] 6                        320     91         101                     
## [2,] 0.4                      20.7    5.9        6.5                     
##      Schizophrenia Schizophreniform Disorder     
## [1,] 476           12                        1543
## [2,] 30.8          0.8                       100

The following items were assessed in clincal participants only!

A Affective Syndromes

Major depressive episode (MDE)**

Age at first MDD episode (continuous [years], v1_scid_age_MDE) This item includes all individuals that ever fulfilled MDD criteria. Also a person with e.g. schizophrenia can have a value on this item. If the individual ever fulfilled MDD criteria, the age at the first MDD episode is given. NA on this item means that age at first MDD episode is missing. Individuals that never experienced an MDD episode are coded as “-999”.

Control individiuals are coded as “-999”.

v1_clin_scid_age_MDE<-ifelse(is.na(v1_clin$v1_sna_21_mde1_a79_beurteilung),-999,
  ifelse(v1_clin$v1_sna_21_mde1_a79_beurteilung==3, v1_clin$v1_sna_21_mde1_s_snx_alter_jahre_30038_1, 
       ifelse((v1_clin$v1_sna_21_mde1_a79_beurteilung==1 | v1_clin$v1_sna_21_mde1_a79_beurteilung==0), -999, NA)))

descT(v1_clin_scid_age_MDE)
##                -999 2   4   6   7   8   9   10  11  12 13  14  15  16  17 
## [1,] No. cases 355  1   1   3   4   1   4   8   10  12 13  26  21  39  39 
## [2,] Percent   29   0.1 0.1 0.2 0.3 0.1 0.3 0.7 0.8 1  1.1 2.1 1.7 3.2 3.2
##      18  19  20  21  22 23  24  25  26  27  28  29 30  31  32 33  34 35 
## [1,] 34  44  40  41  24 30  34  34  21  27  19  12 30  17  25 13  12 8  
## [2,] 2.8 3.6 3.3 3.4 2  2.5 2.8 2.8 1.7 2.2 1.6 1  2.5 1.4 2  1.1 1  0.7
##      36  37  38  39  40  41 42  43 44  45  46  47  48  49  50  51  52  53 
## [1,] 10  13  5   6   14  12 10  12 5   14  4   8   8   5   8   2   8   5  
## [2,] 0.8 1.1 0.4 0.5 1.1 1  0.8 1  0.4 1.1 0.3 0.7 0.7 0.4 0.7 0.2 0.7 0.4
##      54  55  56  57  58  59  60  61  69  <NA>     
## [1,] 3   2   2   1   1   1   3   1   1   57   1223
## [2,] 0.2 0.2 0.2 0.1 0.1 0.1 0.2 0.1 0.1 4.7  100
v1_scid_age_MDE<-c(v1_clin_scid_age_MDE,rep(-999,dim(v1_con)[1]))
descT(v1_scid_age_MDE)
##                -999 2   4   6   7   8   9   10  11  12  13  14  15  16 
## [1,] No. cases 675  1   1   3   4   1   4   8   10  12  13  26  21  39 
## [2,] Percent   43.7 0.1 0.1 0.2 0.3 0.1 0.3 0.5 0.6 0.8 0.8 1.7 1.4 2.5
##      17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33 
## [1,] 39  34  44  40  41  24  30  34  34  21  27  19  12  30  17  25  13 
## [2,] 2.5 2.2 2.9 2.6 2.7 1.6 1.9 2.2 2.2 1.4 1.7 1.2 0.8 1.9 1.1 1.6 0.8
##      34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50 
## [1,] 12  8   10  13  5   6   14  12  10  12  5   14  4   8   8   5   8  
## [2,] 0.8 0.5 0.6 0.8 0.3 0.4 0.9 0.8 0.6 0.8 0.3 0.9 0.3 0.5 0.5 0.3 0.5
##      51  52  53  54  55  56  57  58  59  60  61  69  <NA>     
## [1,] 2   8   5   3   2   2   1   1   1   3   1   1   57   1543
## [2,] 0.1 0.5 0.3 0.2 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 3.7  100

Number of MDD episodes (continuous [number], v1_scid_no_MDE) In individuals that ever fulfilled MDD criteria, the number of MDD episodes is given. Please note the following:

  • NA on this item means that the number of MDD episodes is missing.
  • Individuals that never experienced an MDD episode are coded as “-999”.
  • If the episodes could only be poorly delimited from each other, “99” was coded.
v1_clin_scid_no_MDE<-ifelse(is.na(v1_clin$v1_sna_21_mde1_a79_beurteilung),-999,
  ifelse(v1_clin$v1_sna_21_mde1_a79_beurteilung==3, v1_clin$v1_sna_21_mde1_a81_anzahl, 
       ifelse((v1_clin$v1_sna_21_mde1_a79_beurteilung==1 | v1_clin$v1_sna_21_mde1_a79_beurteilung==0), -999, NA)))

descT(v1_clin_scid_no_MDE)
##                -999 1   2   3   4   5   6   7   8   9   10  11  12  13 
## [1,] No. cases 355  71  93  90  79  60  34  17  32  9   38  7   14  3  
## [2,] Percent   29   5.8 7.6 7.4 6.5 4.9 2.8 1.4 2.6 0.7 3.1 0.6 1.1 0.2
##      14  15  16  17  18  20  21  22  23  25  26  29  30 32  35  36  39 
## [1,] 4   17  2   3   1   18  1   1   1   3   2   2   12 1   1   1   1  
## [2,] 0.3 1.4 0.2 0.2 0.1 1.5 0.1 0.1 0.1 0.2 0.2 0.2 1  0.1 0.1 0.1 0.1
##      40  50  60  70  75  99   <NA>     
## [1,] 2   3   2   1   1   188  53   1223
## [2,] 0.2 0.2 0.2 0.1 0.1 15.4 4.3  100
v1_scid_no_MDE<-c(v1_clin_scid_no_MDE,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_MDE)
##                -999 1   2  3   4   5   6   7   8   9   10  11  12  13  14 
## [1,] No. cases 675  71  93 90  79  60  34  17  32  9   38  7   14  3   4  
## [2,] Percent   43.7 4.6 6  5.8 5.1 3.9 2.2 1.1 2.1 0.6 2.5 0.5 0.9 0.2 0.3
##      15  16  17  18  20  21  22  23  25  26  29  30  32  35  36  39  40 
## [1,] 17  2   3   1   18  1   1   1   3   2   2   12  1   1   1   1   2  
## [2,] 1.1 0.1 0.2 0.1 1.2 0.1 0.1 0.1 0.2 0.1 0.1 0.8 0.1 0.1 0.1 0.1 0.1
##      50  60  70  75  99   <NA>     
## [1,] 3   2   1   1   188  53   1543
## [2,] 0.2 0.1 0.1 0.1 12.2 3.4  100

Mania and hypomania

Age at first manic episode (continuous, v1_scid_age_mania) This item includes all individuals that ever fulfilled mania criteria. Also a person with e.g. schizophrenia can have a value on this item. If the individual ever fulfilled mania criteria, the age at the first manic episode is given. NA on this item means that age at first mania episode is missing.

Individuals that never experienced a manic episode are coded as “-999”.

Control individiuals are coded as “-999”.

v1_clin_scid_age_mania<-ifelse(is.na(v1_clin$v1_sna_23_manie1_a142_beurteilung),-999,
  ifelse(v1_clin$v1_sna_23_manie1_a142_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a143_alter_jahre, 
       ifelse((v1_clin$v1_sna_23_manie1_a142_beurteilung==1 | v1_clin$v1_sna_23_manie1_a142_beurteilung==0), -999, NA)))

descT(v1_clin_scid_age_mania)
##                -999 5   10  11  12  13  14  15  16  17 18  19 20  21  22 
## [1,] No. cases 693  1   1   2   2   3   6   9   18  12 18  12 31  15  18 
## [2,] Percent   56.7 0.1 0.1 0.2 0.2 0.2 0.5 0.7 1.5 1  1.5 1  2.5 1.2 1.5
##      23  24  25  26  27  28  29  30 31  32  33  34  35  36  37  38  39 
## [1,] 21  21  31  16  17  15  9   12 7   6   9   17  5   8   8   8   5  
## [2,] 1.7 1.7 2.5 1.3 1.4 1.2 0.7 1  0.6 0.5 0.7 1.4 0.4 0.7 0.7 0.7 0.4
##      40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56 
## [1,] 14  14  8   6   6   9   5   3   2   4   4   4   2   4   3   2   3  
## [2,] 1.1 1.1 0.7 0.5 0.5 0.7 0.4 0.2 0.2 0.3 0.3 0.3 0.2 0.3 0.2 0.2 0.2
##      57  59  60  61  65  <NA>     
## [1,] 3   1   2   1   2   65   1223
## [2,] 0.2 0.1 0.2 0.1 0.2 5.3  100
v1_scid_age_mania<-c(v1_clin_scid_age_mania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_age_mania)
##                -999 5   10  11  12  13  14  15  16  17  18  19  20 21 22 
## [1,] No. cases 1013 1   1   2   2   3   6   9   18  12  18  12  31 15 18 
## [2,] Percent   65.7 0.1 0.1 0.1 0.1 0.2 0.4 0.6 1.2 0.8 1.2 0.8 2  1  1.2
##      23  24  25 26 27  28 29  30  31  32  33  34  35  36  37  38  39  40 
## [1,] 21  21  31 16 17  15 9   12  7   6   9   17  5   8   8   8   5   14 
## [2,] 1.4 1.4 2  1  1.1 1  0.6 0.8 0.5 0.4 0.6 1.1 0.3 0.5 0.5 0.5 0.3 0.9
##      41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57 
## [1,] 14  8   6   6   9   5   3   2   4   4   4   2   4   3   2   3   3  
## [2,] 0.9 0.5 0.4 0.4 0.6 0.3 0.2 0.1 0.3 0.3 0.3 0.1 0.3 0.2 0.1 0.2 0.2
##      59  60  61  65  <NA>     
## [1,] 1   2   1   2   65   1543
## [2,] 0.1 0.1 0.1 0.1 4.2  100

Number of manic episodes (continuous [number], v1_scid_no_mania) In individuals that ever fulfilled criteria for mania, the number of mania episodes is given. Please note the following:

  • NA on this item means that the number of mania episodes is missing.
  • Individuals that never experienced an episode of mania are coded as “-999”.
  • If the episodes could only be poorly delimited from each other, “99” was coded.
v1_clin_scid_no_mania<-ifelse(is.na(v1_clin$v1_sna_23_manie1_a142_beurteilung),-999,
  ifelse(v1_clin$v1_sna_23_manie1_a142_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a145_anzahl2, 
       ifelse((v1_clin$v1_sna_23_manie1_a142_beurteilung==1 | v1_clin$v1_sna_23_manie1_a142_beurteilung==0), -999, NA)))

descT(v1_clin_scid_no_mania)
##                -999 1   2   3   4   5   6   7  8   10 11  12  13  14  15 
## [1,] No. cases 693  83  68  54  35  34  19  12 14  24 2   4   1   2   3  
## [2,] Percent   56.7 6.8 5.6 4.4 2.9 2.8 1.6 1  1.1 2  0.2 0.3 0.1 0.2 0.2
##      17  20 22  24  25  30  50  96  99  <NA>     
## [1,] 1   12 1   1   1   4   1   1   47  106  1223
## [2,] 0.1 1  0.1 0.1 0.1 0.3 0.1 0.1 3.8 8.7  100
v1_scid_no_mania<-c(v1_clin_scid_no_mania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_mania)
##                -999 1   2   3   4   5   6   7   8   10  11  12  13  14 
## [1,] No. cases 1013 83  68  54  35  34  19  12  14  24  2   4   1   2  
## [2,] Percent   65.7 5.4 4.4 3.5 2.3 2.2 1.2 0.8 0.9 1.6 0.1 0.3 0.1 0.1
##      15  17  20  22  24  25  30  50  96  99 <NA>     
## [1,] 3   1   12  1   1   1   4   1   1   47 106  1543
## [2,] 0.2 0.1 0.8 0.1 0.1 0.1 0.3 0.1 0.1 3  6.9  100

Age at first hypomanic episode (continuous, v1_scid_age_hypomania) This item includes all individuals that ever fulfilled criteria for hypomania, but never fullfilled criteria for mania. Also a person with e.g. schizophrenia can have a value on this item. If the individual ever fulfilled hypomania criteria (without ever fulfilling criteria of mania), the age at the first hypomanic episode is given. NA on this item means that age at first hypomanic episode is missing.

Individuals that never experienced a hypomanic episode, or had one or more mania episodes, are coded as “-999”.

Control individiuals are coded as “-999”.

v1_clin_scid_age_hypomania<-ifelse(is.na(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung),-999,
  ifelse(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a160a_alter_jahre, 
       ifelse((v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==1 | v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==0), -999, NA)))

descT(v1_clin_scid_age_hypomania)
##                -999 13  15  16  17  18  19  20  21  22  23  24  25  26 
## [1,] No. cases 1101 2   5   4   1   3   7   4   6   5   7   1   4   3  
## [2,] Percent   90   0.2 0.4 0.3 0.1 0.2 0.6 0.3 0.5 0.4 0.6 0.1 0.3 0.2
##      27  28  29  30  31  32  33  34  35  36  37  38  40  42  43  45  46 
## [1,] 4   1   2   3   2   3   2   3   2   2   3   2   2   1   1   2   2  
## [2,] 0.3 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.2 0.2
##      47  48  50  51  53  54  55  65  <NA>     
## [1,] 1   1   2   1   1   4   4   1   18   1223
## [2,] 0.1 0.1 0.2 0.1 0.1 0.3 0.3 0.1 1.5  100
v1_scid_age_hypomania<-c(v1_clin_scid_age_hypomania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_age_hypomania)
##                -999 13  15  16  17  18  19  20  21  22  23  24  25  26 
## [1,] No. cases 1421 2   5   4   1   3   7   4   6   5   7   1   4   3  
## [2,] Percent   92.1 0.1 0.3 0.3 0.1 0.2 0.5 0.3 0.4 0.3 0.5 0.1 0.3 0.2
##      27  28  29  30  31  32  33  34  35  36  37  38  40  42  43  45  46 
## [1,] 4   1   2   3   2   3   2   3   2   2   3   2   2   1   1   2   2  
## [2,] 0.3 0.1 0.1 0.2 0.1 0.2 0.1 0.2 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1
##      47  48  50  51  53  54  55  65  <NA>     
## [1,] 1   1   2   1   1   4   4   1   18   1543
## [2,] 0.1 0.1 0.1 0.1 0.1 0.3 0.3 0.1 1.2  100

Number of hypomanic episodes (continuous [number (but see below)], v1_scid_no_hypomania) In individuals that ever fulfilled criteria for hypomania, but never fullfilled criteria for mania, the number of hypomanic episodes is given. Please note the following:

  • NA on this item means that the number of hypomanic episode is missing.
  • Individuals that never experienced an episode of hypomania, or had one or more episodes of mania, are coded as “-999”.
  • If the episodes could only be poorly delimited from each other, “99” was coded.
v1_clin_scid_no_hypomania<-ifelse(is.na(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung),-999,
  ifelse(v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==3, v1_clin$v1_sna_24_hypomane1_a161_anzahl, 
       ifelse((v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==1 | v1_clin$v1_sna_24_hypomane1_a_160_beurteilung==0), -999, NA)))

descT(v1_clin_scid_no_hypomania)
##                -999 1   2   3   4   5   6   7   8   10  12  14  15  18 
## [1,] No. cases 1101 19  14  9   7   4   2   5   2   5   1   1   2   1  
## [2,] Percent   90   1.6 1.1 0.7 0.6 0.3 0.2 0.4 0.2 0.4 0.1 0.1 0.2 0.1
##      20  30  50  70  75  99  <NA>     
## [1,] 2   1   1   1   1   13  31   1223
## [2,] 0.2 0.1 0.1 0.1 0.1 1.1 2.5  100
v1_scid_no_hypomania<-c(v1_clin_scid_no_hypomania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_hypomania)
##                -999 1   2   3   4   5   6   7   8   10  12  14  15  18 
## [1,] No. cases 1421 19  14  9   7   4   2   5   2   5   1   1   2   1  
## [2,] Percent   92.1 1.2 0.9 0.6 0.5 0.3 0.1 0.3 0.1 0.3 0.1 0.1 0.1 0.1
##      20  30  50  70  75  99  <NA>     
## [1,] 2   1   1   1   1   13  31   1543
## [2,] 0.1 0.1 0.1 0.1 0.1 0.8 2    100

B Psychotic and associated symptoms

This section was assessed in all study participants. If at least one item covering delusions (delusion item) or hallucinations (hallucination item) was answered in the affirmative this was coded “Y”, otherwise “N”. Please note that an individual was also coded as “N” if there was insufficient information (“0” on the original SCID item; occuring only in few individuals) or if the symptom was too mild to fulfill criteria (“2” on the original SCID item).

NA means that at least one question was not completed and all other completed questions were coded “N”.

Ever experienced delusions? (dichotomous, v1_scid_ever_delus)

v1_clin_scid_ever_delus<-ifelse((is.na(v1_clin$v1_snb_31_prodsymp1_b12_beeinflusswahn) &
                                  is.na(v1_clin$v1_snb_31_prodsymp1_b13_gedankenentzug) &
                                  is.na(v1_clin$v1_snb_31_prodsymp1_b14_gedankenueber) &        
                                  v1_clin$v1_snb_31_prodsymp1_b1_beziehungswahn!=3 & 
                                  v1_clin$v1_snb_31_prodsymp1_b2_verfolgungswahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b3_groessenwahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b4_koerper_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b6_relig_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b7_schuld_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b8_eifer_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b9_liebe_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b10_nihilist_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b11_veram_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b12_sonstiger_wahn!=3), "N",
                            ifelse((v1_clin$v1_snb_31_prodsymp1_b12_beeinflusswahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b13_gedankenentzug!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b14_gedankenueber!=3 &        
                                  v1_clin$v1_snb_31_prodsymp1_b1_beziehungswahn!=3 & 
                                  v1_clin$v1_snb_31_prodsymp1_b2_verfolgungswahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b3_groessenwahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b4_koerper_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b6_relig_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b7_schuld_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b8_eifer_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b9_liebe_wahnideen!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b10_nihilist_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b11_veram_wahn!=3 &
                                  v1_clin$v1_snb_31_prodsymp1_b12_sonstiger_wahn!=3), "N",      
                          ifelse((v1_clin$v1_snb_31_prodsymp1_b1_beziehungswahn==3 | 
                                  v1_clin$v1_snb_31_prodsymp1_b2_verfolgungswahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b3_groessenwahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b4_koerper_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b6_relig_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b7_schuld_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b8_eifer_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b9_liebe_wahnideen==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b10_nihilist_wahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b11_veram_wahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b12_sonstiger_wahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b12_beeinflusswahn==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b13_gedankenentzug==3 |
                                  v1_clin$v1_snb_31_prodsymp1_b14_gedankenueber==3),"Y",NA)))

v1_scid_ever_delus<-factor(c(v1_clin_scid_ever_delus,rep(-999,dim(v1_con)[1])))
summary(v1_scid_ever_delus)
## -999    N    Y NA's 
##  320  302  862   59

Ever experienced hallucinations? (dichotomous, v1_scid_ever_halls)

v1_clin_scid_ever_halls<-ifelse(v1_clin$v1_snb_31_prodsymp1_b21_olfaktor_halluz==3 | 
                       v1_clin$v1_snb_31_prodsymp1_b_21_gustator_halluz==3 |
                       v1_clin$v1_snb_31_prodsymp1_b20_taktil_halluz==3  |
                       v1_clin$v1_snb_31_prodsymp1_b19_opt_halluz==3 |
                       v1_clin$v1_snb_31_prodsymp1_b16_akust_halluz==3, "Y", "N")

v1_scid_ever_halls<-factor(c(v1_clin_scid_ever_halls,rep(-999,dim(v1_con)[1])))
summary(v1_scid_ever_halls)
## -999    N    Y NA's 
##  320  580  608   35

Ever experienced psychotic symptoms? (dichotomous, v1_scid_ever_psyc)

This item combines the two previous items. If ever delusional or ever hallucinations then yes. This is a crude operational definition of psychosis, as it takes into account only symptoms and not e.g. level of functioning or other aspects.

v1_clin_scid_ever_psyc<-ifelse(v1_clin_scid_ever_delus=="Y" | v1_clin_scid_ever_halls=="Y","Y","N")
v1_scid_ever_psyc<-factor(c(v1_clin_scid_ever_psyc,rep("-999",dim(v1_con)[1])))
summary(v1_scid_ever_psyc)
## -999    N    Y NA's 
##  320  276  884   63

Age at first occurence of psychotic symptoms (continuous [years], v1_scid_age_fst_psyc)

v1_clin_scid_age_fst_psy<-c(v1_clin$v1_snc_41_schizo4_c17_alter_jahre,rep(-999,dim(v1_con)[1]))
v1_scid_age_fst_psyc<-ifelse(v1_scid_ever_psyc=="Y",v1_clin_scid_age_fst_psy,-999)
summary(v1_scid_age_fst_psyc[v1_scid_age_fst_psyc>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    6.00   21.00   27.00   29.22   35.00   73.00     543

Year in which first psychotic symptoms occured (continuous [year], v1_scid_yr_fst_psyc)

v1_clin_scid_yr_fst_psyc<-c(v1_clin$v1_snc_41_schizo4_c17_psycho_jahr,rep(-999,dim(v1_con)[1]))
v1_scid_yr_fst_psyc<-ifelse(v1_scid_ever_psyc=="Y",v1_clin_scid_yr_fst_psyc,-999)
descT(v1_scid_yr_fst_psyc)  
##                -999 1950 1962 1965 1967 1969 1970 1971 1973 1974 1975 1976
## [1,] No. cases 596  1    1    1    1    1    1    2    3    2    1    1   
## [2,] Percent   38.6 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.2  0.1  0.1  0.1 
##      1977 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991
## [1,] 6    3    1    6    1    3    5    4    3    4    10   5    4    8   
## [2,] 0.4  0.2  0.1  0.4  0.1  0.2  0.3  0.3  0.2  0.3  0.6  0.3  0.3  0.5 
##      1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
## [1,] 6    9    5    9    14   6    9    16   11   13   14   15   11   14  
## [2,] 0.4  0.6  0.3  0.6  0.9  0.4  0.6  1    0.7  0.8  0.9  1    0.7  0.9 
##      2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 <NA>     
## [1,] 17   21   12   10   17   20   12   13   7    4    584  1543
## [2,] 1.1  1.4  0.8  0.6  1.1  1.3  0.8  0.8  0.5  0.3  37.8 100

X Suicide attempts and suicidal ideation

Ever suicidal ideation (dichotomous, v1_scid_evr_suic_ide)

Please not that the following items on suicidal ideation were skipped (and coded -999) if this question was not answered positively. The answer “insufficient information” was coded as -999.

v1_scid_evr_suic_ide<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
                        
v1_scid_evr_suic_ide<-ifelse(c(v1_clin$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==0, -999,  
                      ifelse(c(v1_clin$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==1, "N", 
                      ifelse(c(v1_clin$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==3, "Y", 
                             v1_scid_evr_suic_ide))) 

descT(v1_scid_evr_suic_ide)
##                -999 N    Y    <NA>     
## [1,] No. cases 328  286  903  26   1543
## [2,] Percent   21.3 18.5 58.5 1.7  100

Suicidal ideation detailed (ordinal [1,2,3,4], v1_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).

v1_scid_suic_ide<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_scid_suic_ide<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
                         ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt,rep(-999,dim(v1_con)[1]))==1, 1, 
                          ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt,rep(-999,dim(v1_con)[1]))==2, 2, 
                            ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt,rep(-999,dim(v1_con)[1]))==3, 3, 
                              ifelse(c(v1_clin$v1_snx_112_suizged2_x8_suizged_inhalt==4,rep(-999,dim(v1_con)[1])), 4,                                                v1_scid_suic_ide)))))

v1_scid_suic_ide<-factor(v1_scid_suic_ide,ordered=T)
descT(v1_scid_suic_ide)
##                -999 1    2   3   4    <NA>     
## [1,] No. cases 614  291  123 128 330  57   1543
## [2,] Percent   39.8 18.9 8   8.3 21.4 3.7  100

Thoughts about methods (ordinal [1,2,3], v1_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3. The item is coded “-999” if skipped (see above).

v1_scid_suic_thght_mth<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_thght_mth<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
                        ifelse(c(v1_clin$v1_snx_112_suizged2_x10_suiz_methoden,rep(-999,dim(v1_con)[1]))==1, 1, 
                        ifelse(c(v1_clin$v1_snx_112_suizged2_x10_suiz_methoden,rep(-999,dim(v1_con)[1]))==2, 2, 
                        ifelse(c(v1_clin$v1_snx_112_suizged2_x10_suiz_methoden,rep(-999,dim(v1_con)[1]))==3, 3,
                        v1_scid_suic_thght_mth))))

v1_scid_suic_thght_mth<-factor(v1_scid_suic_thght_mth,ordered=T)
descT(v1_scid_suic_thght_mth)
##                -999 1    2    3    <NA>     
## [1,] No. cases 614  264  276  316  73   1543
## [2,] Percent   39.8 17.1 17.9 20.5 4.7  100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v1_scid_suic_note_thgts)

This is on ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).

v1_scid_suic_note_thgts<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_note_thgts<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
                          ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==1, 1,  
                            ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==2, 2,  
                              ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==3, 3, 
                                ifelse(c(v1_clin$v1_snx_112_suizged2_x11_best_erledigt,rep(-999,dim(v1_con)[1]))==4, 4,             v1_scid_suic_note_thgts)))))  
                
v1_scid_suic_note_thgts<-factor(v1_scid_suic_note_thgts,ordered=T)
descT(v1_scid_suic_note_thgts)
##                -999 1    2   3   4   <NA>     
## [1,] No. cases 614  657  45  28  116 83   1543
## [2,] Percent   39.8 42.6 2.9 1.8 7.5 5.4  100

Suicide attempt (categorical [1,2,3], v1_suic_attmpt)

This is on ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”.The answer “insufficient information was coded as -999.

v1_suic_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_suic_attmpt<-ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==0, -999, 
                  ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==1, 1, 
                    ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==2, 2, 
                      ifelse(c(v1_clin$v1_snx_111_suizvrs2_x1_suiz_vers,rep(-999,dim(v1_con)[1]))==3, 3, 
                             v1_suic_attmpt)))) 

v1_suic_attmpt<-factor(v1_suic_attmpt,ordered=T)
descT(v1_suic_attmpt)
##                -999 1    2   3    <NA>     
## [1,] No. cases 330  777  51  354  31   1543
## [2,] Percent   21.4 50.4 3.3 22.9 2    100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v1_scid_no_suic_attmpt)

This is on ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6. The item is coded “-999” if skipped (see above).

v1_scid_no_suic_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_scid_no_suic_attmpt<-ifelse(v1_suic_attmpt==1, -999, 
                ifelse(v1_suic_attmpt>1, c(v1_clin$v1_snx_111_suizvrs2_x2_suiz_anz,rep(-999,dim(v1_con)[1])), v1_scid_no_suic_attmpt))
           
v1_scid_no_suic_attmpt<-factor(v1_scid_no_suic_attmpt,ordered=T)
descT(v1_scid_no_suic_attmpt)
##                -999 1    2  3   <NA>     
## [1,] No. cases 1097 221  93 79  53   1543
## [2,] Percent   71.1 14.3 6  5.1 3.4  100

Preparation of suicide attempt (ordinal [1,2,3,4], v1_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4. The item is coded “-999” if skipped (see above).

v1_prep_suic_attp_ord<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_prep_suic_attp_ord<-ifelse(v1_suic_attmpt==1, -999, 
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==1, 1,
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==2, 2,     
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==3, 3,
                          ifelse(v1_suic_attmpt>1 & 
                                   c(v1_clin$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==4, 4,
                              v1_prep_suic_attp_ord))))) 

v1_prep_suic_attp_ord<-factor(v1_prep_suic_attp_ord,ordered=T)
descT(v1_prep_suic_attp_ord)
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1097 118 36  72  132 88   1543
## [2,] Percent   71.1 7.6 2.3 4.7 8.6 5.7  100

Suicidal attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v1_suic_note_attmpt)

This is on ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).

v1_suic_note_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))

v1_suic_note_attmpt<-ifelse(v1_suic_attmpt==1, -999, 
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==1, "1",
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==2, "2",
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==3, "3",
                     ifelse(v1_suic_attmpt>1 & c(v1_clin$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==4, "4",
                            v1_suic_note_attmpt))))) 

v1_suic_note_attmpt<-factor(v1_suic_note_attmpt,ordered=T)
descT(v1_suic_note_attmpt)
##                -999 1    2   3   4   <NA>     
## [1,] No. cases 1097 250  17  17  90  72   1543
## [2,] Percent   71.1 16.2 1.1 1.1 5.8 4.7  100

Create dataset

v1_scid<-data.frame(v1_scid_dsm_dx,
                    v1_scid_dsm_dx_cat,
                    v1_scid_age_MDE,
                    v1_scid_no_MDE,
                    v1_scid_age_mania,
                    v1_scid_no_mania,
                    v1_scid_age_hypomania,
                    v1_scid_no_hypomania,
                    v1_scid_ever_halls,
                    v1_scid_ever_delus,
                    v1_scid_ever_psyc,
                    v1_scid_age_fst_psyc,
                    v1_scid_yr_fst_psyc,
                    v1_scid_evr_suic_ide,
                    v1_scid_suic_ide,
                    v1_scid_suic_thght_mth,
                    v1_scid_suic_note_thgts,
                    v1_suic_attmpt,
                    v1_scid_no_suic_attmpt,
                    v1_prep_suic_attp_ord,
                    v1_suic_note_attmpt)

Visit 1: Symptom rating scales (interviewer rates individual)

Positive and Negative Sydrome Scale (PANSS)

The PANSS (Kay, Fiszbein, & Opler, 1987) is a rating scale measuring positive and negative symptoms in schizophrenia. It has three subscales: positive, negative and gereral psychopathology symptoms. Each item is rated on an ordinal scale from one to seven with the following gradation: “absent”-1, “minimal”-2, “mild”-3, “moderate”-4, “moderate severe”-5, “severe”-6, “extreme”-7. On all items, higher scores mean more severe symptoms. The ratings refer to the past seven days. Please find the items below.

Positive subscale

P1 Delusions (ordinal [1,2,3,4,5,6,7], v1_panss_p1)

v1_panss_p1<-c(v1_clin$v1_panss_p_p1_wahnideen,v1_con$v1_panss_p_p1_wahnideen)
v1_panss_p1<-factor(v1_panss_p1, ordered=T)
descT(v1_panss_p1)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 983  96  144 110 60  49  101  1543
## [2,] Percent   63.7 6.2 9.3 7.1 3.9 3.2 6.5  100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v1_panss_p2)

v1_panss_p2<-c(v1_clin$v1_panss_p_p2_form_denkst,v1_con$v1_panss_p_p2_form_denkst)
v1_panss_p2<-factor(v1_panss_p2, ordered=T)

descT(v1_panss_p2)
##                1    2   3    4   5  6   7   <NA>     
## [1,] No. cases 898  170 209  110 46 8   1   101  1543
## [2,] Percent   58.2 11  13.5 7.1 3  0.5 0.1 6.5  100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v1_panss_p3)

v1_panss_p3<-c(v1_clin$v1_panss_p_p3_halluz,v1_con$v1_panss_p_p3_halluz)
v1_panss_p3<-factor(v1_panss_p3, ordered=T)

descT(v1_panss_p3)
##                1    2   3   4   5   6  7   <NA>     
## [1,] No. cases 1188 76  66  56  40  15 1   101  1543
## [2,] Percent   77   4.9 4.3 3.6 2.6 1  0.1 6.5  100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v1_panss_p4)

v1_panss_p4<-c(v1_clin$v1_panss_p_p4_erregung,v1_con$v1_panss_p_p4_erregung)
v1_panss_p4<-factor(v1_panss_p4, ordered=T)

descT(v1_panss_p4)
##                1    2   3    4   5   6   <NA>     
## [1,] No. cases 961  147 251  58  17  3   106  1543
## [2,] Percent   62.3 9.5 16.3 3.8 1.1 0.2 6.9  100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v1_panss_p5)

v1_panss_p5<-c(v1_clin$v1_panss_p_p5_groessenideen,v1_con$v1_panss_p_p5_groessenideen)
v1_panss_p5<-factor(v1_panss_p5, ordered=T)

descT(v1_panss_p5)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1197 100 84  34  17  6   105  1543
## [2,] Percent   77.6 6.5 5.4 2.2 1.1 0.4 6.8  100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v1_panss_p6)

v1_panss_p6<-c(v1_clin$v1_panss_p_p6_misstr_verfolg,v1_con$v1_panss_p_p6_misstr_verfolg)
v1_panss_p6<-factor(v1_panss_p6, ordered=T)

descT(v1_panss_p6)
##                1    2   3    4   5  6   7   <NA>     
## [1,] No. cases 1023 144 173  56  31 12  1   103  1543
## [2,] Percent   66.3 9.3 11.2 3.6 2  0.8 0.1 6.7  100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v1_panss_p7)

v1_panss_p7<-c(v1_clin$v1_panss_p_p7_feindseligkeit,v1_con$v1_panss_p_p7_feindseligkeit)
v1_panss_p7<-factor(v1_panss_p7, ordered=T)

descT(v1_panss_p7)
##                1    2   3   4  5   <NA>     
## [1,] No. cases 1245 96  76  16 4   106  1543
## [2,] Percent   80.7 6.2 4.9 1  0.3 6.9  100

PANSS Positive sum score (continuous [7-49], v1_panss_sum_pos)

v1_panss_sum_pos<-as.numeric.factor(v1_panss_p1)+
                  as.numeric.factor(v1_panss_p2)+
                  as.numeric.factor(v1_panss_p3)+
                  as.numeric.factor(v1_panss_p4)+
                  as.numeric.factor(v1_panss_p5)+
                  as.numeric.factor(v1_panss_p6)+
                  as.numeric.factor(v1_panss_p7)

summary(v1_panss_sum_pos)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    8.50   10.81   13.00   35.00     117

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v1_panss_n1)

v1_panss_n1<-c(v1_clin$v1_panss_n_n1_affektverflachung,v1_con$v1_panss_n_n1_affektverflachung)
v1_panss_n1<-factor(v1_panss_n1, ordered=T)

descT(v1_panss_n1)
##                1    2    3    4   5   6   7   <NA>     
## [1,] No. cases 781  174  205  148 111 18  1   105  1543
## [2,] Percent   50.6 11.3 13.3 9.6 7.2 1.2 0.1 6.8  100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v1_panss_n2)

v1_panss_n2<-c(v1_clin$v1_panss_n_n2_emot_rueckzug,v1_con$v1_panss_n_n2_emot_rueckzug)
v1_panss_n2<-factor(v1_panss_n2, ordered=T)

descT(v1_panss_n2)
##                1    2    3    4    5   6   7   <NA>     
## [1,] No. cases 866  165  188  172  34  10  1   107  1543
## [2,] Percent   56.1 10.7 12.2 11.1 2.2 0.6 0.1 6.9  100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v1_panss_n3)

v1_panss_n3<-c(v1_clin$v1_panss_n_n3_mang_aff_rapp,v1_con$v1_panss_n_n3_mang_aff_rapp)
v1_panss_n3<-factor(v1_panss_n3, ordered=T)

descT(v1_panss_n3)
##                1    2    3    4   5   6   <NA>     
## [1,] No. cases 973  165  191  78  28  4   104  1543
## [2,] Percent   63.1 10.7 12.4 5.1 1.8 0.3 6.7  100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v1_panss_n4)

v1_panss_n4<-c(v1_clin$v1_panss_n_n4_soz_pass_apath,v1_con$v1_panss_n_n4_soz_pass_apath)
v1_panss_n4<-factor(v1_panss_n4, ordered=T)

descT(v1_panss_n4)
##                1    2   3    4   5   6   <NA>     
## [1,] No. cases 881  141 252  109 45  9   106  1543
## [2,] Percent   57.1 9.1 16.3 7.1 2.9 0.6 6.9  100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v1_panss_n5)

v1_panss_n5<-c(v1_clin$v1_panss_n_n5_abstr_denken,v1_con$v1_panss_n_n5_abstr_denken)
v1_panss_n5<-factor(v1_panss_n5, ordered=T)

descT(v1_panss_n5)
##                1    2    3    4   5   6  <NA>     
## [1,] No. cases 875  160  244  95  36  16 117  1543
## [2,] Percent   56.7 10.4 15.8 6.2 2.3 1  7.6  100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v1_panss_n6)

v1_panss_n6<-c(v1_clin$v1_panss_n_n6_spon_fl_sprache,v1_con$v1_panss_n_n6_spon_fl_sprache)
v1_panss_n6<-factor(v1_panss_n6, ordered=T)

descT(v1_panss_n6)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1060 136 132 72  33  3   107  1543
## [2,] Percent   68.7 8.8 8.6 4.7 2.1 0.2 6.9  100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v1_panss_n7)

v1_panss_n7<-c(v1_clin$v1_panss_n_n7_stereotyp_ged,v1_con$v1_panss_n_n7_stereotyp_ged)
v1_panss_n7<-factor(v1_panss_n7, ordered=T)

descT(v1_panss_n7)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1096 138 144 42  11  5   107  1543
## [2,] Percent   71   8.9 9.3 2.7 0.7 0.3 6.9  100

PANSS Negative sum score (continuous [7-49], v1_panss_sum_neg)

v1_panss_sum_neg<-as.numeric.factor(v1_panss_n1)+
                  as.numeric.factor(v1_panss_n2)+
                  as.numeric.factor(v1_panss_n3)+
                  as.numeric.factor(v1_panss_n4)+
                  as.numeric.factor(v1_panss_n5)+
                  as.numeric.factor(v1_panss_n6)+
                  as.numeric.factor(v1_panss_n7)

summary(v1_panss_sum_neg)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00   10.00   12.22   16.00   38.00     136

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v1_panss_g1)

v1_panss_g1<-c(v1_clin$v1_panss_g_g1_sorge_gesundh,v1_con$v1_panss_g_g1_sorge_gesundh)
v1_panss_g1<-factor(v1_panss_g1, ordered=T)

descT(v1_panss_g1)
##                1    2    3    4   5   6   7   <NA>     
## [1,] No. cases 954  176  177  83  34  9   3   107  1543
## [2,] Percent   61.8 11.4 11.5 5.4 2.2 0.6 0.2 6.9  100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v1_panss_g2)

v1_panss_g2<-c(v1_clin$v1_panss_g_g2_angst,v1_con$v1_panss_g_g2_angst)
v1_panss_g2<-factor(v1_panss_g2, ordered=T)

descT(v1_panss_g2)
##                1    2   3    4   5   6   7   <NA>     
## [1,] No. cases 828  149 283  123 45  6   1   108  1543
## [2,] Percent   53.7 9.7 18.3 8   2.9 0.4 0.1 7    100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v1_panss_g3)

v1_panss_g3<-c(v1_clin$v1_panss_g_g3_schuldgefuehle,v1_con$v1_panss_g_g3_schuldgefuehle)
v1_panss_g3<-factor(v1_panss_g3, ordered=T)

descT(v1_panss_g3)
##                1    2    3    4   5   6   7   <NA>     
## [1,] No. cases 960  156  176  111 29  5   1   105  1543
## [2,] Percent   62.2 10.1 11.4 7.2 1.9 0.3 0.1 6.8  100

G4 Tension (ordinal [1,2,3,4,5,6,7], v1_panss_g4)

v1_panss_g4<-c(v1_clin$v1_panss_g_g4_anspannung,v1_con$v1_panss_g_g4_anspannung)
v1_panss_g4<-factor(v1_panss_g4, ordered=T)

descT(v1_panss_g4)
##                1    2    3    4   5   6   <NA>     
## [1,] No. cases 815  213  266  115 22  6   106  1543
## [2,] Percent   52.8 13.8 17.2 7.5 1.4 0.4 6.9  100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v1_panss_g5)

v1_panss_g5<-c(v1_clin$v1_panss_g_g5_manier_koerperh,v1_con$v1_panss_g_g5_manier_koerperh)
v1_panss_g5<-factor(v1_panss_g5, ordered=T)

descT(v1_panss_g5)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 1221 111 71  20  6   8   1   105  1543
## [2,] Percent   79.1 7.2 4.6 1.3 0.4 0.5 0.1 6.8  100

G6 Depression (ordinal [1,2,3,4,5,6,7], v1_panss_g6)

v1_panss_g6<-c(v1_clin$v1_panss_g_g6_depression,v1_con$v1_panss_g_g6_depression)
v1_panss_g6<-factor(v1_panss_g6, ordered=T)

descT(v1_panss_g6)
##                1    2   3    4   5   6  7   <NA>     
## [1,] No. cases 722  154 251  185 106 16 4   105  1543
## [2,] Percent   46.8 10  16.3 12  6.9 1  0.3 6.8  100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v1_panss_g7)

v1_panss_g7<-c(v1_clin$v1_panss_g_g7_mot_verlangs,v1_con$v1_panss_g_g7_mot_verlangs)
v1_panss_g7<-factor(v1_panss_g7, ordered=T)

descT(v1_panss_g7)
##                1    2    3    4   5   6   <NA>     
## [1,] No. cases 896  175  237  102 23  5   105  1543
## [2,] Percent   58.1 11.3 15.4 6.6 1.5 0.3 6.8  100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v1_panss_g8)

v1_panss_g8<-c(v1_clin$v1_panss_g_g8_unkoop_verh,v1_con$v1_panss_g_g8_unkoop_verh)
v1_panss_g8<-factor(v1_panss_g8, ordered=T)

descT(v1_panss_g8)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1296 75  50  12  3   2   105  1543
## [2,] Percent   84   4.9 3.2 0.8 0.2 0.1 6.8  100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v1_panss_g9)

v1_panss_g9<-c(v1_clin$v1_panss_g_g9_ungew_denkinh,v1_con$v1_panss_g_g9_ungew_denkinh)
v1_panss_g9<-factor(v1_panss_g9, ordered=T)

descT(v1_panss_g9)
##                1    2   3    4   5   6   7   <NA>     
## [1,] No. cases 1056 91  163  74  44  12  1   102  1543
## [2,] Percent   68.4 5.9 10.6 4.8 2.9 0.8 0.1 6.6  100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v1_panss_g10)

v1_panss_g10<-c(v1_clin$v1_panss_g_g10_desorient,v1_con$v1_panss_g_g10_desorient)
v1_panss_g10<-factor(v1_panss_g10, ordered=T)

descT(v1_panss_g10)
##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 1269 99  61 4   4   2   104  1543
## [2,] Percent   82.2 6.4 4  0.3 0.3 0.1 6.7  100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v1_panss_g11)

v1_panss_g11<-c(v1_clin$v1_panss_g_g11_mang_aufmerks,v1_con$v1_panss_g_g11_mang_aufmerks)
v1_panss_g11<-factor(v1_panss_g11, ordered=T)

descT(v1_panss_g11)
##                1    2    3    4   5   6   <NA>     
## [1,] No. cases 775  172  332  125 27  3   109  1543
## [2,] Percent   50.2 11.1 21.5 8.1 1.7 0.2 7.1  100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v1_panss_g12)

v1_panss_g12<-c(v1_clin$v1_panss_g_g12_mang_urt_einsi,v1_con$v1_panss_g_g12_mang_urt_einsi)
v1_panss_g12<-factor(v1_panss_g12, ordered=T)

descT(v1_panss_g12)
##                1    2   3   4   5  6   7   <NA>     
## [1,] No. cases 1116 132 101 66  15 7   1   105  1543
## [2,] Percent   72.3 8.6 6.5 4.3 1  0.5 0.1 6.8  100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v1_panss_g13)

v1_panss_g13<-c(v1_clin$v1_panss_g_g13_willensschwae,v1_con$v1_panss_g_g13_willensschwae)
v1_panss_g13<-factor(v1_panss_g13, ordered=T)

descT(v1_panss_g13)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 1173 111 112 37  2   108  1543
## [2,] Percent   76   7.2 7.3 2.4 0.1 7    100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v1_panss_g14)

v1_panss_g14<-c(v1_clin$v1_panss_g_g14_mang_impulsk,v1_con$v1_panss_g_g14_mang_impulsk)
v1_panss_g14<-factor(v1_panss_g14, ordered=T)

descT(v1_panss_g14)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1173 102 136 20  1   2   109  1543
## [2,] Percent   76   6.6 8.8 1.3 0.1 0.1 7.1  100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v1_panss_g15)

v1_panss_g15<-c(v1_clin$v1_panss_g_g15_selbstbezog,v1_con$v1_panss_g_g15_selbstbezog)
v1_panss_g15<-factor(v1_panss_g15, ordered=T)

descT(v1_panss_g15)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 1180 110 100 40  8   1   104  1543
## [2,] Percent   76.5 7.1 6.5 2.6 0.5 0.1 6.7  100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v1_panss_g16)

v1_panss_g16<-c(v1_clin$v1_panss_g_g16_aktsoz_vermeid,v1_con$v1_panss_g_g16_aktsoz_vermeid)
v1_panss_g16<-factor(v1_panss_g16, ordered=T)

descT(v1_panss_g16)
##                1    2   3    4   5   6   <NA>     
## [1,] No. cases 991  141 203  65  30  6   107  1543
## [2,] Percent   64.2 9.1 13.2 4.2 1.9 0.4 6.9  100

PANSS General Psychopathology sum score (continuous [16-112], v1_panss_sum_gen)

v1_panss_sum_gen<-as.numeric.factor(v1_panss_g1)+
                  as.numeric.factor(v1_panss_g2)+
                  as.numeric.factor(v1_panss_g3)+
                  as.numeric.factor(v1_panss_g4)+
                  as.numeric.factor(v1_panss_g5)+
                  as.numeric.factor(v1_panss_g6)+
                  as.numeric.factor(v1_panss_g7)+
                  as.numeric.factor(v1_panss_g8)+
                  as.numeric.factor(v1_panss_g9)+
                  as.numeric.factor(v1_panss_g10)+
                  as.numeric.factor(v1_panss_g11)+
                  as.numeric.factor(v1_panss_g12)+
                  as.numeric.factor(v1_panss_g13)+
                  as.numeric.factor(v1_panss_g14)+
                  as.numeric.factor(v1_panss_g15)+
                  as.numeric.factor(v1_panss_g16)

summary(v1_panss_sum_gen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   18.00   23.00   25.31   31.00   74.00     147

Create PANSS Total score (continuous [30-210], v1_panss_sum_tot)

v1_panss_sum_tot<-v1_panss_sum_pos+v1_panss_sum_neg+v1_panss_sum_gen
summary(v1_panss_sum_tot)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   33.00   44.00   48.39   58.00  141.00     188

Create dataset

v1_symp_panss<-data.frame(v1_panss_p1,v1_panss_p2,v1_panss_p3,v1_panss_p4,v1_panss_p5,v1_panss_p6,v1_panss_p7,
                          v1_panss_n1,v1_panss_n2,v1_panss_n3,v1_panss_n4,v1_panss_n5,v1_panss_n6,v1_panss_n7,
                          v1_panss_g1,v1_panss_g2,v1_panss_g3,v1_panss_g4,v1_panss_g5,v1_panss_g6,v1_panss_g7,
                          v1_panss_g8,v1_panss_g9,v1_panss_g10,v1_panss_g11,v1_panss_g12,v1_panss_g13,v1_panss_g14,
                          v1_panss_g15,v1_panss_g16,v1_panss_sum_pos,v1_panss_sum_neg,v1_panss_sum_gen,
                          v1_panss_sum_tot)

Inventory of depressive symptomatology (IDS-C30)

The IDS-C30 is is a 30-item rating scale used to assess the severity of depressive symptoms. Each item is rated on an ordinal scale from zero to three with zero indicating absence of the respective symptom. One item, #9, has additional information. The ratings refer to the past seven days. On all items, higher scores indicate more severe symptoms. Please find the items below.

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v1_idsc_itm1)

v1_idsc_itm1<-c(v1_clin$v1_ids_c_s1_ids1_einschlafschw,v1_con$v1_ids_c_s1_ids1_einschlafschw)
v1_idsc_itm1<-factor(v1_idsc_itm1, ordered=T)

descT(v1_idsc_itm1)
##                0    1    2   3   <NA>     
## [1,] No. cases 996  218  111 102 116  1543
## [2,] Percent   64.5 14.1 7.2 6.6 7.5  100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v1_idsc_itm2)

v1_idsc_itm2<-c(v1_clin$v1_ids_c_s1_ids2_naechtl_aufw,v1_con$v1_ids_c_s1_ids2_naechtl_aufw)
v1_idsc_itm2<-factor(v1_idsc_itm2, ordered=T)

descT(v1_idsc_itm2)
##                0   1    2    3   <NA>     
## [1,] No. cases 880 224  173  148 118  1543
## [2,] Percent   57  14.5 11.2 9.6 7.6  100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v1_idsc_itm3)

v1_idsc_itm3<-c(v1_clin$v1_ids_c_s1_ids3_frueh_aufw,v1_con$v1_ids_c_s1_ids3_frueh_aufw)
v1_idsc_itm3<-factor(v1_idsc_itm3, ordered=T)

descT(v1_idsc_itm3)
##                0    1   2   3  <NA>     
## [1,] No. cases 1133 101 97  92 120  1543
## [2,] Percent   73.4 6.5 6.3 6  7.8  100

Item 4 Hypersomnia (ordinal [0,1,2,3], v1_idsc_itm4)

v1_idsc_itm4<-c(v1_clin$v1_ids_c_s1_ids4_hypersomnie,v1_con$v1_ids_c_s1_ids4_hypersomnie)
v1_idsc_itm4<-factor(v1_idsc_itm4, ordered=T)

descT(v1_idsc_itm4)
##                0    1    2   3   <NA>     
## [1,] No. cases 947  305  138 28  125  1543
## [2,] Percent   61.4 19.8 8.9 1.8 8.1  100

Item 5 Mood (sad) (ordinal [0,1,2,3], v1_idsc_itm5)

v1_idsc_itm5<-c(v1_clin$v1_ids_c_s1_ids5_stimmung_trgk,v1_con$v1_ids_c_s1_ids5_stimmung_trgk)
v1_idsc_itm5<-factor(v1_idsc_itm5, ordered=T)

descT(v1_idsc_itm5)
##                0    1    2   3   <NA>     
## [1,] No. cases 807  397  133 84  122  1543
## [2,] Percent   52.3 25.7 8.6 5.4 7.9  100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v1_idsc_itm6)

v1_idsc_itm6<-c(v1_clin$v1_ids_c_s1_ids6_stimmung_grzt,v1_con$v1_ids_c_s1_ids6_stimmung_grzt)
v1_idsc_itm6<-factor(v1_idsc_itm6, ordered=T)

descT(v1_idsc_itm6)
##                0    1    2   3   <NA>     
## [1,] No. cases 1042 292  55  35  119  1543
## [2,] Percent   67.5 18.9 3.6 2.3 7.7  100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v1_idsc_itm7)

v1_idsc_itm7<-c(v1_clin$v1_ids_c_s1_ids7_stimmung_agst,v1_con$v1_ids_c_s1_ids7_stimmung_agst)
v1_idsc_itm7<-factor(v1_idsc_itm7, ordered=T)

descT(v1_idsc_itm7)
##                0    1   2   3   <NA>     
## [1,] No. cases 909  308 131 72  123  1543
## [2,] Percent   58.9 20  8.5 4.7 8    100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v1_idsc_itm8)

v1_idsc_itm8<-c(v1_clin$v1_ids_c_s1_ids8_reakt_stimmung,v1_con$v1_ids_c_s1_ids8_reakt_stimmung)
v1_idsc_itm8<-factor(v1_idsc_itm8, ordered=T)

descT(v1_idsc_itm8)
##                0    1    2   3   <NA>     
## [1,] No. cases 1126 181  74  42  120  1543
## [2,] Percent   73   11.7 4.8 2.7 7.8  100

Item 9 Mood Variation (ordinal [0,1,2,3], v1_idsc_itm9)

v1_idsc_itm9<-c(v1_clin$v1_ids_c_s1_ids9_stimmungsschw,v1_con$v1_ids_c_s1_ids9_stimmungsschw)
v1_idsc_itm9<-factor(v1_idsc_itm9, ordered=T)

descT(v1_idsc_itm9)
##                0    1   2   3   <NA>     
## [1,] No. cases 1057 128 82  149 127  1543
## [2,] Percent   68.5 8.3 5.3 9.7 8.2  100

Item 9A (categorical [M, A, N], v1_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v1_idsc_itm9a_pre<-c(v1_clin$v1_ids_c_s1_ids9a_stimmungsschw,v1_con$v1_ids_c_s1_ids9a_stimmungsschw)

v1_idsc_itm9a<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==1, "M", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==2, "A", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9a_pre==3, "N", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9a))
v1_idsc_itm9a<-factor(v1_idsc_itm9a, ordered=F)

descT(v1_idsc_itm9a)
##                -999 A  M    N   <NA>     
## [1,] No. cases 1057 15 160  64  247  1543
## [2,] Percent   68.5 1  10.4 4.1 16   100

Item 9B (dichotomous, v1_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v1_idsc_itm9b_pre<-c(v1_clin$v1_ids_c_s1_ids9b_stimmungsschw,v1_con$v1_ids_c_s1_ids9b_stimmungsschw)

v1_idsc_itm9b<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm9b<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9b_pre==0, "N", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9b))
v1_idsc_itm9b<-ifelse(v1_idsc_itm9!=0 & v1_idsc_itm9b_pre==1, "Y", ifelse(v1_idsc_itm9==0, -999, v1_idsc_itm9b))
v1_idsc_itm9b<-factor(v1_idsc_itm9b, ordered=F)

descT(v1_idsc_itm9b)
##                -999 N   Y   <NA>     
## [1,] No. cases 1057 107 84  295  1543
## [2,] Percent   68.5 6.9 5.4 19.1 100

Item 10 Quality of mood (ordinal [0,1,2,3], v1_idsc_itm10)

v1_idsc_itm10<-c(v1_clin$v1_ids_c_s1_ids10_quali_stimmung,v1_con$v1_ids_c_s1_ids10_quali_stimmung)
v1_idsc_itm10<-factor(v1_idsc_itm10, ordered=T)

descT(v1_idsc_itm10)
##                0    1   2  3   <NA>     
## [1,] No. cases 1108 103 61 135 136  1543
## [2,] Percent   71.8 6.7 4  8.7 8.8  100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v1_idsc_itm11)

v1_idsc_app_verm<-c(v1_clin$v1_ids_c_s2_ids11_appetit_verm,v1_con$v1_ids_c_s2_ids11_appetit_verm)
v1_idsc_app_gest<-c(v1_clin$v1_ids_c_s2_ids12_appetit_steig,v1_con$v1_ids_c_s2_ids12_appetit_steig)

v1_idsc_itm11<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm11<-ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==T,          
                         v1_idsc_app_verm, 
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_verm>v1_idsc_app_gest), v1_idsc_app_verm,
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_gest>=v1_idsc_app_verm),-999,v1_idsc_itm11)))))                                                   

#Important: do not code as factor, see after calculation of sum score!
descT(v1_idsc_itm11)
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 416  838  133 24  11  121  1543
## [2,] Percent   27   54.3 8.6 1.6 0.7 7.8  100

Item 12 (ordinal [0,1,2,3], v1_idsc_itm12)

v1_idsc_itm12<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm12<-ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v1_idsc_app_verm)==T & is.na(v1_idsc_app_gest)==F,    
                         v1_idsc_app_gest,                
                  ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==T,          
                         -999, 
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_verm>v1_idsc_app_gest), -999,   
                     ifelse(is.na(v1_idsc_app_verm)==F & is.na(v1_idsc_app_gest)==F & (v1_idsc_app_gest>=v1_idsc_app_verm), v1_idsc_app_gest,v1_idsc_itm12)))))                                                       
#Important: do not code as factor, see after calculation of sum score!

descT(v1_idsc_itm12)
##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1006 132 174  66  44  121  1543
## [2,] Percent   65.2 8.6 11.3 4.3 2.9 7.8  100

Item 13 (ordinal [0,1,2,3], v1_idsc_itm13)

v1_idsc_gew_abn<-c(v1_clin$v1_ids_c_s2_ids13_gewichtsabn,v1_con$v1_ids_c_s2_ids13_gewichtsabn)
v1_idsc_gew_zun<-c(v1_clin$v1_ids_c_s2_ids14_gewichtszun,v1_con$v1_ids_c_s2_ids14_gewichtszun)

v1_idsc_itm13<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm13<-ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==T, v1_idsc_gew_abn,          
                      ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_abn>v1_idsc_gew_zun), v1_idsc_gew_abn,                                                     ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_zun >= v1_idsc_gew_abn),-999,v1_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v1_idsc_itm13)
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 447  791  70  70  36  129  1543
## [2,] Percent   29   51.3 4.5 4.5 2.3 8.4  100

Item 14 (ordinal [0,1,2,3], v1_idsc_itm14)

v1_idsc_itm14<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_idsc_itm14<-ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v1_idsc_gew_abn)==T & is.na(v1_idsc_gew_zun)==F,    
                         v1_idsc_gew_zun,                
                  ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==T, -999, 
                    ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_abn>v1_idsc_gew_zun), -999,                                            
                    ifelse(is.na(v1_idsc_gew_abn)==F & is.na(v1_idsc_gew_zun)==F & (v1_idsc_gew_zun>=v1_idsc_gew_abn), v1_idsc_gew_zun,v1_idsc_itm14)))))                                                       
#Important: do not code as factor, see after calculation of sum score!

descT(v1_idsc_itm14)
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 967  185 107 88  67  129  1543
## [2,] Percent   62.7 12  6.9 5.7 4.3 8.4  100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v1_idsc_itm15)

v1_idsc_itm15<-c(v1_clin$v1_ids_c_s2_ids15_konz_entscheid,v1_con$v1_ids_c_s2_ids15_konz_entscheid)
v1_idsc_itm15<-factor(v1_idsc_itm15, ordered=T)

descT(v1_idsc_itm15)
##                0    1   2    3  <NA>     
## [1,] No. cases 755  339 261  62 126  1543
## [2,] Percent   48.9 22  16.9 4  8.2  100

Item 16 Outlook (self) (ordinal [0,1,2,3], v1_idsc_itm16)

v1_idsc_itm16<-c(v1_clin$v1_ids_c_s2_ids16_selbstbild,v1_con$v1_ids_c_s2_ids16_selbstbild)
v1_idsc_itm16<-factor(v1_idsc_itm16, ordered=T)

descT(v1_idsc_itm16)
##                0    1    2  3   <NA>     
## [1,] No. cases 1004 222  93 103 121  1543
## [2,] Percent   65.1 14.4 6  6.7 7.8  100

Item 17 Outlook (future) (ordinal [0,1,2,3], v1_idsc_itm17)

v1_idsc_itm17<-c(v1_clin$v1_ids_c_s2_ids17_zukunftssicht,v1_con$v1_ids_c_s2_ids17_zukunftssicht)
v1_idsc_itm17<-factor(v1_idsc_itm17, ordered=T)

descT(v1_idsc_itm17)
##                0    1    2   3   <NA>     
## [1,] No. cases 877  373  142 27  124  1543
## [2,] Percent   56.8 24.2 9.2 1.7 8    100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v1_idsc_itm18)

v1_idsc_itm18<-c(v1_clin$v1_ids_c_s2_ids18_selbstmordged,v1_con$v1_ids_c_s2_ids18_selbstmordged)
v1_idsc_itm18<-factor(v1_idsc_itm18, ordered=T)

descT(v1_idsc_itm18)
##                0    1   2  3   <NA>     
## [1,] No. cases 1271 85  61 9   117  1543
## [2,] Percent   82.4 5.5 4  0.6 7.6  100

Item 19 Involvement (ordinal [0,1,2,3], v1_idsc_itm19)

v1_idsc_itm19<-c(v1_clin$v1_ids_c_s2_ids19_interess_aktiv,v1_con$v1_ids_c_s2_ids19_interess_aktiv)
v1_idsc_itm19<-factor(v1_idsc_itm19, ordered=T)

descT(v1_idsc_itm19)
##                0    1    2   3   <NA>     
## [1,] No. cases 1075 257  50  41  120  1543
## [2,] Percent   69.7 16.7 3.2 2.7 7.8  100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v1_idsc_itm20)

v1_idsc_itm20<-c(v1_clin$v1_ids_c_s2_ids20_energ_ermued,v1_con$v1_ids_c_s2_ids20_energ_ermued)
v1_idsc_itm20<-factor(v1_idsc_itm20, ordered=T)

descT(v1_idsc_itm20)
##                0    1    2   3   <NA>     
## [1,] No. cases 856  359  170 40  118  1543
## [2,] Percent   55.5 23.3 11  2.6 7.6  100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v1_idsc_itm21)

v1_idsc_itm21<-c(v1_clin$v1_ids_c_s3_ids21_vergn_genuss,v1_con$v1_ids_c_s3_ids21_vergn_genuss)
v1_idsc_itm21<-factor(v1_idsc_itm21, ordered=T)

descT(v1_idsc_itm21)
##                0    1    2   3   <NA>     
## [1,] No. cases 1106 224  68  22  123  1543
## [2,] Percent   71.7 14.5 4.4 1.4 8    100

Item 22 Sexual interest (ordinal [0,1,2,3], v1_idsc_itm22)

v1_idsc_itm22<-c(v1_clin$v1_ids_c_s3_ids22_sex_interesse,v1_con$v1_ids_c_s3_ids22_sex_interesse)
v1_idsc_itm22<-factor(v1_idsc_itm22, ordered=T)

descT(v1_idsc_itm22)
##                0   1   2    3   <NA>     
## [1,] No. cases 987 99  189  140 128  1543
## [2,] Percent   64  6.4 12.2 9.1 8.3  100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v1_idsc_itm23)

v1_idsc_itm23<-c(v1_clin$v1_ids_c_s3_ids23_psymo_hemm,v1_con$v1_ids_c_s3_ids23_psymo_hemm)
v1_idsc_itm23<-factor(v1_idsc_itm23, ordered=T)

descT(v1_idsc_itm23)
##                0    1    2  3   <NA>     
## [1,] No. cases 1076 273  62 8   124  1543
## [2,] Percent   69.7 17.7 4  0.5 8    100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v1_idsc_itm24)

v1_idsc_itm24<-c(v1_clin$v1_ids_c_s3_ids24_psymo_agitht,v1_con$v1_ids_c_s3_ids24_psymo_agitht)
v1_idsc_itm24<-factor(v1_idsc_itm24, ordered=T)

descT(v1_idsc_itm24)
##                0    1    2   3  <NA>     
## [1,] No. cases 1123 199  79  16 126  1543
## [2,] Percent   72.8 12.9 5.1 1  8.2  100

Item 25 Somatic complaints (ordinal [0,1,2,3], v1_idsc_itm25)

v1_idsc_itm25<-c(v1_clin$v1_ids_c_s3_ids25_som_beschw,v1_con$v1_ids_c_s3_ids25_som_beschw)
v1_idsc_itm25<-factor(v1_idsc_itm25, ordered=T)

descT(v1_idsc_itm25)
##                0    1    2   3   <NA>     
## [1,] No. cases 989  327  72  27  128  1543
## [2,] Percent   64.1 21.2 4.7 1.7 8.3  100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v1_idsc_itm26)

v1_idsc_itm26<-c(v1_clin$v1_ids_c_s3_ids26_veg_erreg,v1_con$v1_ids_c_s3_ids26_veg_erreg)
v1_idsc_itm26<-factor(v1_idsc_itm26, ordered=T)

descT(v1_idsc_itm26)
##                0    1    2   3   <NA>     
## [1,] No. cases 1025 297  79  20  122  1543
## [2,] Percent   66.4 19.2 5.1 1.3 7.9  100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v1_idsc_itm27)

v1_idsc_itm27<-c(v1_clin$v1_ids_c_s3_ids27_panik_phob,v1_con$v1_ids_c_s3_ids27_panik_phob)
v1_idsc_itm27<-factor(v1_idsc_itm27, ordered=T)

descT(v1_idsc_itm27)
##                0    1   2  3   <NA>     
## [1,] No. cases 1210 142 46 23  122  1543
## [2,] Percent   78.4 9.2 3  1.5 7.9  100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v1_idsc_itm28)

v1_idsc_itm28<-c(v1_clin$v1_ids_c_s3_ids28_verdauung,v1_con$v1_ids_c_s3_ids28_verdauung)
v1_idsc_itm28<-factor(v1_idsc_itm28, ordered=T)

descT(v1_idsc_itm28)
##                0    1   2   3  <NA>     
## [1,] No. cases 1178 143 68  31 123  1543
## [2,] Percent   76.3 9.3 4.4 2  8    100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v1_idsc_itm29)

v1_idsc_itm29<-c(v1_clin$v1_ids_c_s3_ids29_pers_bezieh,v1_con$v1_ids_c_s3_ids29_pers_bezieh)
v1_idsc_itm29<-factor(v1_idsc_itm29, ordered=T)

descT(v1_idsc_itm29)
##                0    1    2   3  <NA>     
## [1,] No. cases 1119 199  73  31 121  1543
## [2,] Percent   72.5 12.9 4.7 2  7.8  100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v1_idsc_itm30)

v1_idsc_itm30<-c(v1_clin$v1_ids_c_s3_ids30_schwgf_k_energ,v1_con$v1_ids_c_s3_ids30_schwgf_k_energ)
v1_idsc_itm30<-factor(v1_idsc_itm30, ordered=T)

descT(v1_idsc_itm30)
##                0    1    2   3  <NA>     
## [1,] No. cases 1104 202  78  31 128  1543
## [2,] Percent   71.5 13.1 5.1 2  8.3  100

Create IDS-C30 total score (continuous [0-84], v1_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v1_idsc_sum<-as.numeric.factor(v1_idsc_itm1)+
             as.numeric.factor(v1_idsc_itm2)+
             as.numeric.factor(v1_idsc_itm3)+
             as.numeric.factor(v1_idsc_itm4)+
             as.numeric.factor(v1_idsc_itm5)+
             as.numeric.factor(v1_idsc_itm6)+
             as.numeric.factor(v1_idsc_itm7)+
             as.numeric.factor(v1_idsc_itm8)+
             as.numeric.factor(v1_idsc_itm9)+
             as.numeric.factor(v1_idsc_itm10)+
  
 ifelse(is.na(v1_idsc_itm11)==T & is.na(v1_idsc_itm12)==T, NA, 
        ifelse((v1_idsc_itm11==-999 & v1_idsc_itm12!=-999), v1_idsc_itm12,                
        ifelse((v1_idsc_itm11!=-999 & v1_idsc_itm12==-999),v1_idsc_itm11, NA)))+
  
   ifelse(is.na(v1_idsc_itm13)==T & is.na(v1_idsc_itm14)==T, NA, 
        ifelse((v1_idsc_itm13==-999 & v1_idsc_itm14!=-999), v1_idsc_itm14,                
        ifelse((v1_idsc_itm13!=-999 & v1_idsc_itm14==-999),v1_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v1_idsc_itm15)+
             as.numeric.factor(v1_idsc_itm16)+
             as.numeric.factor(v1_idsc_itm17)+
             as.numeric.factor(v1_idsc_itm18)+
             as.numeric.factor(v1_idsc_itm19)+
             as.numeric.factor(v1_idsc_itm20)+
             as.numeric.factor(v1_idsc_itm21)+
             as.numeric.factor(v1_idsc_itm22)+
             as.numeric.factor(v1_idsc_itm23)+
             as.numeric.factor(v1_idsc_itm24)+
             as.numeric.factor(v1_idsc_itm25)+
             as.numeric.factor(v1_idsc_itm26)+
             as.numeric.factor(v1_idsc_itm27)+
             as.numeric.factor(v1_idsc_itm28)+
             as.numeric.factor(v1_idsc_itm29)+
             as.numeric.factor(v1_idsc_itm30)

summary(v1_idsc_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    3.00    8.50   12.12   18.00   63.00     263

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v1_idsc_itm11<-factor(v1_idsc_itm11,ordered=T)
v1_idsc_itm12<-factor(v1_idsc_itm12,ordered=T)
v1_idsc_itm13<-factor(v1_idsc_itm13,ordered=T)
v1_idsc_itm14<-factor(v1_idsc_itm14,ordered=T)

Create dataset

v1_symp_ids_c<-data.frame(v1_idsc_itm1,v1_idsc_itm2,v1_idsc_itm3,v1_idsc_itm4,v1_idsc_itm5,v1_idsc_itm6,v1_idsc_itm7,
                          v1_idsc_itm8,v1_idsc_itm9,v1_idsc_itm9a,v1_idsc_itm9b,v1_idsc_itm10,v1_idsc_itm11,v1_idsc_itm12,
                          v1_idsc_itm13,v1_idsc_itm14,v1_idsc_itm15,v1_idsc_itm16,v1_idsc_itm17,v1_idsc_itm18,v1_idsc_itm19,
                          v1_idsc_itm20,v1_idsc_itm21,v1_idsc_itm22,v1_idsc_itm23,v1_idsc_itm24,v1_idsc_itm25,v1_idsc_itm26,
                          v1_idsc_itm27,v1_idsc_itm28,v1_idsc_itm29,v1_idsc_itm30,v1_idsc_sum)

Young Mania Rating Scale (YMRS)

The YMRS (R. C. Young, Biggs, Ziegler, & Meyer, 1978) is an 11-item rating scale used to assess the severity of mania symptoms. Each item is rated on an ordinal scale, either from zero to four or from zero to eight with zero indicating absence of the respective symptom. The ratings refer to the past fourty-eight hours. On all items, higher scores mean more severe symptoms. Please find the items below.

Item 1 Elevated mood (ordinal [0,1,2,3,4], v1_ymrs_itm1)

v1_ymrs_itm1<-c(v1_clin$v1_ymrs_ymrs1_gehob_stimm,v1_con$v1_ymrs_ymrs1_gehob_stimm)
v1_ymrs_itm1<-factor(v1_ymrs_itm1, ordered=T)

descT(v1_ymrs_itm1)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 1164 155 83  22  5   114  1543
## [2,] Percent   75.4 10  5.4 1.4 0.3 7.4  100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v1_ymrs_itm2)

v1_ymrs_itm2<-c(v1_clin$v1_ymrs_ymrs2_gest_aktiv,v1_con$v1_ymrs_ymrs2_gest_aktiv)
v1_ymrs_itm2<-factor(v1_ymrs_itm2, ordered=T)

descT(v1_ymrs_itm2)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 1195 133 67  28  3   117  1543
## [2,] Percent   77.4 8.6 4.3 1.8 0.2 7.6  100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v1_ymrs_itm3)

v1_ymrs_itm3<-c(v1_clin$v1_ymrs_ymrs3_sex_interesse,v1_con$v1_ymrs_ymrs3_sex_interesse)
v1_ymrs_itm3<-factor(v1_ymrs_itm3, ordered=T)

descT(v1_ymrs_itm3)
##                0    1   2   3   <NA>     
## [1,] No. cases 1324 60  30  10  119  1543
## [2,] Percent   85.8 3.9 1.9 0.6 7.7  100

Item 4 Sleep (ordinal [0,1,2,3,4], v1_ymrs_itm4)

v1_ymrs_itm4<-c(v1_clin$v1_ymrs_ymrs4_schlaf,v1_con$v1_ymrs_ymrs4_schlaf)
v1_ymrs_itm4<-factor(v1_ymrs_itm4, ordered=T)

descT(v1_ymrs_itm4)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 1263 81  41  41  2   115  1543
## [2,] Percent   81.9 5.2 2.7 2.7 0.1 7.5  100

Item 5 Irritability (ordinal [0,2,4,6,8], v1_ymrs_itm5)

v1_ymrs_itm5<-c(v1_clin$v1_ymrs_ymrs5_reizbarkeit,v1_con$v1_ymrs_ymrs5_reizbarkeit)
v1_ymrs_itm5<-factor(v1_ymrs_itm5, ordered=T)

descT(v1_ymrs_itm5)
##                0    2    4   6   <NA>     
## [1,] No. cases 1197 186  43  3   114  1543
## [2,] Percent   77.6 12.1 2.8 0.2 7.4  100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v1_ymrs_itm6)

v1_ymrs_itm6<-c(v1_clin$v1_ymrs_ymrs6_sprechweise,v1_con$v1_ymrs_ymrs6_sprechweise)
v1_ymrs_itm6<-factor(v1_ymrs_itm6, ordered=T)

descT(v1_ymrs_itm6)
##                0    2   4   6   8   <NA>     
## [1,] No. cases 1199 106 82  39  3   114  1543
## [2,] Percent   77.7 6.9 5.3 2.5 0.2 7.4  100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v1_ymrs_itm7)

v1_ymrs_itm7<-c(v1_clin$v1_ymrs_ymrs7_sprachstoer,v1_con$v1_ymrs_ymrs7_sprachstoer)
v1_ymrs_itm7<-factor(v1_ymrs_itm7, ordered=T)

descT(v1_ymrs_itm7)
##                0    1    2  3   <NA>     
## [1,] No. cases 1198 158  61 9   117  1543
## [2,] Percent   77.6 10.2 4  0.6 7.6  100

Item 8 Content (ordinal [0,2,4,6,8], v1_ymrs_itm8)

v1_ymrs_itm8<-c(v1_clin$v1_ymrs_ymrs8_inhalte,v1_con$v1_ymrs_ymrs8_inhalte)
v1_ymrs_itm8<-factor(v1_ymrs_itm8, ordered=T)

descT(v1_ymrs_itm8)
##                0    2   4  6   8   <NA>     
## [1,] No. cases 1297 72  15 20  21  118  1543
## [2,] Percent   84.1 4.7 1  1.3 1.4 7.6  100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v1_ymrs_itm9)

v1_ymrs_itm9<-c(v1_clin$v1_ymrs_ymrs9_exp_aggr_verh,v1_con$v1_ymrs_ymrs9_exp_aggr_verh)
v1_ymrs_itm9<-factor(v1_ymrs_itm9, ordered=T)

descT(v1_ymrs_itm9)
##                0    2   4   <NA>     
## [1,] No. cases 1351 67  5   120  1543
## [2,] Percent   87.6 4.3 0.3 7.8  100

Item 10 Appearance (ordinal [0,1,2,3,4], v1_ymrs_itm10)

v1_ymrs_itm10<-c(v1_clin$v1_ymrs_ymrs10_erscheinung,v1_con$v1_ymrs_ymrs10_erscheinung)
v1_ymrs_itm10<-factor(v1_ymrs_itm10, ordered=T)

descT(v1_ymrs_itm10)
##                0    1   2   3   <NA>     
## [1,] No. cases 1274 129 21  3   116  1543
## [2,] Percent   82.6 8.4 1.4 0.2 7.5  100

Item 11 Insight (ordinal [0,1,2,3,4], v1_ymrs_itm11)

v1_ymrs_itm11<-c(v1_clin$v1_ymrs_ymrs11_krkh_einsicht,v1_con$v1_ymrs_ymrs11_krkh_einsicht)
v1_ymrs_itm11<-factor(v1_ymrs_itm11, ordered=T)

descT(v1_ymrs_itm11)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 1321 52  27  11  8   124  1543
## [2,] Percent   85.6 3.4 1.7 0.7 0.5 8    100

Create YMRS total score (continuous [0-60], v1_ymrs_sum)

v1_ymrs_sum<-(as.numeric.factor(v1_ymrs_itm1)+
        as.numeric.factor(v1_ymrs_itm2)+
        as.numeric.factor(v1_ymrs_itm3)+
        as.numeric.factor(v1_ymrs_itm4)+
        as.numeric.factor(v1_ymrs_itm5)+
        as.numeric.factor(v1_ymrs_itm6)+
        as.numeric.factor(v1_ymrs_itm7)+
        as.numeric.factor(v1_ymrs_itm8)+
        as.numeric.factor(v1_ymrs_itm9)+
        as.numeric.factor(v1_ymrs_itm10)+
        as.numeric.factor(v1_ymrs_itm11))

summary(v1_ymrs_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   2.705   3.000  39.000     149

Create dataset

v1_symp_ymrs<-data.frame(v1_ymrs_itm1,
                         v1_ymrs_itm2,
                         v1_ymrs_itm3,
                         v1_ymrs_itm4,
                         v1_ymrs_itm5,
                         v1_ymrs_itm6,
                         v1_ymrs_itm7,
                         v1_ymrs_itm8,
                         v1_ymrs_itm9,
                         v1_ymrs_itm10,
                         v1_ymrs_itm11,
                         v1_ymrs_sum)

Clinical Global Impression (CGI) - illness severity scale (ordinal [1,2,3,4,5,6,7], v1_cgi_s)

The CGI (see e.g. Busner & Targum, 2007) measures illness severity. The degree of impairment is to be quantified on a scale from zero to seven. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “normal, not at all ill”-1 to “extremely ill”-7. Please note that in all other study visits, the improvement scale (whether or not there was improvement compared to the last study visit) is also assessed. All control subjects also have -999 in this variable.

v1_cgi_s<-c(v1_clin$v1_cgi_cgi1_schweregrad,rep(-999,dim(v1_con)[1]))
v1_cgi_s[v1_cgi_s==0]<- -999
v1_cgi_s<-factor(v1_cgi_s, ordered=T)

descT(v1_cgi_s)
##                -999 1   2   3    4    5    6   7   <NA>     
## [1,] No. cases 322  17  53  285  351  419  68  5   23   1543
## [2,] Percent   20.9 1.1 3.4 18.5 22.7 27.2 4.4 0.3 1.5  100

Global Assessment of Functioning (GAF) (continuous [1-100], v1_gaf)

The GAF scale is a rating scale that is used to measure an individual’s psychosocial functioning. The GAF was initially developed by Luborsky (1962) as Health-Sickness Rating Scale, revised by Endicott et al. (1976) under the name GAS of which a modified version was included in the DSM-III-R and, with minimal changes, also in the DSM-IV as GAF scale (Axis V). The scale is continuous and ranges from one to 100. Values of zero indicate lack of information. Such values were therefore set to “-999”. The following rating instructions are given:

  • “No symptoms. Superior functioning in a wide range of activities, life’s problems never seem to get out of hand, is sought out by others because of his or her many positive qualities.”: 91-100

  • “Absent or minimal symptoms (e.g., mild anxiety before an exam), good functioning in all areas, interested and involved in a wide range of activities, socially effective, generally satisfied with life, no more than everyday problems or concerns.”: 81-90

  • “If symptoms are present, they are transient and expectable reactions to psychosocial stressors (e.g., difficulty concentrating after family argument); no more than slight impairment in social, occupational, or school functioning (e.g., temporarily falling behind in schoolwork).”: 71-80,

  • “Some mild symptoms (e.g., depressed mood and mild insomnia) or some difficulty in social, occupational, or school functioning (e.g., occasional truancy, or theft within the household), but generally functioning pretty well, has some meaningful interpersonal relationships.”: 61-70

  • “Moderate symptoms (e.g., flat affect and circumlocutory speech, occasional panic attacks) or moderate difficulty in social, occupational, or school functioning (e.g., few friends, conflicts with peers or co-workers)”: 51-60

  • “Serious symptoms (e.g., suicidal ideation, severe obsessional rituals, frequent shoplifting) or any serious impairment in social, occupational, or school functioning (e.g., no friends, unable to keep a job, cannot work).”: 41-50

  • “Some impairment in reality testing or communication (e.g., speech is at times illogical, obscure, or irrelevant) or major impairment in several areas, such as work or school, family relations, judgment, thinking, or mood (e.g., depressed adult avoids friends, neglects family, and is unable to work; child frequently beats up younger children, is defiant at home, and is failing at school).”: 31-40

  • “Behavior is considerably influenced by delusions or hallucinations or serious impairment, in communication or judgment (e.g., sometimes incoherent, acts grossly inappropriately, suicidal preoccupation) or inability to function in almost all areas (e.g., stays in bed all day, no job, home, or friends)”: 21-30

  • “Some danger of hurting self or others (e.g., suicide attempts without clear expectation of death; frequently violent; manic excitement) or occasionally fails to maintain minimal personal hygiene (e.g., smears feces) or gross impairment in communication (e.g., largely incoherent or mute).”: 11-20

  • “Persistent danger of severely hurting self or others (e.g., recurrent violence) or persistent inability to maintain minimal personal hygiene or serious suicidal act with clear expectation of death.”: 1-10

According to the Endicott et al. (1976), “[m]ost outpatients will be rated 31 to 70, and most inpatients between 1 and 40.”. The scale is continuous but, in the opinion of most experienced raters, rather has ordinal scale level.

v1_gaf<-c(v1_clin$v1_gaf_gaf_code,v1_con$v1_gaf_gaf_code)
v1_gaf[v1_gaf==0]<- -999

summary(v1_gaf[v1_gaf>0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   50.00   60.00   61.37   72.00   99.00     106

Boxplot of GAF scores of both CLINICAL and CONTROL study participants

boxplot(v1_gaf[v1_gaf>0 & v1_stat=="CLINICAL"], v1_gaf[v1_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v1_ill_sev<-data.frame(v1_cgi_s, v1_gaf)

Visit 1: Neuropsychology (cognitive tests)

During the first study visit, the following neuropsychological tests are completed by the participant: Trail-Making-Test (parts A and B), Digit-Symbol-Test (taken fron HAWIE-R), Verbal Digit-span (forward and backward; “Zahlennachsprechen”, also from HAWIE-R), Multiple-Choice Vocabulary Intelligence Test (MWT-B). All are paper-and-pencil tests and are briefly explained below.

Please note: We have now also included test results from only partially completed tests

General comments on the testing (character [free text], v1_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v1_nrpsy_lng)

v1_nrpsy_lng_pre<-c(v1_clin$v1_npu_np_sprach,v1_con$v1_npu_np_sprach)
v1_nrpsy_lng<-ifelse(v1_nrpsy_lng_pre==0, "mother tongue", 
                  ifelse(v1_nrpsy_lng_pre==1, "good",
                      ifelse(v1_nrpsy_lng_pre==2, "sufficient", 
                          ifelse(v1_nrpsy_lng_pre==3, "not sufficient", NA))))

v1_nrpsy_lng<-factor(v1_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v1_nrpsy_lng)
##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 1355          110  25         2              51   1543
## [2,] Percent   87.8          7.1  1.6        0.1            3.3  100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v1_nrpsy_mtv)

v1_nrpsy_mtv_pre<-c(v1_clin$v1_npu_np_mot,v1_con$v1_npu_np_mot)

v1_nrpsy_mtv<-ifelse(v1_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v1_nrpsy_mtv_pre==1, "average", 
                      ifelse(v1_nrpsy_mtv_pre==2, "good", NA)))

v1_nrpsy_mtv<-factor(v1_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))

descT(v1_nrpsy_mtv)
##                poor average good <NA>     
## [1,] No. cases 27   140     1306 70   1543
## [2,] Percent   1.7  9.1     84.6 4.5  100

Executive function: Trail-Making-Test (TMT)

Using a pen, the participant is required to connect digits in increasing order (“1-2-3-4…”; part A) or connect digits and symbols alternately (“1-A-2-B-3-C…”; part B). The time taken to complete each part of the test is measured. While part A assesses psychomotor speed of the participant, part B assesses switching between two automated tasks (counting and reciting the alphabet). The time taken to complete the A form may be subtracted from the time taken to complete the B form to arrive at an estimate of the switching process. This test measures multiple cognitive domains which are difficult to disentangle (e.g. visual search etc.), but is a good estimator of executive function. The errors the participant made are also recorded (during the test, the participant is required by the interviewer to correct errors immediately). However, these errors are usually not evaluated separately, as any error the participant makes is supposed to be reflected in the time taken to complete the test.

TMT Part A, time (continuous [seconds], v1_nrpsy_tmt_A_rt)

v1_nrpsy_tmt_A_rt<-c(v1_clin$v1_npu_tmt_001,v1_con$v1_npu_np_tmt_001)
summary(v1_nrpsy_tmt_A_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    9.00   24.00   32.00   35.48   42.00  180.00      71

TMT Part A, errors (continuous [number of errors], v1_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see above)

v1_nrpsy_tmt_A_err<-c(v1_clin$v1_npu_tmt_af_001,v1_con$v1_npu_np_tmtfehler_001)
summary(v1_nrpsy_tmt_A_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.1406  0.0000  5.0000      85

TMT Part B, time (continuous [seconds], v1_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v1_nrpsy_tmt_B_rt<-c(v1_clin$v1_npu_tmt_002,v1_con$v1_npu_np_tmt_002)
v1_nrpsy_tmt_B_rt[v1_nrpsy_tmt_B_rt>300]<-300

summary(v1_nrpsy_tmt_B_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   25.00   53.00   71.00   80.97   97.00  300.00     141

TMT Part B, errors (continuous [number of errors], v1_nrpsy_tmt_B_err) We did not impose any cut-off value to errors (see above)

v1_nrpsy_tmt_B_err<-c(v1_clin$v1_npu_tmt_af_002,v1_con$v1_npu_np_tmtfehler_002)
summary(v1_nrpsy_tmt_B_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.6007  1.0000 17.0000     148

Short-term and working memory: Verbal digit span

This test assesses short-term (forward digit-span) and working memory (backward digit-span). In the short-term memory task, the participant is asked to repeat strings of digits verbally presented by the interviewer. The initial length of the string is two items (“1-7”). If the participant is able to repeat two different strings of numbers of the same length (“1-7” and “6-3”, each assessed separately), the interviewer moves to a longer string, (“5-8-2”), which is also assessed two times separately (using different strings). For each correctly repeated string of digits, the subject receives one point. The test is repeated until the participant fails to repeat two presented strings of the same length. All points are added up in the end to receive the final score. The working memory task works exactly the same way, only that the subject has to repeat the string of digits presented by the interviewer in backward order. Briefly, the difference between short-term and working memory is that the latter involves mental manipulation.

Forward (continuous [number of items], v1_nrpsy_dgt_sp_frw)

v1_nrpsy_dgt_sp_frw<-c(v1_clin$v1_npu_zns_001,v1_con$v1_npu_np_wie_001)
summary(v1_nrpsy_dgt_sp_frw)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.000   8.000   9.000   9.382  11.000  16.000     112

Histogram

hist(v1_nrpsy_dgt_sp_frw,breaks=c(1:16), main="Digit-span forward", xlab="Score",ylab="Number of Individuals")

Backward (continuous [number of items], v1_nrpsy_dgt_sp_bck)

v1_nrpsy_dgt_sp_bck<-c(v1_clin$v1_npu_zns_002,v1_con$v1_npu_np_wie_002)
summary(v1_nrpsy_dgt_sp_bck)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00    5.00    6.00    6.19    8.00   14.00     115

Histogram

hist(v1_nrpsy_dgt_sp_bck, breaks=c(1:14), xlim=c(7,37), main="Digit-span backward", xlab="Score",ylab="Number of Individuals")

Psychomotor speed: Digit-Symbol-Test (DST) (continuous [number of correct symbols], v1_nrpsy_dg_sym)

This test measures processing speed. The participant is presented with rows of numbers and an empty space below each number. In these empty spaces, the participant is asked to fill in symbols that match the number above it. The respective number-symbole association is given at the top of the test sheet. It is measured how many correct symbols the participant can fill in during a 120 second period. Participants that only partially completed the test were excluded and are coded as -999.

v1_introcheck3<-c(v1_clin$v1_npu_np_introcheck3,v1_con$v1_npu_np_hawier)
v1_nrpsy_dg_sym_pre<-c(v1_clin$v1_npu_zst_001,v1_con$v1_npu_np_hawier_001)

v1_nrpsy_dg_sym<-ifelse(v1_introcheck3==1, v1_nrpsy_dg_sym_pre, 
                           ifelse(v1_introcheck3==9,-999,
                                  ifelse(v1_introcheck3==0,NA,NA)))

summary(v1_nrpsy_dg_sym[v1_nrpsy_dg_sym>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   13.00   49.00   61.00   62.43   75.00  124.00     125

Histogram

hist(v1_nrpsy_dg_sym[v1_nrpsy_dg_sym>=0], breaks=c(1:133), main="Digit-Symbol-Test", xlab="Number of correct symbols")

Crystallized IQ: Multiple-Choice Vocabulary Intelligence Test (MWT-B) (continuous [score], v1_nrpsy_mwtb)

This test assesses crystallized IQ. Crystallized IQ rises with advancing age. In this test, subjects are presented with 37 sets of five words each. Four “words” of each set are artificial (i.e. do not exist in the German language), one word really exists. They are instructed that they may be familiar with one word in each set, and asked to cross out that word. The known words start with easy ones and their difficulty increases. The sum score of correctly identified real words is the final score.

Important: only persons with German as a native language and who completed the test are included in the present dataset, those who were excluded due to these criteria are coded -999.

v1_introcheck4<-c(v1_clin$v1_npu_np_introcheck4,v1_con$v1_npu_np_mwtb)
v1_nrpsy_mwtb_pre<-c(v1_clin$v1_npu_mwt_001,v1_con$v1_npu_np_mwtb_001)

v1_nrpsy_mwtb<-ifelse((v1_introcheck4=="1" & v1_nrpsy_lng=="mother tongue"),v1_nrpsy_mwtb_pre,-999)

#Set one participant with zero recognized words to NA - this person either misunderstood the instructions or
#gave wrong answers on purpose
v1_nrpsy_mwtb[v1_nrpsy_mwtb==0]<-NA

summary(v1_nrpsy_mwtb[v1_nrpsy_mwtb>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   26.00   29.00   28.47   32.00   37.00      58

Histogram

hist(v1_nrpsy_mwtb[v1_nrpsy_mwtb>=0], breaks=c(0:37), main="Multiple-Choice Vocabulary Intelligence Test", xlab="Score")

Create dataset

v1_nrpsy<-data.frame(v1_nrpsy_com,
                     v1_nrpsy_lng,
                     v1_nrpsy_mtv,
                     v1_nrpsy_tmt_A_rt,
                     v1_nrpsy_tmt_A_err,
                     v1_nrpsy_tmt_B_rt,
                     v1_nrpsy_tmt_B_err,
                     v1_nrpsy_dgt_sp_frw,
                     v1_nrpsy_dgt_sp_bck,
                     v1_nrpsy_dg_sym,
                     v1_nrpsy_mwtb)

Visit 1: Questionnaires (patient/proband rates her/himself)

All participants were asked to fill out questionnaires on the following topics: religious beliefs, current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, current quality of life (WHOQOL-BREF) and personality (big five). Additionally, control participants completed the CAPE-42 questionnaire (Community Assessment of Psycic Experiences) and the Short Form Health Survey (SF-12). Medication adherence (compliance) was only assessed in clinical participants. All questionnaires are briefly explained below. Importantly, there are items in our database assessing whether a questionnaire was filled out correctly. Questionnaires considered unusable are NOT included in this dataset (i.e. are NA).

Community Assessment Psychic Experiences-42 (CAPE-42)

The CAPE-42 was developed by Jim van Os, Hélène Verdoux and Manon Hanssen. It is based on the PDI-21 and PDI-40 developed by Emmanuelle Peters et al (2004). It asesses psychotic-like experiences and was only assessed in control subjects. All items have a part A (“Never”,“Sometimes”,“Often”,“Nearly always”; coded 0-3, repectively) and a part B, which is to be answered if the answer to the corresponding part A item was not “Never” and asks how distressed the participant was by this experience (“Not distressed”,“A bit distressed”,“Quite distressed” or “Very distressed”; coded 0-3, repectively).

“Do you ever feel sad?” (ordinal [0,1,2,3], v1_cape_itm1A)

v1_cape_recode(v1_con$v1_cape_cape_hsjtraugefa1,"v1_cape_itm1A")
##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1223 4   207  25  1   83   1543
## [2,] Percent   79.3 0.3 13.4 1.6 0.1 5.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm1B)

v1_cape_recode(v1_con$v1_cape_cape_hsjtraugefb1,"v1_cape_itm1B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 97  118 19  1   85   1543
## [2,] Percent   79.3 6.3 7.6 1.2 0.1 5.5  100

“Do you ever feel as if people seem to drop hints about you or say things with a double meaning?” (ordinal [0,1,2,3], v1_cape_itm2A)

v1_cape_recode(v1_con$v1_cape_cape_anspapersa1,"v1_cape_itm2A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 76  148 13  2   81   1543
## [2,] Percent   79.3 4.9 9.6 0.8 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm2B)

v1_cape_recode(v1_con$v1_cape_cape_anspapersb1,"v1_cape_itm2B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 50  87  21  5   157  1543
## [2,] Percent   79.3 3.2 5.6 1.4 0.3 10.2 100

“Do you ever feel that you are not a very animated person?” (ordinal [0,1,2,3], v1_cape_itm3A)

v1_cape_recode(v1_con$v1_cape_cape_nlebha1,"v1_cape_itm3A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 148 80  9   2   81   1543
## [2,] Percent   79.3 9.6 5.2 0.6 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm3B)

v1_cape_recode(v1_con$v1_cape_cape_nlebhb1,"v1_cape_itm3B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 48  37  5   230  1543
## [2,] Percent   79.3 3.1 2.4 0.3 14.9 100

“Do you ever feel that you are not much of a talker when you are conversing with other people?” (ordinal [0,1,2,3], v1_cape_itm4A)

v1_cape_recode(v1_con$v1_cape_cape_nsaga1,"v1_cape_itm4A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 104 111 20  3   82   1543
## [2,] Percent   79.3 6.7 7.2 1.3 0.2 5.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm4B)

v1_cape_recode(v1_con$v1_cape_cape_nsagb1,"v1_cape_itm4B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 65  63  6   186  1543
## [2,] Percent   79.3 4.2 4.1 0.4 12.1 100

“Do you ever feel as if things in magazines or on TV were written especially for you?” (ordinal [0,1,2,3], v1_cape_itm5A)

v1_cape_recode(v1_con$v1_cape_cape_auszeita1,"v1_cape_itm5A")
##                -999 0    1  2   3   <NA>     
## [1,] No. cases 1223 202  31 5   1   81   1543
## [2,] Percent   79.3 13.1 2  0.3 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm5B)

v1_cape_recode(v1_con$v1_cape_cape_auszeitb1,"v1_cape_itm5B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 28  7   2   283  1543
## [2,] Percent   79.3 1.8 0.5 0.1 18.3 100

“Do you ever feel as if some people are not what they seem to be?” (ordinal [0,1,2,3], v1_cape_itm6A)

v1_cape_recode(v1_con$v1_cape_cape_geflnswsea1,"v1_cape_itm6A")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 1223 31 143 59  6   81   1543
## [2,] Percent   79.3 2  9.3 3.8 0.4 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm6B)

v1_cape_recode(v1_con$v1_cape_cape_geflnswseb1,"v1_cape_itm6B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 81  109 14  3   113  1543
## [2,] Percent   79.3 5.2 7.1 0.9 0.2 7.3  100

“Do you ever feel as if you are being persecuted in some way?” (ordinal [0,1,2,3], v1_cape_itm7A)

v1_cape_recode(v1_con$v1_cape_cape_verfa1,"v1_cape_itm7A")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 207  30  2   81   1543
## [2,] Percent   79.3 13.4 1.9 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm7B)

v1_cape_recode(v1_con$v1_cape_cape_verfb1,"v1_cape_itm7B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 17  10  5   288  1543
## [2,] Percent   79.3 1.1 0.6 0.3 18.7 100

“Do you ever feel that you experience few or no emotions at important events?” (ordinal [0,1,2,3], v1_cape_itm8A)

v1_cape_recode(v1_con$v1_cape_cape_kgefa1,"v1_cape_itm8A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 154 75  8   2   81   1543
## [2,] Percent   79.3 10  4.9 0.5 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm8B)

v1_cape_recode(v1_con$v1_cape_cape_kgefb1,"v1_cape_itm8B")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 1223 46 34  4   1   235  1543
## [2,] Percent   79.3 3  2.2 0.3 0.1 15.2 100

“Do you ever feel pessimistic about everything?” (ordinal [0,1,2,3], v1_cape_itm9A)

v1_cape_recode(v1_con$v1_cape_cape_negseha1,"v1_cape_itm9A")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 155 78  6   81   1543
## [2,] Percent   79.3 10  5.1 0.4 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm9B)

v1_cape_recode(v1_con$v1_cape_cape_negsehb1,"v1_cape_itm9B")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 1223 16 52  11  5   236  1543
## [2,] Percent   79.3 1  3.4 0.7 0.3 15.3 100

“Do you ever feel as if there is a conspiracy against you?” (ordinal [0,1,2,3], v1_cape_itm10A)

v1_cape_recode(v1_con$v1_cape_cape_kompla1,"v1_cape_itm10A")
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1223 204  32  1   1   82   1543
## [2,] Percent   79.3 13.2 2.1 0.1 0.1 5.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm10B)

v1_cape_recode(v1_con$v1_cape_cape_komplb1,"v1_cape_itm10B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 5   21  6   2   286  1543
## [2,] Percent   79.3 0.3 1.4 0.4 0.1 18.5 100

“Do you ever feel as if you are destined to be someone very important?” (ordinal [0,1,2,3], v1_cape_itm11A)

v1_cape_recode(v1_con$v1_cape_cape_bestwpa1,"v1_cape_itm11A")
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1223 178  53  7   1   81   1543
## [2,] Percent   79.3 11.5 3.4 0.5 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm11B)

v1_cape_recode(v1_con$v1_cape_cape_bestwpb1,"v1_cape_itm11B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 54  5   1   1   259  1543
## [2,] Percent   79.3 3.5 0.3 0.1 0.1 16.8 100

“Do you ever feel as if there is no future for you?” (ordinal [0,1,2,3], v1_cape_itm12A)

v1_cape_recode(v1_con$v1_cape_cape_keinza1,"v1_cape_itm12A")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 192  44  3   81   1543
## [2,] Percent   79.3 12.4 2.9 0.2 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm12B)

v1_cape_recode(v1_con$v1_cape_cape_keinzb1,"v1_cape_itm12B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 8   26  9   4   273  1543
## [2,] Percent   79.3 0.5 1.7 0.6 0.3 17.7 100

“Do you ever feel that you are a very special or unusual person?” (ordinal [0,1,2,3], v1_cape_itm13A)

v1_cape_recode(v1_con$v1_cape_cape_gefaupersa1,"v1_cape_itm13A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 131 79  25  4   81   1543
## [2,] Percent   79.3 8.5 5.1 1.6 0.3 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm13B)

v1_cape_recode(v1_con$v1_cape_cape_gefaupersb1,"v1_cape_itm13B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 88  14  3   1   214  1543
## [2,] Percent   79.3 5.7 0.9 0.2 0.1 13.9 100

“Do you ever feel as if you do not want to live anymore?” (ordinal [0,1,2,3], v1_cape_itm14A)

v1_cape_recode(v1_con$v1_cape_cape_nileba1,"v1_cape_itm14A")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 181  56  1   82   1543
## [2,] Percent   79.3 11.7 3.6 0.1 5.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm14B)

v1_cape_recode(v1_con$v1_cape_cape_nileba1,"v1_cape_itm14B")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 181  56  1   82   1543
## [2,] Percent   79.3 11.7 3.6 0.1 5.3  100

“Do you ever think that people can communicate telepathically?” (ordinal [0,1,2,3], v1_cape_itm15A)

v1_cape_recode(v1_con$v1_cape_cape_telea1,"v1_cape_itm15A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 170 56  11  2   81   1543
## [2,] Percent   79.3 11  3.6 0.7 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm15B)

v1_cape_recode(v1_con$v1_cape_cape_teleb1,"v1_cape_itm15B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 63  4   1   1   251  1543
## [2,] Percent   79.3 4.1 0.3 0.1 0.1 16.3 100

“Do you ever feel that you have no interest to be with other people?” (ordinal [0,1,2,3], v1_cape_itm16A)

v1_cape_recode(v1_con$v1_cape_cape_kbedgesa1,"v1_cape_itm16A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 84  136 18  1   81   1543
## [2,] Percent   79.3 5.4 8.8 1.2 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm16B)

v1_cape_recode(v1_con$v1_cape_cape_kbedgesb1,"v1_cape_itm16B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 133 18  4   165  1543
## [2,] Percent   79.3 8.6 1.2 0.3 10.7 100

“Do you ever feel as if electrical devices such as computers can influence the way you think?” (ordinal [0,1,2,3], v1_cape_itm17A)

v1_cape_recode(v1_con$v1_cape_cape_elegeggeda1,"v1_cape_itm17A")
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1223 221  13  4   1   81   1543
## [2,] Percent   79.3 14.3 0.8 0.3 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm17B)

v1_cape_recode(v1_con$v1_cape_cape_elegeggedb1,"v1_cape_itm17B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 7   9   2   302  1543
## [2,] Percent   79.3 0.5 0.6 0.1 19.6 100

“Do you ever feel that you are lacking in motivation to do things?” (ordinal [0,1,2,3], v1_cape_itm18A)

v1_cape_recode(v1_con$v1_cape_cape_motfehla1,"v1_cape_itm18A")
##                -999 0  1    2   3   <NA>     
## [1,] No. cases 1223 46 161  26  6   81   1543
## [2,] Percent   79.3 3  10.4 1.7 0.4 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm18B)

v1_cape_recode(v1_con$v1_cape_cape_motfehlb1,"v1_cape_itm18B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 53  99  30  11  127  1543
## [2,] Percent   79.3 3.4 6.4 1.9 0.7 8.2  100

“Do you ever cry about nothing?” (ordinal [0,1,2,3], v1_cape_itm19A)

v1_cape_recode(v1_con$v1_cape_cape_ougewa1,"v1_cape_itm19A")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 190  48  1   81   1543
## [2,] Percent   79.3 12.3 3.1 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm19B)

v1_cape_recode(v1_con$v1_cape_cape_ougewb1,"v1_cape_itm19B")
##                -999 0   1  2   <NA>     
## [1,] No. cases 1223 27  16 6   271  1543
## [2,] Percent   79.3 1.7 1  0.4 17.6 100

“Do you believe in the power of witchcraft, voodoo or the occult?” (ordinal [0,1,2,3], v1_cape_itm20A)

v1_cape_recode(v1_con$v1_cape_cape_hexvoa1,"v1_cape_itm20A")
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1223 193  34  6   6   81   1543
## [2,] Percent   79.3 12.5 2.2 0.4 0.4 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm20B)

v1_cape_recode(v1_con$v1_cape_cape_hexvob1,"v1_cape_itm20B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 38  5   1   1   275  1543
## [2,] Percent   79.3 2.5 0.3 0.1 0.1 17.8 100

“Do you ever feel that you are lacking in energy?” (ordinal [0,1,2,3], v1_cape_itm21A)

v1_cape_recode(v1_con$v1_cape_cape_energiela1,"v1_cape_itm21A")
##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1223 59  161  17  2   81   1543
## [2,] Percent   79.3 3.8 10.4 1.1 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm21B)

v1_cape_recode(v1_con$v1_cape_cape_energielb1,"v1_cape_itm21B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 59  86  26  9   140  1543
## [2,] Percent   79.3 3.8 5.6 1.7 0.6 9.1  100

“Do you ever feel that people look at you oddly because of your appearance?” (ordinal [0,1,2,3], v1_cape_itm22A)

v1_cape_recode(v1_con$v1_cape_cape_sonda1,"v1_cape_itm22A")
##                -999 0   1  2  3   <NA>     
## [1,] No. cases 1223 129 93 16 1   81   1543
## [2,] Percent   79.3 8.4 6  1  0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm22B)

v1_cape_recode(v1_con$v1_cape_cape_sondb1,"v1_cape_itm22B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 56  45  7   1   211  1543
## [2,] Percent   79.3 3.6 2.9 0.5 0.1 13.7 100

“Do you ever feel that your mind is empty?” (ordinal [0,1,2,3], v1_cape_itm23A)

v1_cape_recode(v1_con$v1_cape_cape_kopfleera1,"v1_cape_itm23A")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 134 100 4   82   1543
## [2,] Percent   79.3 8.7 6.5 0.3 5.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm23B)

v1_cape_recode(v1_con$v1_cape_cape_kopfleerb1,"v1_cape_itm23B")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 1223 47 44  7   5   217  1543
## [2,] Percent   79.3 3  2.9 0.5 0.3 14.1 100

“Do you ever feel as if the thoughts in your head are being taken away from you?” (ordinal [0,1,2,3], v1_cape_itm24A)

v1_cape_recode(v1_con$v1_cape_cape_gedaka1,"v1_cape_itm24A")
##                -999 0    1   <NA>     
## [1,] No. cases 1223 229  10  81   1543
## [2,] Percent   79.3 14.8 0.6 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm24B)

v1_cape_recode(v1_con$v1_cape_cape_gedakb1,"v1_cape_itm24B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 5   4   1   310  1543
## [2,] Percent   79.3 0.3 0.3 0.1 20.1 100

“Do you ever feel that you are spending all your days doing nothing?” (ordinal [0,1,2,3], v1_cape_itm25A)

v1_cape_recode(v1_con$v1_cape_cape_tagoetuna1,"v1_cape_itm25A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 113 110 14  2   81   1543
## [2,] Percent   79.3 7.3 7.1 0.9 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm25B)

v1_cape_recode(v1_con$v1_cape_cape_tagoetunb1,"v1_cape_itm25B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1223 49  47 25  5   194  1543
## [2,] Percent   79.3 3.2 3  1.6 0.3 12.6 100

“Do you ever feel as if the thoughts in your head are not your own?” (ordinal [0,1,2,3], v1_cape_itm26A)

v1_cape_recode(v1_con$v1_cape_cape_gedneiga1,"v1_cape_itm26A")
##                -999 0    1  2   <NA>     
## [1,] No. cases 1223 221  15 1   83   1543
## [2,] Percent   79.3 14.3 1  0.1 5.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm26B)

v1_cape_recode(v1_con$v1_cape_cape_gedneigb1,"v1_cape_itm26B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 6   4   4   2   304  1543
## [2,] Percent   79.3 0.4 0.3 0.3 0.1 19.7 100

" Do you ever feel that your feelings are lacking in intensity?" (ordinal [0,1,2,3], v1_cape_itm27A)

v1_cape_recode(v1_con$v1_cape_cape_gefinta1,"v1_cape_itm27A")
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1223 162  71  5   1   81   1543
## [2,] Percent   79.3 10.5 4.6 0.3 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm27B)

v1_cape_recode(v1_con$v1_cape_cape_gefintb1,"v1_cape_itm27B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 32  33  10  2   243  1543
## [2,] Percent   79.3 2.1 2.1 0.6 0.1 15.7 100

“Have your thoughts ever been so vivid that you were worried other people would hear them?” (ordinal [0,1,2,3], v1_cape_itm28A)

v1_cape_recode(v1_con$v1_cape_cape_lebhfa1,"v1_cape_itm28A")
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1223 223  12  2   1   82   1543
## [2,] Percent   79.3 14.5 0.8 0.1 0.1 5.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm28B)

v1_cape_recode(v1_con$v1_cape_cape_lebhfb1,"v1_cape_itm28B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 10  4   1   305  1543
## [2,] Percent   79.3 0.6 0.3 0.1 19.8 100

“Do you ever feel that you are lacking in spontaneity?” (ordinal [0,1,2,3], v1_cape_itm29A)

v1_cape_recode(v1_con$v1_cape_cape_sponfehla1,"v1_cape_itm29A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 123 102 13  1   81   1543
## [2,] Percent   79.3 8   6.6 0.8 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm29B)

v1_cape_recode(v1_con$v1_cape_cape_sponfehlb1,"v1_cape_itm29B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 42  60  11  2   205  1543
## [2,] Percent   79.3 2.7 3.9 0.7 0.1 13.3 100

“Do you ever hear your own thoughts being echoed back to you?” (ordinal [0,1,2,3], v1_cape_itm30A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa1,"v1_cape_itm30A")
##                -999 0    1  <NA>     
## [1,] No. cases 1223 224  15 81   1543
## [2,] Percent   79.3 14.5 1  5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm30B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob1,"v1_cape_itm30B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 10  4   1   305  1543
## [2,] Percent   79.3 0.6 0.3 0.1 19.8 100

“Do you ever feel as if you are under the control of some force or power other than yourself?” (ordinal [0,1,2,3], v1_cape_itm31A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa2,"v1_cape_itm31A")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 226  11  2   81   1543
## [2,] Percent   79.3 14.6 0.7 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm31B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob2,"v1_cape_itm31B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 5   4   3   1   307  1543
## [2,] Percent   79.3 0.3 0.3 0.2 0.1 19.9 100

“Do you ever feel that your emotions are blunted?” (ordinal [0,1,2,3], v1_cape_itm32A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa3,"v1_cape_itm32A")
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 1223 161  70  4   3   82   1543
## [2,] Percent   79.3 10.4 4.5 0.3 0.2 5.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm32B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob3,"v1_cape_itm32B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 36  30  7   3   244  1543
## [2,] Percent   79.3 2.3 1.9 0.5 0.2 15.8 100

“Do you ever hear voices when you are alone?” (ordinal [0,1,2,3], v1_cape_itm33A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa4,"v1_cape_itm33A")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 234  4   1   81   1543
## [2,] Percent   79.3 15.2 0.3 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm33B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob4,"v1_cape_itm33B")
##                -999 0   1   3   <NA>     
## [1,] No. cases 1223 3   1   1   315  1543
## [2,] Percent   79.3 0.2 0.1 0.1 20.4 100

“Do you ever hear voices talking to each other when you are alone?” (ordinal [0,1,2,3], v1_cape_itm34A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa5,"v1_cape_itm34A")
##                -999 0    <NA>     
## [1,] No. cases 1223 239  81   1543
## [2,] Percent   79.3 15.5 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm34B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob5,"v1_cape_itm34B")
##                -999 <NA>     
## [1,] No. cases 1223 320  1543
## [2,] Percent   79.3 20.7 100

“Do you ever feel that you are neglecting your appearance or personal hygiene?” (ordinal [0,1,2,3], v1_cape_itm35A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa6,"v1_cape_itm35A")
##                -999 0    1  2   <NA>     
## [1,] No. cases 1223 187  46 6   81   1543
## [2,] Percent   79.3 12.1 3  0.4 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm35B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob6,"v1_cape_itm35B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 22  21  7   2   268  1543
## [2,] Percent   79.3 1.4 1.4 0.5 0.1 17.4 100

“Do you ever feel that you can never get things done?” (ordinal [0,1,2,3], v1_cape_itm36A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa7,"v1_cape_itm36A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 112 110 14  3   81   1543
## [2,] Percent   79.3 7.3 7.1 0.9 0.2 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm36B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob7,"v1_cape_itm36B")
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 1223 24  64  24  15 193  1543
## [2,] Percent   79.3 1.6 4.1 1.6 1  12.5 100

“Do you ever feel that you have only few hobbies or interests?” (ordinal [0,1,2,3], v1_cape_itm37A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa8,"v1_cape_itm37A")
##                -999 0    1   2   <NA>     
## [1,] No. cases 1223 178  59  2   81   1543
## [2,] Percent   79.3 11.5 3.8 0.1 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm37B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob8,"v1_cape_itm37B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 1223 28  29  4   259  1543
## [2,] Percent   79.3 1.8 1.9 0.3 16.8 100

“Do you ever feel guilty?” (ordinal [0,1,2,3], v1_cape_itm38A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa9,"v1_cape_itm38A")
##                -999 0   1    2   3   <NA>     
## [1,] No. cases 1223 39  175  20  5   81   1543
## [2,] Percent   79.3 2.5 11.3 1.3 0.3 5.2  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm38B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob9,"v1_cape_itm38B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 40  107 35  18  120  1543
## [2,] Percent   79.3 2.6 6.9 2.3 1.2 7.8  100

“Do you ever feel like a failure?” (ordinal [0,1,2,3], v1_cape_itm39A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa10,"v1_cape_itm39A")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1223 114 110 12  2   82   1543
## [2,] Percent   79.3 7.4 7.1 0.8 0.1 5.3  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm39B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob10,"v1_cape_itm39B")
##                -999 0   1  2   3  <NA>     
## [1,] No. cases 1223 27  62 20  15 196  1543
## [2,] Percent   79.3 1.7 4  1.3 1  12.7 100

“Do you ever feel tense?” (ordinal [0,1,2,3], v1_cape_itm40A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa11,"v1_cape_itm40A")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1223 51  153 31 2   83   1543
## [2,] Percent   79.3 3.3 9.9 2  0.1 5.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm40B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob11,"v1_cape_itm40B")
##                -999 0   1  2  3   <NA>     
## [1,] No. cases 1223 89  77 16 3   135  1543
## [2,] Percent   79.3 5.8 5  1  0.2 8.7  100

“Do you ever feel as if a double has taken the place of a family member, friend or acquaintance?” (ordinal [0,1,2,3], v1_cape_itm41A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa12,"v1_cape_itm41A")
##                -999 0    1   <NA>     
## [1,] No. cases 1223 235  1   84   1543
## [2,] Percent   79.3 15.2 0.1 5.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm41B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob12,"v1_cape_itm41B")
##                -999 0   2   <NA>     
## [1,] No. cases 1223 1   1   318  1543
## [2,] Percent   79.3 0.1 0.1 20.6 100

“Do you ever see objects, people or animals that other people cannot see?” (ordinal [0,1,2,3], v1_cape_itm42A)

v1_cape_recode(v1_con$v1_cape_cape_gedechoa13,"v1_cape_itm42A")
##                -999 0    1   3   <NA>     
## [1,] No. cases 1223 230  6   1   83   1543
## [2,] Percent   79.3 14.9 0.4 0.1 5.4  100

“Please indicate how distressed you are by this experience” (ordinal [0,1,2,3], v1_cape_itm42B)

v1_cape_recode(v1_con$v1_cape_cape_gedechob13,"v1_cape_itm42B")
##                -999 0   <NA>     
## [1,] No. cases 1223 8   312  1543
## [2,] Percent   79.3 0.5 20.2 100

Create dataset

v1_cape<-data.frame(v1_cape_itm1A,v1_cape_itm1B,
                    v1_cape_itm2A,v1_cape_itm2B,
                    v1_cape_itm3A,v1_cape_itm3B,
                    v1_cape_itm4A,v1_cape_itm4B,
                    v1_cape_itm5A,v1_cape_itm5B,
                    v1_cape_itm6A,v1_cape_itm6B,
                    v1_cape_itm7A,v1_cape_itm7B,
                    v1_cape_itm8A,v1_cape_itm8B,
                    v1_cape_itm9A,v1_cape_itm9B,
                    v1_cape_itm10A,v1_cape_itm10B,
                    v1_cape_itm11A,v1_cape_itm11B,
                    v1_cape_itm12A,v1_cape_itm12B,
                    v1_cape_itm13A,v1_cape_itm13B,
                    v1_cape_itm14A,v1_cape_itm14B,
                    v1_cape_itm15A,v1_cape_itm15B,
                    v1_cape_itm16A,v1_cape_itm16B,
                    v1_cape_itm17A,v1_cape_itm17B,
                    v1_cape_itm18A,v1_cape_itm18B,
                    v1_cape_itm19A,v1_cape_itm19B,
                    v1_cape_itm20A,v1_cape_itm20B,
                    v1_cape_itm21A,v1_cape_itm21B,
                    v1_cape_itm22A,v1_cape_itm22B,
                    v1_cape_itm23A,v1_cape_itm23B,
                    v1_cape_itm24A,v1_cape_itm24B,
                    v1_cape_itm25A,v1_cape_itm25B,
                    v1_cape_itm26A,v1_cape_itm26B,
                    v1_cape_itm27A,v1_cape_itm27B,
                    v1_cape_itm28A,v1_cape_itm28B,
                    v1_cape_itm29A,v1_cape_itm29B,
                    v1_cape_itm30A,v1_cape_itm30B,
                    v1_cape_itm31A,v1_cape_itm31B,
                    v1_cape_itm32A,v1_cape_itm32B,
                    v1_cape_itm33A,v1_cape_itm33B,
                    v1_cape_itm34A,v1_cape_itm34B,
                    v1_cape_itm35A,v1_cape_itm35B,
                    v1_cape_itm36A,v1_cape_itm36B,
                    v1_cape_itm37A,v1_cape_itm37B,
                    v1_cape_itm38A,v1_cape_itm38B,
                    v1_cape_itm39A,v1_cape_itm39B,
                    v1_cape_itm40A,v1_cape_itm40B,
                    v1_cape_itm41A,v1_cape_itm41B,
                    v1_cape_itm42A,v1_cape_itm42B)

Short Form Health Survey (SF-12)

The SF-12 is a short instrument to assess health-related quality of life.

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v1_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v1_sf12_recode(v1_con$v1_sf12_sf_allgemein,"v1_sf12_itm0")
##                -999 2   3   4   5   6   7   8   9   10  <NA>     
## [1,] No. cases 1223 2   3   11  10  11  43  98  91  44  7    1543
## [2,] Percent   79.3 0.1 0.2 0.7 0.6 0.7 2.8 6.4 5.9 2.9 0.5  100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v1_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v1_sf12_recode(v1_con$v1_sf12_sf1,"v1_sf12_itm1")
##                -999 1   2    3   4   5   <NA>     
## [1,] No. cases 1223 59  160  88  11  1   1    1543
## [2,] Percent   79.3 3.8 10.4 5.7 0.7 0.1 0.1  100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v1_sf12_itm2) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v1_sf12_recode(v1_con$v1_sf12_sf2,"v1_sf12_itm2")
##                -999 1   2   3    <NA>     
## [1,] No. cases 1223 1   27  291  1    1543
## [2,] Percent   79.3 0.1 1.7 18.9 0.1  100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v1_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v1_sf12_recode(v1_con$v1_sf12_sf3,"v1_sf12_itm3")
##                -999 1   2   3    <NA>     
## [1,] No. cases 1223 3   42  274  1    1543
## [2,] Percent   79.3 0.2 2.7 17.8 0.1  100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v1_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf4,"v1_sf12_itm4")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 37  280  3    1543
## [2,] Percent   79.3 2.4 18.1 0.2  100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v1_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf5,"v1_sf12_itm5")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 21  291  8    1543
## [2,] Percent   79.3 1.4 18.9 0.5  100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v1_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf6,"v1_sf12_itm6")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 18  299  3    1543
## [2,] Percent   79.3 1.2 19.4 0.2  100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v1_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v1_sf12_recode(v1_con$v1_sf12_sf7,"v1_sf12_itm7")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 10  307  3    1543
## [2,] Percent   79.3 0.6 19.9 0.2  100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v1_sf12_itm8) Answering alternatives are the following: “None”-1 to “Extremely”-6.

v1_sf12_recode(v1_con$v1_sf12_st8,"v1_sf12_itm8")
##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 1223 175  64  41  26  9   1   4    1543
## [2,] Percent   79.3 11.3 4.1 2.7 1.7 0.6 0.1 0.3  100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v1_sf12_itm9)

v1_sf12_recode(v1_con$v1_sf12_st9,"v1_sf12_itm9")
##                -999 1   2    3   4   5   6   <NA>     
## [1,] No. cases 1223 23  194  71  25  3   2   2    1543
## [2,] Percent   79.3 1.5 12.6 4.6 1.6 0.2 0.1 0.1  100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v1_sf12_itm10)

v1_sf12_recode(v1_con$v1_sf12_st10,"v1_sf12_itm10")
##                -999 1   2   3   4   5  6   <NA>     
## [1,] No. cases 1223 18  119 105 57  15 2   4    1543
## [2,] Percent   79.3 1.2 7.7 6.8 3.7 1  0.1 0.3  100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v1_sf12_itm11)

v1_sf12_recode(v1_con$v1_sf12_st11,"v1_sf12_itm11")
##                -999 3   4   5    6   <NA>     
## [1,] No. cases 1223 7   44  163  102 4    1543
## [2,] Percent   79.3 0.5 2.9 10.6 6.6 0.3  100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [1,2,3,4,5], v1_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

ATTENTION: there appears to be something wrong with the coding of this item, i.e. the export of the item and the display in the GUI of the database are inconsistent. NOT CONTAINED IN DATASET FOR NOW

v1_sf12_recode(v1_con$v1_sf12_st12,"v1_sf12_itm12")

Create dataset

v1_sf12<-data.frame(v1_sf12_itm0,
                    v1_sf12_itm1,
                    v1_sf12_itm2,
                    v1_sf12_itm3,
                    v1_sf12_itm4,
                    v1_sf12_itm5,
                    v1_sf12_itm6,
                    v1_sf12_itm7,
                    v1_sf12_itm8,
                    v1_sf12_itm9,
                    v1_sf12_itm10,
                    v1_sf12_itm11)

#INCLUDE v2_sf12_itm12 when issues are settled

Religious beliefs

This self-created questionnaire asks about whether the participant belongs to a certain belief system and how actively she or he practices this belief. The first two questions are about Christianity and Islam. In a third question, other belief systems such are Judaism, Hinduism, Buddhism, Other (specify) and No religious denomination are assessed. There are also mode fine-grained distinctions concerning Christianity and Islan, but these are not included in the present dataset. The second item assesses how actively the belief is practiced. Because this questionnaire was introduced after data collection started, it is included in Visit 4 as well for those participants that were not assessed in Visit 1. In control participants, the questionnaire is assessed in Visit 1.

Religion Christianity (dichotomous, v1_rel_chr)

v1_rel_chris<-c(v1_clin$v1_religion_christ,v1_con$v1_religion_christ_jn)
v1_rel_chr<-ifelse(v1_rel_chris==1, "Y","N")
descT(v1_rel_chr) 
##                N   Y    <NA>     
## [1,] No. cases 38  677  828  1543
## [2,] Percent   2.5 43.9 53.7 100

Religion Islam (dichotomous, v1_rel_isl)

v1_rel_islam<-c(v1_clin$v1_religion_islam_jn,v1_con$v1_religion_islam_jn)
v1_rel_isl<-ifelse(v1_rel_islam==1, "Y","N")
descT(v1_rel_isl) 
##                N   Y   <NA>     
## [1,] No. cases 121 21  1401 1543
## [2,] Percent   7.8 1.4 90.8 100

Other religion (categorical,[v1_rel_oth])

v1_rel_var<-c(v1_clin$v1_religion_religion,v1_con$v1_religion_religion)
v1_rel_oth<-ifelse(v1_rel_var==1, "Judaism",
                   ifelse(v1_rel_var==2, "Hinduism",
                          ifelse(v1_rel_var==3, "Buddhism",
                                 ifelse(v1_rel_var==4, "Other",
                                        ifelse(v1_rel_var==5, "No denomination",NA)))))
descT(v1_rel_oth) 
##                Buddhism Judaism No denomination Other <NA>     
## [1,] No. cases 14       1       249             3     1276 1543
## [2,] Percent   0.9      0.1     16.1            0.2   82.7 100

“How actively do you practice your belief?” (ordinal [1,2,3,4,5], v1_rel_act)
This is an ordinal item with the following answer possibilities and the assigned gradation: “not at all”-1,“little active”-2,“moderately active”-3,“rather active”-4,“very actively”-5.

v1_rel_act<-c(v1_clin$v1_religion_religion_aktiv,v1_con$v1_religion_aktiv)
descT(v1_rel_act)
##                1   2    3    4   5   <NA>     
## [1,] No. cases 324 269  161  79  48  662  1543
## [2,] Percent   21  17.4 10.4 5.1 3.1 42.9 100

Create dataset

v1_rlgn<-data.frame(v1_rel_chr,v1_rel_isl,v1_rel_oth,v1_rel_act)

Medication adherence (compliance)

This questionnaire asks whether psychopharmacological medication was taken as prescribed. The past seven days and the past six months are assessed. Both items have the following gradation: “everyday, exactly as prescribed”-1, “everyday, but not always as prescribed”-2, “regularly, but not every day”-3, “sometimes, but not regularly”-4, “seldom”-5, “not at all”-6. Control participants are coded -999.

Past seven days (ordinal [1,2,3,4,5,6], v1_med_pst_wk)

v1_med_chk<-c(v1_clin$v1_compl_verwer_fragebogen,rep(1,dim(v1_con)[1]))
v1_med_pst_wk_pre<-c(v1_clin$v1_compl_psychopharm_7_tag,rep(-999,dim(v1_con)[1]))
  
v1_med_pst_wk<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_med_pst_wk<-ifelse((is.na(v1_med_chk) | v1_med_chk!=2), 
                      v1_med_pst_wk_pre, v1_med_pst_wk)

descT(v1_med_pst_wk)
##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 320  955  82  21  5   3   13  144  1543
## [2,] Percent   20.7 61.9 5.3 1.4 0.3 0.2 0.8 9.3  100

Past six months (ordinal [1,2,3,4,5,6], v1_med_pst_sx_mths)

v1_med_pre<-c(v1_clin$v1_compl_psychopharm_6_mon,rep(-999,dim(v1_con)[1]))

v1_med_pst_sx_mths<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])
v1_med_pst_sx_mths<-ifelse((is.na(v1_med_chk) | v1_med_chk!=2),
                           v1_med_pre, v1_med_pst_sx_mths)

descT(v1_med_pst_sx_mths)
##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 320  700  145 103 42  23  49  161  1543
## [2,] Percent   20.7 45.4 9.4 6.7 2.7 1.5 3.2 10.4 100

Create dataset

v1_med_adh<-data.frame(v1_med_pst_wk,v1_med_pst_sx_mths)

Beck Depression Inventory (BDI-II)

The German translation of the BDI-II (Hautzinger, Keller, & Kühner, 2006) asesses depressive symptoms. Patients are supposed to pick the answer that best describes how they have been feeling during the past two weeks. Each item is rated from zero to three, except item 16 (sleep) and item 18 (apppetite), for which seven alternatives exist (described below). With all items, higher scores mean more depressive symptomatology. For clinically meaningful threshold values see sum score calculation at the end of thhis section.

1. Sadness (ordinal [0,1,2,3], v1_bdi2_itm1)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi1_traurigkeit,v1_con$v1_bdi2_s1_bdi1,"v1_bdi2_itm1")
##                0    1    2   3   <NA>     
## [1,] No. cases 885  444  59  28  127  1543
## [2,] Percent   57.4 28.8 3.8 1.8 8.2  100

2. Pessimism (ordinal [0,1,2,3], v1_bdi2_itm2)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi2_pessimismus,v1_con$v1_bdi2_s1_bdi2,"v1_bdi2_itm2")
##                0    1    2   3   <NA>     
## [1,] No. cases 965  298  109 41  130  1543
## [2,] Percent   62.5 19.3 7.1 2.7 8.4  100

3. Past failure (ordinal [0,1,2,3], v1_bdi2_itm3)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi3_versagensgef,v1_con$v1_bdi2_s1_bdi3,"v1_bdi2_itm3")
##                0    1    2    3   <NA>     
## [1,] No. cases 842  300  220  54  127  1543
## [2,] Percent   54.6 19.4 14.3 3.5 8.2  100

4. Loss of pleasure (ordinal [0,1,2,3], v1_bdi2_itm4)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi4_verlust_freude,v1_con$v1_bdi2_s1_bdi4,"v1_bdi2_itm4")
##                0    1    2   3   <NA>     
## [1,] No. cases 758  456  146 54  129  1543
## [2,] Percent   49.1 29.6 9.5 3.5 8.4  100

5. Guilty feelings (ordinal [0,1,2,3], v1_bdi2_itm5)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi5_schuldgef,v1_con$v1_bdi2_s1_bdi5,"v1_bdi2_itm5")
##                0    1    2  3   <NA>     
## [1,] No. cases 904  411  61 41  126  1543
## [2,] Percent   58.6 26.6 4  2.7 8.2  100

6. Punishment feelings (ordinal [0,1,2,3], v1_bdi2_itm6)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi6_bestrafungsgef,v1_con$v1_bdi2_s1_bdi6,"v1_bdi2_itm6")
##                0    1    2   3   <NA>     
## [1,] No. cases 1056 213  35  110 129  1543
## [2,] Percent   68.4 13.8 2.3 7.1 8.4  100

7. Self-dislike (ordinal [0,1,2,3], v1_bdi2_itm7)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi7_selbstablehnung,v1_con$v1_bdi2_s1_bdi7,"v1_bdi2_itm7")
##                0    1    2   3   <NA>     
## [1,] No. cases 980  257  138 38  130  1543
## [2,] Percent   63.5 16.7 8.9 2.5 8.4  100

8. Self-criticalness (ordinal [0,1,2,3], v1_bdi2_itm8)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi8_selbstvorwuerfe,v1_con$v1_bdi2_s1_bdi8,"v1_bdi2_itm8")
##                0    1    2   3  <NA>     
## [1,] No. cases 808  411  145 47 132  1543
## [2,] Percent   52.4 26.6 9.4 3  8.6  100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v1_bdi2_itm9)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi9_selbstmordged,v1_con$v1_bdi2_s1_bdi9,"v1_bdi2_itm9")
##                0    1    2   3   <NA>     
## [1,] No. cases 1112 269  22  12  128  1543
## [2,] Percent   72.1 17.4 1.4 0.8 8.3  100

10. Crying (ordinal [0,1,2,3], v1_bdi2_itm10)

v1_bdi2_recode(v1_clin$v1_bdi2_s1_bdi10_weinen,v1_con$v1_bdi2_s1_bdi10,"v1_bdi2_itm10")
##                0    1    2   3   <NA>     
## [1,] No. cases 999  208  64  141 131  1543
## [2,] Percent   64.7 13.5 4.1 9.1 8.5  100

11. Agitation (ordinal [0,1,2,3], v1_bdi2_itm11)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi11_unruhe,v1_con$v1_bdi2_s2_bdi11,"v1_bdi2_itm11")
##                0    1    2  3   <NA>     
## [1,] No. cases 894  376  77 50  146  1543
## [2,] Percent   57.9 24.4 5  3.2 9.5  100

12. Loss of interest (ordinal [0,1,2,3], v1_bdi2_itm12)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi12_interessverl,v1_con$v1_bdi2_s2_bdi12,"v1_bdi2_itm12")
##                0    1    2   3   <NA>     
## [1,] No. cases 906  320  102 70  145  1543
## [2,] Percent   58.7 20.7 6.6 4.5 9.4  100

13. Indecisiveness (ordinal [0,1,2,3], v1_bdi2_itm13)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi13_entschlussunf,v1_con$v1_bdi2_s2_bdi13,"v1_bdi2_itm13")
##                0    1    2   3   <NA>     
## [1,] No. cases 836  366  115 83  143  1543
## [2,] Percent   54.2 23.7 7.5 5.4 9.3  100

14. Worthlessness (ordinal [0,1,2,3], v1_bdi2_itm14)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi14_wertlosigkeit,v1_con$v1_bdi2_s2_bdi14,"v1_bdi2_itm14")
##                0    1    2    3   <NA>     
## [1,] No. cases 968  228  159  41  147  1543
## [2,] Percent   62.7 14.8 10.3 2.7 9.5  100

15. Loss of energy (ordinal [0,1,2,3], v1_bdi2_itm15)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi15_energieverlust,v1_con$v1_bdi2_s2_bdi15,"v1_bdi2_itm15")
##                0    1    2    3   <NA>     
## [1,] No. cases 658  537  172  26  150  1543
## [2,] Percent   42.6 34.8 11.1 1.7 9.7  100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v1_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep“. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v1_itm_bdi2_chk<-c(v1_clin$v1_bdi2_s1_verwer_fragebogen,v1_con$v1_bdi2_s1_bdi_korrekt)
v1_itm_bdi2_itm16_clin_con<-c(v1_clin$v1_bdi2_s2_bdi16_schlafgewohn,v1_con$v1_bdi2_s2_bdi16)

v1_bdi2_itm16<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])

v1_bdi2_itm16<-ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & v1_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                           (v1_itm_bdi2_itm16_clin_con==1 | v1_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm16_clin_con==2 | v1_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm16_clin_con==3 | v1_itm_bdi2_itm16_clin_con==300), 3, v1_bdi2_itm16))))  

v1_bdi2_itm16<-factor(v1_bdi2_itm16,ordered=T)
descT(v1_bdi2_itm16)
##                0    1    2   3   <NA>     
## [1,] No. cases 604  516  170 107 146  1543
## [2,] Percent   39.1 33.4 11  6.9 9.5  100

17. Irritability (ordinal [0,1,2,3], v1_bdi2_itm17)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi17_reizbarkeit,v1_con$v1_bdi2_s2_bdi17,"v1_bdi2_itm17")
##                0    1    2  3   <NA>     
## [1,] No. cases 1033 298  47 21  144  1543
## [2,] Percent   66.9 19.3 3  1.4 9.3  100

18. Change in appetite (ordinal [0,1,2,3], v1_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v1_itm_bdi2_itm18_clin_con<-c(v1_clin$v1_bdi2_s2_bdi18_appetit,v1_con$v1_bdi2_s2_bdi18)
v1_bdi2_itm18<-rep(NA,dim(v1_clin)[1]+dim(v1_con)[1])

v1_bdi2_itm18<-ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & v1_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                           (v1_itm_bdi2_itm18_clin_con==1 | v1_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm18_clin_con==2 | v1_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v1_itm_bdi2_chk) | v1_itm_bdi2_chk!=2) & 
                               (v1_itm_bdi2_itm18_clin_con==3 | v1_itm_bdi2_itm18_clin_con==300), 3, v1_bdi2_itm18))))  

v1_bdi2_itm18<-factor(v1_bdi2_itm18,ordered=T)
descT(v1_bdi2_itm18)
##                0    1    2   3   <NA>     
## [1,] No. cases 808  435  99  52  149  1543
## [2,] Percent   52.4 28.2 6.4 3.4 9.7  100

19. Concentration difficulty (ordinal [0,1,2,3], v1_bdi2_itm19)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi19_konzschw,v1_con$v1_bdi2_s2_bdi19,"v1_bdi2_itm19")
##                0    1    2    3   <NA>     
## [1,] No. cases 729  399  239  30  146  1543
## [2,] Percent   47.2 25.9 15.5 1.9 9.5  100

20. Tiredness or fatigue (ordinal [0,1,2,3], v1_bdi2_itm20)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi20_ermued_ersch,v1_con$v1_bdi2_s2_bdi20,"v1_bdi2_itm20")
##                0    1    2   3   <NA>     
## [1,] No. cases 705  512  145 37  144  1543
## [2,] Percent   45.7 33.2 9.4 2.4 9.3  100

21. Loss of interest in sex (ordinal [0,1,2,3], v1_bdi2_itm21)

v1_bdi2_recode(v1_clin$v1_bdi2_s2_bdi21_sex_interess,v1_con$v1_bdi2_s2_bdi21,"v1_bdi2_itm21")
##                0    1    2   3   <NA>     
## [1,] No. cases 836  279  137 138 153  1543
## [2,] Percent   54.2 18.1 8.9 8.9 9.9  100

BDI-II sum score calculation (continuous [0-63], v1_bdi2_sum) The following cut-off values are generally considered to be meaningful:

  • “0-10 = These ups and downs are considered normal”
  • “11-16 = Mild mood disturbance”
  • “17-20 = Borderline clinical depression”
  • “21-30 = Moderate depression”
  • “31-40 = Severe depression”
  • “over 40 = Extreme depression”

Please note that if one or more of BDI-II items are missing, this will result in the sum score to become NA.

v1_bdi2_sum<-as.numeric.factor(v1_bdi2_itm1)+
              as.numeric.factor(v1_bdi2_itm2)+
              as.numeric.factor(v1_bdi2_itm3)+
              as.numeric.factor(v1_bdi2_itm4)+
              as.numeric.factor(v1_bdi2_itm5)+
              as.numeric.factor(v1_bdi2_itm6)+
              as.numeric.factor(v1_bdi2_itm7)+
              as.numeric.factor(v1_bdi2_itm8)+
              as.numeric.factor(v1_bdi2_itm9)+
              as.numeric.factor(v1_bdi2_itm10)+
              as.numeric.factor(v1_bdi2_itm11)+
              as.numeric.factor(v1_bdi2_itm12)+
              as.numeric.factor(v1_bdi2_itm13)+
              as.numeric.factor(v1_bdi2_itm14)+
              as.numeric.factor(v1_bdi2_itm15)+
              as.numeric.factor(v1_bdi2_itm16)+
              as.numeric.factor(v1_bdi2_itm17)+
              as.numeric.factor(v1_bdi2_itm18)+
              as.numeric.factor(v1_bdi2_itm19)+
              as.numeric.factor(v1_bdi2_itm20)+
              as.numeric.factor(v1_bdi2_itm21)

summary(v1_bdi2_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    2.00    8.00   11.36   18.00   59.00     219

Create dataset

v1_bdi2<-data.frame(v1_bdi2_itm1,v1_bdi2_itm2,v1_bdi2_itm3,v1_bdi2_itm4,v1_bdi2_itm5,
                    v1_bdi2_itm6,v1_bdi2_itm7,v1_bdi2_itm8,v1_bdi2_itm9,v1_bdi2_itm10,
                    v1_bdi2_itm11,v1_bdi2_itm12,v1_bdi2_itm13,v1_bdi2_itm14,
                    v1_bdi2_itm15,v1_bdi2_itm16,v1_bdi2_itm17,v1_bdi2_itm18,
                    v1_bdi2_itm19,v1_bdi2_itm20,v1_bdi2_itm21,v1_bdi2_sum)

Altman Self-Rating Mania Scala (ASRM)

The ASRM (Altman, Hedeker, Peterson, & Davis, 1997) assesses symptoms of mania during the past week. All items are scored from zero to four with higher scores indicating more mania symptoms.

1. Positive Mood (ordinal [0,1,2,3,4], v1_asrm_itm1)

v1_asrm_recode(v1_clin$v1_asrm_asrm1_gluecklich,v1_con$v1_asrm_asrm1,"v1_asrm_itm1")
##                0    1   2   3   4   <NA>     
## [1,] No. cases 928  340 81  49  12  133  1543
## [2,] Percent   60.1 22  5.2 3.2 0.8 8.6  100

2 Self-Confidence (ordinal [0,1,2,3,4], v1_asrm_itm2)

v1_asrm_recode(v1_clin$v1_asrm_asrm2_selbstbewusst,v1_con$v1_asrm_asrm2,"v1_asrm_itm2")
##                0    1    2  3  4   <NA>     
## [1,] No. cases 954  299  93 46 18  133  1543
## [2,] Percent   61.8 19.4 6  3  1.2 8.6  100

3. Sleep (ordinal [0,1,2,3,4], v1_asrm_itm3)

v1_asrm_recode(v1_clin$v1_asrm_asrm3_schlaf,v1_con$v1_asrm_asrm3,"v1_asrm_itm3")
##                0    1    2   3   4  <NA>     
## [1,] No. cases 1083 222  53  35  16 134  1543
## [2,] Percent   70.2 14.4 3.4 2.3 1  8.7  100

4. Speech (ordinal [0,1,2,3,4], v1_asrm_itm4)

v1_asrm_recode(v1_clin$v1_asrm_asrm4_reden,v1_con$v1_asrm_asrm4,"v1_asrm_itm4")
##                0    1    2   3   4   <NA>     
## [1,] No. cases 998  295  70  36  14  130  1543
## [2,] Percent   64.7 19.1 4.5 2.3 0.9 8.4  100

5. Activity Level (ordinal [0,1,2,3,4], v1_asrm_itm5)

v1_asrm_recode(v1_clin$v1_asrm_asrm5_aktiv,v1_con$v1_asrm_asrm5,"v1_asrm_itm5")
##                0    1    2   3   4   <NA>     
## [1,] No. cases 951  320  83  36  24  129  1543
## [2,] Percent   61.6 20.7 5.4 2.3 1.6 8.4  100

Create ASRM sum scoresum score (continuous [0-20],v1_asrm_sum)

v1_asrm_sum<-as.numeric.factor(v1_asrm_itm1)+
            as.numeric.factor(v1_asrm_itm2)+
            as.numeric.factor(v1_asrm_itm3)+
            as.numeric.factor(v1_asrm_itm4)+
            as.numeric.factor(v1_asrm_itm5)

summary(v1_asrm_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   1.000   2.241   3.000  20.000     142

Create dataset

v1_asrm<-data.frame(v1_asrm_itm1,v1_asrm_itm2,v1_asrm_itm3,v1_asrm_itm4,v1_asrm_itm5,v1_asrm_sum)

Manie-Selbstbeurteilungsskala (MSS)

Forty-eight statements, each of which asks for a mania symtom and should to be answered “Yes” or No" (Shugar, Schertzer, Toner, & Di Gasbarro, 1992). Measures mania symptoms during the past month. All questions have the same direction, “Yes” indicating mania symptom present.

1. “I had more energy” (dichotomous, v1_mss_itm1)

v1_mss_recode(v1_clin$v1_mss_s1_mss1_energie,v1_con$v1_mss_s1_mss1,"v1_mss_itm1")
##                N    Y    <NA>     
## [1,] No. cases 1040 359  144  1543
## [2,] Percent   67.4 23.3 9.3  100

2. “I had trouble sitting still” (dichotomous, v1_mss_itm2)

v1_mss_recode(v1_clin$v1_mss_s1_mss2_ruhig_sitzen,v1_con$v1_mss_s1_mss2,"v1_mss_itm2")
##                N    Y    <NA>     
## [1,] No. cases 1078 317  148  1543
## [2,] Percent   69.9 20.5 9.6  100

3. “I drove faster” (dichotomous, v1_mss_itm3)

v1_mss_recode(v1_clin$v1_mss_s1_mss3_auto_fahren,v1_con$v1_mss_s1_mss3,"v1_mss_itm3")
##                N    Y   <NA>     
## [1,] No. cases 1250 71  222  1543
## [2,] Percent   81   4.6 14.4 100

4. “I drank more alcoholic beverages” (dichotomous, v1_mss_itm4)

v1_mss_recode(v1_clin$v1_mss_s1_mss4_alkohol,v1_con$v1_mss_s1_mss4,"v1_mss_itm4")
##                N    Y   <NA>     
## [1,] No. cases 1253 126 164  1543
## [2,] Percent   81.2 8.2 10.6 100

5. “I changed clothes several times a day” (dichotomous, v1_mss_itm5)

v1_mss_recode(v1_clin$v1_mss_s1_mss5_umziehen, v1_con$v1_mss_s1_mss5,"v1_mss_itm5")
##                N    Y   <NA>     
## [1,] No. cases 1224 169 150  1543
## [2,] Percent   79.3 11  9.7  100

6. “I wore brighter clothes/make-up” (dichotomous, v1_mss_itm6)

v1_mss_recode(v1_clin$v1_mss_s1_mss6_bunter,v1_con$v1_mss_s1_mss6,"v1_mss_itm6")
##                N    Y   <NA>     
## [1,] No. cases 1278 113 152  1543
## [2,] Percent   82.8 7.3 9.9  100

7. “I played music louder” (dichotomous, v1_mss_itm7)

v1_mss_recode(v1_clin$v1_mss_s1_mss7_musik_lauter,v1_con$v1_mss_s1_mss7,"v1_mss_itm7")
##                N    Y    <NA>     
## [1,] No. cases 1128 270  145  1543
## [2,] Percent   73.1 17.5 9.4  100

8. “I ate faster than usual” (dichotomous, v1_mss_itm8)

v1_mss_recode(v1_clin$v1_mss_s1_mss8_hastiger_essen,v1_con$v1_mss_s1_mss8,"v1_mss_itm8")
##                N    Y   <NA>     
## [1,] No. cases 1169 231 143  1543
## [2,] Percent   75.8 15  9.3  100

9. “I ate more than usual” (dichotomous, v1_mss_itm9)

v1_mss_recode(v1_clin$v1_mss_s1_mss9_mehr_essen,v1_con$v1_mss_s1_mss9,"v1_mss_itm9")
##                N    Y    <NA>     
## [1,] No. cases 1043 356  144  1543
## [2,] Percent   67.6 23.1 9.3  100

10. “I slept fewer hours than usual” (dichotomous, v1_mss_itm10)

v1_mss_recode(v1_clin$v1_mss_s1_mss10_weniger_schlaf,v1_con$v1_mss_s1_mss10,"v1_mss_itm10")
##                N    Y    <NA>     
## [1,] No. cases 1152 241  150  1543
## [2,] Percent   74.7 15.6 9.7  100

11. “I started things that I didn’t finish” (dichotomous, v1_mss_itm11)

v1_mss_recode(v1_clin$v1_mss_s1_mss11_unbeendet,v1_con$v1_mss_s1_mss11,"v1_mss_itm11")
##                N    Y   <NA>     
## [1,] No. cases 1014 386 143  1543
## [2,] Percent   65.7 25  9.3  100

12. “I gave away my own possessions” (dichotomous, v1_mss_itm12)

v1_mss_recode(v1_clin$v1_mss_s1_mss12_weggeben,v1_con$v1_mss_s1_mss12,"v1_mss_itm12")
##                N    Y    <NA>     
## [1,] No. cases 1193 206  144  1543
## [2,] Percent   77.3 13.4 9.3  100

13. “I bought gifts for people” (dichotomous, v1_mss_itm13)

v1_mss_recode(v1_clin$v1_mss_s1_mss13_geschenke,v1_con$v1_mss_s1_mss13,"v1_mss_itm13")
##                N    Y   <NA>     
## [1,] No. cases 1229 169 145  1543
## [2,] Percent   79.7 11  9.4  100

14. “I spent money more freely” (dichotomous, v1_mss_itm14)

v1_mss_recode(v1_clin$v1_mss_s1_mss14_mehr_geld,v1_con$v1_mss_s1_mss14,"v1_mss_itm14")
##                N    Y    <NA>     
## [1,] No. cases 1032 368  143  1543
## [2,] Percent   66.9 23.8 9.3  100

15. “I accumulated debts” (dichotomous, v1_mss_itm15)

v1_mss_recode(v1_clin$v1_mss_s1_mss15_schulden,v1_con$v1_mss_s1_mss15,"v1_mss_itm15")
##                N    Y   <NA>     
## [1,] No. cases 1276 124 143  1543
## [2,] Percent   82.7 8   9.3  100

16. “I made unwise business decisions” (dichotomous, v1_mss_itm16)

v1_mss_recode(v1_clin$v1_mss_s1_mss16_unkluge_entsch,v1_con$v1_mss_s1_mss16,"v1_mss_itm16")
##                N    Y   <NA>     
## [1,] No. cases 1323 74  146  1543
## [2,] Percent   85.7 4.8 9.5  100

17. “I partied more” (dichotomous, v1_mss_itm17)

v1_mss_recode(v1_clin$v1_mss_s1_mss17_parties,v1_con$v1_mss_s1_mss17,"v1_mss_itm17")
##                N    Y   <NA>     
## [1,] No. cases 1296 101 146  1543
## [2,] Percent   84   6.5 9.5  100

18. “I enjoyed flirting” (dichotomous, v1_mss_itm18)

v1_mss_recode(v1_clin$v1_mss_s1_mss18_flirten,v1_con$v1_mss_s1_mss18,"v1_mss_itm18")
##                N    Y    <NA>     
## [1,] No. cases 1244 158  141  1543
## [2,] Percent   80.6 10.2 9.1  100

19. “I masturbated more often” (dichotomous, v1_mss_itm19)

v1_mss_recode(v1_clin$v1_mss_s2_mss19_selbstbefried,v1_con$v1_mss_s2_mss19,"v1_mss_itm19")
##                N    Y   <NA>     
## [1,] No. cases 1265 108 170  1543
## [2,] Percent   82   7   11   100

20. “I was more interested in sex than usual” (dichotomous, v1_mss_itm20)

v1_mss_recode(v1_clin$v1_mss_s2_mss20_sex_interess,v1_con$v1_mss_s2_mss20,"v1_mss_itm20")
##                N    Y    <NA>     
## [1,] No. cases 1209 165  169  1543
## [2,] Percent   78.4 10.7 11   100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v1_mss_itm21)

v1_mss_recode(v1_clin$v1_mss_s2_mss21_sexpartner,v1_con$v1_mss_s2_mss21,"v1_mss_itm21")
##                N    Y   <NA>     
## [1,] No. cases 1328 45  170  1543
## [2,] Percent   86.1 2.9 11   100

22. “I spent more time on the phone” (dichotomous, v1_mss_itm22)

v1_mss_recode(v1_clin$v1_mss_s2_mss22_mehr_telefon,v1_con$v1_mss_s2_mss22,"v1_mss_itm22")
##                N    Y    <NA>     
## [1,] No. cases 1117 266  160  1543
## [2,] Percent   72.4 17.2 10.4 100

23. “I spoke louder than usual” (dichotomous, v1_mss_itm23)

v1_mss_recode(v1_clin$v1_mss_s2_mss23_sprache_lauter,v1_con$v1_mss_s2_mss23,"v1_mss_itm23")
##                N    Y    <NA>     
## [1,] No. cases 1193 190  160  1543
## [2,] Percent   77.3 12.3 10.4 100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v1_mss_itm24)

v1_mss_recode(v1_clin$v1_mss_s2_mss24_spr_schneller,v1_con$v1_mss_s2_mss24,"v1_mss_itm24")
##                N    Y    <NA>     
## [1,] No. cases 1220 160  163  1543
## [2,] Percent   79.1 10.4 10.6 100

25. “1 enjoyed punning or rhyming” (dichotomous, v1_mss_itm25)

v1_mss_recode(v1_clin$v1_mss_s2_mss25_witze,v1_con$v1_mss_s2_mss25,"v1_mss_itm25")
##                N    Y   <NA>     
## [1,] No. cases 1197 185 161  1543
## [2,] Percent   77.6 12  10.4 100

26. “I butted into conversations” (dichotomous, v1_mss_itm26)

v1_mss_recode(v1_clin$v1_mss_s2_mss26_einmischen,v1_con$v1_mss_s2_mss26,"v1_mss_itm26")
##                N    Y   <NA>     
## [1,] No. cases 1237 146 160  1543
## [2,] Percent   80.2 9.5 10.4 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v1_mss_itm27)

v1_mss_recode(v1_clin$v1_mss_s2_mss27_red_pausenlos,v1_con$v1_mss_s2_mss27,"v1_mss_itm27")
##                N    Y   <NA>     
## [1,] No. cases 1298 83  162  1543
## [2,] Percent   84.1 5.4 10.5 100

28. “I enjoyed being the centre of attention” (dichotomous, v1_mss_itm28)

v1_mss_recode(v1_clin$v1_mss_s2_mss28_mittelpunkt,v1_con$v1_mss_s2_mss28,"v1_mss_itm28")
##                N    Y   <NA>     
## [1,] No. cases 1235 146 162  1543
## [2,] Percent   80   9.5 10.5 100

29. “I liked to joke and laugh” (dichotomous, v1_mss_itm29)

v1_mss_recode(v1_clin$v1_mss_s2_mss29_herumalbern,v1_con$v1_mss_s2_mss29,"v1_mss_itm29")
##                N    Y    <NA>     
## [1,] No. cases 1128 253  162  1543
## [2,] Percent   73.1 16.4 10.5 100

30. “People found me entertaining” (dichotomous, v1_mss_itm30)

v1_mss_recode(v1_clin$v1_mss_s2_mss30_unterhaltsamer,v1_con$v1_mss_s2_mss30,"v1_mss_itm30")
##                N    Y    <NA>     
## [1,] No. cases 1182 190  171  1543
## [2,] Percent   76.6 12.3 11.1 100

31. “I felt as if I was on top of the world” (dichotomous, v1_mss_itm31)

v1_mss_recode(v1_clin$v1_mss_s2_mss31_obenauf,v1_con$v1_mss_s2_mss31,"v1_mss_itm31")
##                N    Y   <NA>     
## [1,] No. cases 1210 170 163  1543
## [2,] Percent   78.4 11  10.6 100

32. “I was more cheerful than my usual self” (dichotomous, v1_mss_itm32)

v1_mss_recode(v1_clin$v1_mss_s2_mss32_froehlicher,v1_con$v1_mss_s2_mss32,"v1_mss_itm32")
##                N    Y    <NA>     
## [1,] No. cases 1068 313  162  1543
## [2,] Percent   69.2 20.3 10.5 100

33. “Other people got on my nerves” (dichotomous, v1_mss_itm33)

v1_mss_recode(v1_clin$v1_mss_s2_mss33_ungeduldiger,v1_con$v1_mss_s2_mss33,"v1_mss_itm33")
##                N    Y    <NA>     
## [1,] No. cases 976  405  162  1543
## [2,] Percent   63.3 26.2 10.5 100

34. “I was getting into arguments” (dichotomous, v1_mss_itm34)

v1_mss_recode(v1_clin$v1_mss_s2_mss34_streiten,v1_con$v1_mss_s2_mss34,"v1_mss_itm34")
##                N    Y   <NA>     
## [1,] No. cases 1191 185 167  1543
## [2,] Percent   77.2 12  10.8 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v1_mss_itm35)

v1_mss_recode(v1_clin$v1_mss_s2_mss35_ideen,v1_con$v1_mss_s2_mss35,"v1_mss_itm35")
##                N    Y    <NA>     
## [1,] No. cases 1051 330  162  1543
## [2,] Percent   68.1 21.4 10.5 100

36. “My thoughts raced through my mind” (dichotomous, v1_mss_itm36)

v1_mss_recode(v1_clin$v1_mss_s2_mss36_gedanken,v1_con$v1_mss_s2_mss36,"v1_mss_itm36")
##                N    Y    <NA>     
## [1,] No. cases 914  466  163  1543
## [2,] Percent   59.2 30.2 10.6 100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v1_mss_itm37)

v1_mss_recode(v1_clin$v1_mss_s2_mss37_konzentration,v1_con$v1_mss_s2_mss37,"v1_mss_itm37")
##                N    Y    <NA>     
## [1,] No. cases 1093 284  166  1543
## [2,] Percent   70.8 18.4 10.8 100

38. “I thought I was an especially important person” (dichotomous, v1_mss_itm38)

v1_mss_recode(v1_clin$v1_mss_s2_mss38_etw_besonderes,v1_con$v1_mss_s2_mss38,"v1_mss_itm38")
##                N    Y    <NA>     
## [1,] No. cases 1227 157  159  1543
## [2,] Percent   79.5 10.2 10.3 100

39. “I thought I could change the world” (dichotomous, v1_mss_itm39)

v1_mss_recode(v1_clin$v1_mss_s2_mss39_welt_veraender,v1_con$v1_mss_s2_mss39,"v1_mss_itm39")
##                N    Y   <NA>     
## [1,] No. cases 1245 135 163  1543
## [2,] Percent   80.7 8.7 10.6 100

40. “I thought I was right most of the time” (dichotomous, v1_mss_itm40)

v1_mss_recode(v1_clin$v1_mss_s2_mss40_recht_haben,v1_con$v1_mss_s2_mss40,"v1_mss_itm40")
##                N    Y   <NA>     
## [1,] No. cases 1258 123 162  1543
## [2,] Percent   81.5 8   10.5 100

41. “I thought I was superior to others” (dichotomous, v1_mss_itm41)

v1_mss_recode(v1_clin$v1_mss_s3_mss41_ueberlegen,v1_con$v1_mss_s3_mss41,"v1_mss_itm41")
##                N    Y   <NA>     
## [1,] No. cases 1281 90  172  1543
## [2,] Percent   83   5.8 11.1 100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v1_mss_itm42)

v1_mss_recode(v1_clin$v1_mss_s3_mss42_uebermut,v1_con$v1_mss_s3_mss42,"v1_mss_itm42")
##                N    Y   <NA>     
## [1,] No. cases 1215 155 173  1543
## [2,] Percent   78.7 10  11.2 100

43. “I thought I knew what other people were thinking” (dichotomous, v1_mss_itm43)

v1_mss_recode(v1_clin$v1_mss_s3_mss43_ged_lesen_akt,v1_con$v1_mss_s3_mss43,"v1_mss_itm43")
##                N    Y   <NA>     
## [1,] No. cases 1201 169 173  1543
## [2,] Percent   77.8 11  11.2 100

44. “I thought other people knew what I was thinking” (dichotomous, v1_mss_itm44)

v1_mss_recode(v1_clin$v1_mss_s3_mss44_ged_lesen_pas,v1_con$v1_mss_s3_mss44,"v1_mss_itm44")
##                N    Y   <NA>     
## [1,] No. cases 1242 125 176  1543
## [2,] Percent   80.5 8.1 11.4 100

45. “I thought someone wanted to harm me” (dichotomous, v1_mss_itm45)

v1_mss_recode(v1_clin$v1_mss_s3_mss45_etw_antun,v1_con$v1_mss_s3_mss45,"v1_mss_itm45")
##                N    Y   <NA>     
## [1,] No. cases 1242 127 174  1543
## [2,] Percent   80.5 8.2 11.3 100

46. “I heard voices when people weren’t there” (dichotomous, v1_mss_itm46)

v1_mss_recode(v1_clin$v1_mss_s3_mss46_stimmen,v1_con$v1_mss_s3_mss46,"v1_mss_itm46")
##                N    Y   <NA>     
## [1,] No. cases 1230 140 173  1543
## [2,] Percent   79.7 9.1 11.2 100

47. “I had false beliefs concerning who I was” (dichotomous, v1_mss_itm47)

v1_mss_recode(v1_clin$v1_mss_s3_mss47_jmd_anders,v1_con$v1_mss_s3_mss47,"v1_mss_itm47")
##                N    Y  <NA>     
## [1,] No. cases 1309 62 172  1543
## [2,] Percent   84.8 4  11.1 100

48. “I knew I was getting ill” (dichotomous, v1_mss_itm48)

v1_mss_recode(v1_clin$v1_mss_s3_mss48_krank_einsicht,v1_con$v1_mss_s3_mss48,"v1_mss_itm48")
##                N    Y    <NA>     
## [1,] No. cases 1048 307  188  1543
## [2,] Percent   67.9 19.9 12.2 100

Create MSS sum score (continuous [0-48],v1_mss_sum) Please note that if one or more of MSS items are missing, this will result in the sum score to become NA.

v1_mss_sum<-ifelse(v1_mss_itm1=="Y",1,0)+
            ifelse(v1_mss_itm2=="Y",1,0)+
            ifelse(v1_mss_itm3=="Y",1,0)+
            ifelse(v1_mss_itm4=="Y",1,0)+
            ifelse(v1_mss_itm5=="Y",1,0)+
            ifelse(v1_mss_itm6=="Y",1,0)+
            ifelse(v1_mss_itm7=="Y",1,0)+
            ifelse(v1_mss_itm8=="Y",1,0)+
            ifelse(v1_mss_itm9=="Y",1,0)+
            ifelse(v1_mss_itm10=="Y",1,0)+
            ifelse(v1_mss_itm11=="Y",1,0)+
            ifelse(v1_mss_itm12=="Y",1,0)+
            ifelse(v1_mss_itm13=="Y",1,0)+
            ifelse(v1_mss_itm14=="Y",1,0)+
            ifelse(v1_mss_itm15=="Y",1,0)+
            ifelse(v1_mss_itm16=="Y",1,0)+
            ifelse(v1_mss_itm17=="Y",1,0)+
            ifelse(v1_mss_itm18=="Y",1,0)+
            ifelse(v1_mss_itm19=="Y",1,0)+
            ifelse(v1_mss_itm20=="Y",1,0)+
            ifelse(v1_mss_itm21=="Y",1,0)+
            ifelse(v1_mss_itm22=="Y",1,0)+
            ifelse(v1_mss_itm23=="Y",1,0)+
            ifelse(v1_mss_itm24=="Y",1,0)+
            ifelse(v1_mss_itm25=="Y",1,0)+
            ifelse(v1_mss_itm26=="Y",1,0)+
            ifelse(v1_mss_itm27=="Y",1,0)+
            ifelse(v1_mss_itm28=="Y",1,0)+
            ifelse(v1_mss_itm29=="Y",1,0)+
            ifelse(v1_mss_itm30=="Y",1,0)+
            ifelse(v1_mss_itm31=="Y",1,0)+
            ifelse(v1_mss_itm32=="Y",1,0)+
            ifelse(v1_mss_itm33=="Y",1,0)+
            ifelse(v1_mss_itm34=="Y",1,0)+
            ifelse(v1_mss_itm35=="Y",1,0)+
            ifelse(v1_mss_itm36=="Y",1,0)+
            ifelse(v1_mss_itm37=="Y",1,0)+
            ifelse(v1_mss_itm38=="Y",1,0)+
            ifelse(v1_mss_itm39=="Y",1,0)+
            ifelse(v1_mss_itm40=="Y",1,0)+
            ifelse(v1_mss_itm41=="Y",1,0)+
            ifelse(v1_mss_itm42=="Y",1,0)+
            ifelse(v1_mss_itm43=="Y",1,0)+
            ifelse(v1_mss_itm44=="Y",1,0)+
            ifelse(v1_mss_itm45=="Y",1,0)+
            ifelse(v1_mss_itm46=="Y",1,0)+
            ifelse(v1_mss_itm47=="Y",1,0)+
            ifelse(v1_mss_itm48=="Y",1,0)

summary(v1_mss_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   4.000   6.051   9.000  39.000     412

Create dataset

v1_mss<-data.frame(v1_mss_itm1,v1_mss_itm2,v1_mss_itm3,v1_mss_itm4,v1_mss_itm5,v1_mss_itm6,
                   v1_mss_itm7,v1_mss_itm8,v1_mss_itm9,v1_mss_itm10,v1_mss_itm11,
                   v1_mss_itm12,v1_mss_itm13,v1_mss_itm14,v1_mss_itm15,v1_mss_itm16,
                   v1_mss_itm17,v1_mss_itm18,v1_mss_itm19,v1_mss_itm20,v1_mss_itm21,
                   v1_mss_itm22,v1_mss_itm23,v1_mss_itm24,v1_mss_itm25,v1_mss_itm26,
                   v1_mss_itm27,v1_mss_itm28,v1_mss_itm29,v1_mss_itm30,v1_mss_itm31,
                   v1_mss_itm32,v1_mss_itm33,v1_mss_itm34,v1_mss_itm35,v1_mss_itm36,
                   v1_mss_itm37,v1_mss_itm38,v1_mss_itm39,v1_mss_itm40,v1_mss_itm41,
                   v1_mss_itm42,v1_mss_itm43,v1_mss_itm44,v1_mss_itm45,v1_mss_itm46,
                   v1_mss_itm47,v1_mss_itm48, v1_mss_sum)

Life Events Questionnaire (LEQ)

In this questionnaire (Norbeck, 1984; Sarason, Johnson, & Siegel, 1978) many possible life events are listed (e.g. “Difficulties finding work”) from the following areas: health, work, school, residence, love and marriage, family and close friends, parenting, personal or social, financial, crime and legal matters, and other. Participants are supposed to answer only to those life events which they have experienced during the past six months. For these particular events, participants were asked to rate:

  1. the nature of the influence (good/bad) and
  2. the impact of the event in question on her/his life (0-3).

As participants usually only experience relatively few life events during the follow-up period, most of the items are not filled out. We have coded empty items as “-999” in people that filled out the questionnaire correctly. In participants that did not fill out the questionaire at all or filled it out obviously wrong (e.g. answering every question, regardless whether they experienced the life event or not), all items are “NA”.

The questionaire is divided in ten separate sections (A-“Health”, B-“Work”, C-“School”, D-“Residence”, E-“Love and marriage”, F-“Family and close friends”, G-“Parenting”, H-“Personal or social”, I-“Financial”, J-“Crime and legal matters”). The respective sections are contained in the item name.

Health

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v1_leq_A_1A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq1a_schw_krankh,v1_con$v1_leq_a_leq1a,"v1_leq_A_1A")
##                -999 bad  good <NA>     
## [1,] No. cases 796  409  78   260  1543
## [2,] Percent   51.6 26.5 5.1  16.9 100

1B Impact (ordinal [0,1,2,3], v1_leq_A_1B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq1e_schw_krankh,v1_con$v1_leq_a_leq1e,"v1_leq_A_1B")
##                -999 0   1   2   3    <NA>     
## [1,] No. cases 787  22  37  108 329  260  1543
## [2,] Percent   51   1.4 2.4 7   21.3 16.9 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v1_leq_A_2A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq2a_ernaehrung,v1_con$v1_leq_a_leq2a,"v1_leq_A_2A")
##                -999 bad  good <NA>     
## [1,] No. cases 869  193  221  260  1543
## [2,] Percent   56.3 12.5 14.3 16.9 100

2B Impact (ordinal [0,1,2,3], v1_leq_A_2B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq2e_ernaehrung,v1_con$v1_leq_a_leq2e,"v1_leq_A_2B")
##                -999 0   1   2    3   <NA>     
## [1,] No. cases 858  35  80  174  136 260  1543
## [2,] Percent   55.6 2.3 5.2 11.3 8.8 16.9 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v1_leq_A_3A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq3a_schlaf,v1_con$v1_leq_a_leq3a,"v1_leq_A_3A")
##                -999 bad  good <NA>     
## [1,] No. cases 811  317  155  260  1543
## [2,] Percent   52.6 20.5 10   16.9 100

3B Impact (ordinal [0,1,2,3], v1_leq_A_3B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq3e_schlaf,v1_con$v1_leq_a_leq3e,"v1_leq_A_3B")
##                -999 0   1   2   3    <NA>     
## [1,] No. cases 802  26  100 169 186  260  1543
## [2,] Percent   52   1.7 6.5 11  12.1 16.9 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v1_leq_A_4A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq4a_freizeit,v1_con$v1_leq_a_leq4a,"v1_leq_A_4A")
##                -999 bad  good <NA>     
## [1,] No. cases 777  242  264  260  1543
## [2,] Percent   50.4 15.7 17.1 16.9 100

4B Impact (ordinal [0,1,2,3], v1_leq_A_4B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq4e_freizeit,v1_con$v1_leq_a_leq4e,"v1_leq_A_4B")
##                -999 0  1   2    3   <NA>     
## [1,] No. cases 768  31 95  204  185 260  1543
## [2,] Percent   49.8 2  6.2 13.2 12  16.9 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v1_leq_A_5A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq5a_zahnarzt,v1_con$v1_leq_a_leq5a,"v1_leq_A_5A")
##                -999 bad good <NA>     
## [1,] No. cases 1076 87  120  260  1543
## [2,] Percent   69.7 5.6 7.8  16.9 100

5B Impact (ordinal [0,1,2,3], v1_leq_A_5B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq5e_zahnarzt,v1_con$v1_leq_a_leq5e,"v1_leq_A_5B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1061 57  49  63  53  260  1543
## [2,] Percent   68.8 3.7 3.2 4.1 3.4 16.9 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v1_leq_A_6A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq6a_schwanger,v1_con$v1_leq_a_leq6a,"v1_leq_A_6A")
##                -999 bad good <NA>     
## [1,] No. cases 1259 7   17   260  1543
## [2,] Percent   81.6 0.5 1.1  16.9 100

6B Impact (ordinal [0,1,2,3], v1_leq_A_6B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq6e_schwanger,v1_con$v1_leq_a_leq6e,"v1_leq_A_6B")
##                -999 0   2   3  <NA>     
## [1,] No. cases 1258 7   3   15 260  1543
## [2,] Percent   81.5 0.5 0.2 1  16.9 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v1_leq_A_7A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq7a_fehlg_abtr,v1_con$v1_leq_a_leq7a,"v1_leq_A_7A")
##                -999 bad good <NA>     
## [1,] No. cases 1271 9   3    260  1543
## [2,] Percent   82.4 0.6 0.2  16.9 100

7B Impact (ordinal [0,1,2,3], v1_leq_A_7B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq7e_fehlg_abtr,v1_con$v1_leq_a_leq7e,"v1_leq_A_7B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1270 4   2   1   6   260  1543
## [2,] Percent   82.3 0.3 0.1 0.1 0.4 16.9 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v1_leq_A_8A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq8a_wechseljahre,v1_con$v1_leq_a_leq8a,"v1_leq_A_8A")
##                -999 bad good <NA>     
## [1,] No. cases 1232 35  16   260  1543
## [2,] Percent   79.8 2.3 1    16.9 100

8B Impact (ordinal [0,1,2,3], v1_leq_A_8B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq8e_wechseljahre,v1_con$v1_leq_a_leq8e,"v1_leq_A_8B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1229 9   8   19  18  260  1543
## [2,] Percent   79.7 0.6 0.5 1.2 1.2 16.9 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v1_leq_A_9A)

v1_leq_a_recode(v1_clin$v1_leq_a_leq9a_verhuetung,v1_con$v1_leq_a_leq9a,"v1_leq_A_9A")
##                -999 bad good <NA>     
## [1,] No. cases 1234 36  13   260  1543
## [2,] Percent   80   2.3 0.8  16.9 100

9B Impact (ordinal [0,1,2,3], v1_leq_A_9B)

v1_leq_b_recode(v1_clin$v1_leq_a_leq9e_verhuetung,v1_con$v1_leq_a_leq9e,"v1_leq_A_9B")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 1231 15 12  8   17  260  1543
## [2,] Percent   79.8 1  0.8 0.5 1.1 16.9 100

Create dataset

v1_leq_A<-data.frame(v1_leq_A_1A,v1_leq_A_1B,v1_leq_A_2A,v1_leq_A_2B,v1_leq_A_3A,
                     v1_leq_A_3B,v1_leq_A_4A,v1_leq_A_4B,v1_leq_A_5A,v1_leq_A_5B,
                     v1_leq_A_6A,v1_leq_A_6B,v1_leq_A_7A,v1_leq_A_7B,v1_leq_A_8A,
                     v1_leq_A_8B,v1_leq_A_9A,v1_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v1_leq_B_10A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq10a_arbeitssuche,v1_con$v1_leq_b_leq10a,"v1_leq_B_10A")
##                -999 bad  good <NA>     
## [1,] No. cases 1058 180  45   260  1543
## [2,] Percent   68.6 11.7 2.9  16.9 100

10B Impact (ordinal [0,1,2,3], v1_leq_B_10B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq10e_arbeitssuche,v1_con$v1_leq_b_leq10e,"v1_leq_B_10B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1049 22  40  62 110 260  1543
## [2,] Percent   68   1.4 2.6 4  7.1 16.9 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v1_leq_B_11A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq11a_arbeit_aussen,v1_con$v1_leq_b_leq11a,"v1_leq_B_11A")
##                -999 bad good <NA>     
## [1,] No. cases 1092 65  126  260  1543
## [2,] Percent   70.8 4.2 8.2  16.9 100

11B Impact (ordinal [0,1,2,3], v1_leq_B_11B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq11e_arbeit_aussen,v1_con$v1_leq_b_leq11e,"v1_leq_B_11B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1087 20  38  52  86  260  1543
## [2,] Percent   70.4 1.3 2.5 3.4 5.6 16.9 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v1_leq_B_12A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq12a_arbeitswechs,v1_con$v1_leq_b_leq12a,"v1_leq_B_12A")
##                -999 bad good <NA>     
## [1,] No. cases 1067 57  159  260  1543
## [2,] Percent   69.2 3.7 10.3 16.9 100

12B Impact (ordinal [0,1,2,3], v1_leq_B_12B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq12e_arbeitswechs,v1_con$v1_leq_b_leq12e,"v1_leq_B_12B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1064 13  44  72  90  260  1543
## [2,] Percent   69   0.8 2.9 4.7 5.8 16.9 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v1_leq_B_13A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq13a_veraend_arb,v1_con$v1_leq_b_leq13a,"v1_leq_B_13A")
##                -999 bad good <NA>     
## [1,] No. cases 1029 95  159  260  1543
## [2,] Percent   66.7 6.2 10.3 16.9 100

13B Impact (ordinal [0,1,2,3], v1_leq_B_13B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq13e_veraend_arb,v1_con$v1_leq_b_leq13e,"v1_leq_B_13B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1026 11  66  83  97  260  1543
## [2,] Percent   66.5 0.7 4.3 5.4 6.3 16.9 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v1_leq_B_14A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq14a_veraend_ba,v1_con$v1_leq_b_leq14a,"v1_leq_B_14A")
##                -999 bad good <NA>     
## [1,] No. cases 1017 81  185  260  1543
## [2,] Percent   65.9 5.2 12   16.9 100

14B Impact (ordinal [0,1,2,3], v1_leq_B_14B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq14e_veraend_ba,v1_con$v1_leq_b_leq14e,"v1_leq_B_14B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1016 18  57  85  107 260  1543
## [2,] Percent   65.8 1.2 3.7 5.5 6.9 16.9 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v1_leq_B_15A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq15a_schw_arbeit,v1_con$v1_leq_b_leq15a,"v1_leq_B_15A")
##                -999 bad  good <NA>     
## [1,] No. cases 1079 181  23   260  1543
## [2,] Percent   69.9 11.7 1.5  16.9 100

15B Impact (ordinal [0,1,2,3], v1_leq_B_15B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq15e_schw_arbeit,v1_con$v1_leq_b_leq15e,"v1_leq_B_15B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1077 23  50  58  75  260  1543
## [2,] Percent   69.8 1.5 3.2 3.8 4.9 16.9 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v1_leq_B_16A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq16a_betr_reorg,v1_con$v1_leq_b_leq16a,"v1_leq_B_16A")
##                -999 bad good <NA>     
## [1,] No. cases 1214 38  31   260  1543
## [2,] Percent   78.7 2.5 2    16.9 100

16B Impact (ordinal [0,1,2,3], v1_leq_B_16B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq16e_betr_reorg,v1_con$v1_leq_b_leq16e,"v1_leq_B_16B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1210 12  20  19  22  260  1543
## [2,] Percent   78.4 0.8 1.3 1.2 1.4 16.9 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v1_leq_B_17A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq17a_kuendigung,v1_con$v1_leq_b_leq17a,"v1_leq_B_17A")
##                -999 bad good <NA>     
## [1,] No. cases 1165 90  28   260  1543
## [2,] Percent   75.5 5.8 1.8  16.9 100

17B Impact (ordinal [0,1,2,3], v1_leq_B_17B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq17e_kuendigung,v1_con$v1_leq_b_leq17e,"v1_leq_B_17B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1163 13  14  28  65  260  1543
## [2,] Percent   75.4 0.8 0.9 1.8 4.2 16.9 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v1_leq_B_18A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq18a_ende_beruf,v1_con$v1_leq_b_leq18a,"v1_leq_B_18A")
##                -999 bad good <NA>     
## [1,] No. cases 1206 44  33   260  1543
## [2,] Percent   78.2 2.9 2.1  16.9 100

18B Impact (ordinal [0,1,2,3], v1_leq_B_18B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq18e_ende_beruf,v1_con$v1_leq_b_leq18e,"v1_leq_B_18B")
##                -999 0   1   2  3  <NA>     
## [1,] No. cases 1203 11  6   16 47 260  1543
## [2,] Percent   78   0.7 0.4 1  3  16.9 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v1_leq_B_19A)

v1_leq_a_recode(v1_clin$v1_leq_b_leq19a_fortbildung,v1_con$v1_leq_b_leq19a,"v1_leq_B_19A")
##                -999 bad good <NA>     
## [1,] No. cases 1179 21  83   260  1543
## [2,] Percent   76.4 1.4 5.4  16.9 100

19B Impact (ordinal [0,1,2,3], v1_leq_B_19B)

v1_leq_b_recode(v1_clin$v1_leq_b_leq19e_fortbildung,v1_con$v1_leq_b_leq19e,"v1_leq_B_19B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1176 18  16 36  37  260  1543
## [2,] Percent   76.2 1.2 1  2.3 2.4 16.9 100
v1_leq_B<-data.frame(v1_leq_B_10A,v1_leq_B_10B,v1_leq_B_11A,v1_leq_B_11B,v1_leq_B_12A,
                     v1_leq_B_12B,v1_leq_B_13A,v1_leq_B_13B,v1_leq_B_14A,v1_leq_B_14B,
                     v1_leq_B_15A,v1_leq_B_15B,v1_leq_B_16A,v1_leq_B_16B,v1_leq_B_17A,
                     v1_leq_B_17B,v1_leq_B_18A,v1_leq_B_18B,v1_leq_B_19A,v1_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v1_leq_C_20A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq20a_beginn_ende,v1_con$v1_leq_c_d_leq20a,"v1_leq_C_20A")
##                -999 bad good <NA>     
## [1,] No. cases 1168 35  80   260  1543
## [2,] Percent   75.7 2.3 5.2  16.9 100

20B Impact (ordinal [0,1,2,3], v1_leq_C_20B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq20e_beginn_ende,v1_con$v1_leq_c_d_leq20e,"v1_leq_C_20B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1166 5   20  29  63  260  1543
## [2,] Percent   75.6 0.3 1.3 1.9 4.1 16.9 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v1_leq_C_21A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq21a_schulwechsel,v1_con$v1_leq_c_d_leq21a,"v1_leq_C_21A")
##                -999 bad good <NA>     
## [1,] No. cases 1243 12  28   260  1543
## [2,] Percent   80.6 0.8 1.8  16.9 100

21B Impact (ordinal [0,1,2,3], v1_leq_C_21B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq21e_schulwechsel,v1_con$v1_leq_c_d_leq21e,"v1_leq_C_21B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1240 5   7   17  14  260  1543
## [2,] Percent   80.4 0.3 0.5 1.1 0.9 16.9 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v1_leq_C_22A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq22a_aend_karriere,v1_con$v1_leq_c_d_leq22a,"v1_leq_C_22A")
##                -999 bad good <NA>     
## [1,] No. cases 1191 21  71   260  1543
## [2,] Percent   77.2 1.4 4.6  16.9 100

B Impact (ordinal [0,1,2,3], v1_leq_C_22B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq22e_aend_karriere,v1_con$v1_leq_c_d_leq22e,"v1_leq_C_22B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1188 6   17  27  45  260  1543
## [2,] Percent   77   0.4 1.1 1.7 2.9 16.9 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v1_leq_C_23A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq23a_schulprob,v1_con$v1_leq_c_d_leq23a,"v1_leq_C_23A")
##                -999 bad good <NA>     
## [1,] No. cases 1201 73  9    260  1543
## [2,] Percent   77.8 4.7 0.6  16.9 100

23B Impact (ordinal [0,1,2,3], v1_leq_C_23B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq23e_schulprob,v1_con$v1_leq_c_d_leq23e,"v1_leq_C_23B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1199 7   17  27  33  260  1543
## [2,] Percent   77.7 0.5 1.1 1.7 2.1 16.9 100

Create dataset

v1_leq_C<-data.frame(v1_leq_C_20A,v1_leq_C_20B,v1_leq_C_21A,v1_leq_C_21B,v1_leq_C_22A,v1_leq_C_22B,v1_leq_C_23A,v1_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v1_leq_D_24A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq24a_schw_wsuche,v1_con$v1_leq_c_d_leq24a,"v1_leq_D_24A")
##                -999 bad good <NA>     
## [1,] No. cases 1116 139 28   260  1543
## [2,] Percent   72.3 9   1.8  16.9 100

24B Impact (ordinal [0,1,2,3], v1_leq_D_24B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq24e_schw_wsuche,v1_con$v1_leq_c_d_leq24e,"v1_leq_D_24B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1111 18  43  51  60  260  1543
## [2,] Percent   72   1.2 2.8 3.3 3.9 16.9 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v1_leq_D_25A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq25a_umzug_nah,v1_con$v1_leq_c_d_leq25a,"v1_leq_D_25A")
##                -999 bad good <NA>     
## [1,] No. cases 1142 35  106  260  1543
## [2,] Percent   74   2.3 6.9  16.9 100

B Impact (ordinal [0,1,2,3], v1_leq_D_25B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq25e_umzug_nah,v1_con$v1_leq_c_d_leq25e,"v1_leq_D_25B")
##                -999 0  1   2   3  <NA>     
## [1,] No. cases 1140 15 26  41  61 260  1543
## [2,] Percent   73.9 1  1.7 2.7 4  16.9 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v1_leq_D_26A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq26a_umzug_fern,v1_con$v1_leq_c_d_leq26a,"v1_leq_D_26A")
##                -999 bad good <NA>     
## [1,] No. cases 1168 37  78   260  1543
## [2,] Percent   75.7 2.4 5.1  16.9 100

26B Impact (ordinal [0,1,2,3], v1_leq_D_26B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq26e_umzug_fern,v1_con$v1_leq_c_d_leq26e,"v1_leq_D_26B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1162 13  11  32  65  260  1543
## [2,] Percent   75.3 0.8 0.7 2.1 4.2 16.9 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v1_leq_D_27A)

v1_leq_a_recode(v1_clin$v1_leq_c_d_leq27a_veraend_lu,v1_con$v1_leq_c_d_leq27a,"v1_leq_D_27A")
##                -999 bad good <NA>     
## [1,] No. cases 1029 111 143  260  1543
## [2,] Percent   66.7 7.2 9.3  16.9 100

27B Impact (ordinal [0,1,2,3], v1_leq_D_27B)

v1_leq_b_recode(v1_clin$v1_leq_c_d_leq27e_veraend_lu,v1_con$v1_leq_c_d_leq27e,"v1_leq_D_27B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1023 20  49  69  122 260  1543
## [2,] Percent   66.3 1.3 3.2 4.5 7.9 16.9 100

Create dataset

v1_leq_D<-data.frame(v1_leq_D_24A,v1_leq_D_24B,v1_leq_D_25A,v1_leq_D_25B,v1_leq_D_26A,
                     v1_leq_D_26B,v1_leq_D_27A,v1_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v1_leq_E_28A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq28a_neue_bez,v1_con$v1_leq_e_leq28a,"v1_leq_E_28A")
##                -999 bad good <NA>     
## [1,] No. cases 1100 29  154  260  1543
## [2,] Percent   71.3 1.9 10   16.9 100

28B Impact (ordinal [0,1,2,3], v1_leq_E_28B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq28e_neue_bez,v1_con$v1_leq_e_leq28e,"v1_leq_E_28B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1096 9   28  45  105 260  1543
## [2,] Percent   71   0.6 1.8 2.9 6.8 16.9 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v1_leq_E_29A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq29a_verlobung,v1_con$v1_leq_e_leq29a,"v1_leq_E_29A")
##                -999 bad good <NA>     
## [1,] No. cases 1251 8   24   260  1543
## [2,] Percent   81.1 0.5 1.6  16.9 100

29B Impact (ordinal [0,1,2,3], v1_leq_E_29B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq29e_verlobung,v1_con$v1_leq_e_leq29e,"v1_leq_E_29B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1249 3   4   8   19  260  1543
## [2,] Percent   80.9 0.2 0.3 0.5 1.2 16.9 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v1_leq_E_30A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq30a_prob_partner,v1_con$v1_leq_e_leq30a,"v1_leq_E_30A")
##                -999 bad  good <NA>     
## [1,] No. cases 1068 190  25   260  1543
## [2,] Percent   69.2 12.3 1.6  16.9 100

30B Impact (ordinal [0,1,2,3], v1_leq_E_30B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq30e_prob_partner,v1_con$v1_leq_e_leq30e,"v1_leq_E_30B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1065 6   39  84  89  260  1543
## [2,] Percent   69   0.4 2.5 5.4 5.8 16.9 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v1_leq_E_31A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq31a_trennung,v1_con$v1_leq_e_leq31a,"v1_leq_E_31A")
##                -999 bad good <NA>     
## [1,] No. cases 1149 103 31   260  1543
## [2,] Percent   74.5 6.7 2    16.9 100

31B Impact (ordinal [0,1,2,3], v1_leq_E_31B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq31e_trennung,v1_con$v1_leq_e_leq31e,"v1_leq_E_31B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1144 10  21  45  63  260  1543
## [2,] Percent   74.1 0.6 1.4 2.9 4.1 16.9 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v1_leq_E_32A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq32a_schwanger_p,v1_con$v1_leq_e_leq32a,"v1_leq_E_32A")
##                -999 bad good <NA>     
## [1,] No. cases 1269 6   8    260  1543
## [2,] Percent   82.2 0.4 0.5  16.9 100

32B Impact (ordinal [0,1,2,3], v1_leq_E_32B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq32e_schwanger_p,v1_con$v1_leq_e_leq32e,"v1_leq_E_32B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1267 2   3   2   9   260  1543
## [2,] Percent   82.1 0.1 0.2 0.1 0.6 16.9 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v1_leq_E_33A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq33a_fehlg_abtr_p,v1_con$v1_leq_e_leq33a,"v1_leq_E_33A")
##                -999 bad good <NA>     
## [1,] No. cases 1272 9   2    260  1543
## [2,] Percent   82.4 0.6 0.1  16.9 100

33B Impact (ordinal [0,1,2,3], v1_leq_E_33B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq33e_fehlg_abtr_p,v1_con$v1_leq_e_leq33e,"v1_leq_E_33B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1271 5   2   1   4   260  1543
## [2,] Percent   82.4 0.3 0.1 0.1 0.3 16.9 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v1_leq_E_34A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq34a_heirat,v1_con$v1_leq_e_leq34a,"v1_leq_E_34A")
##                -999 bad good <NA>     
## [1,] No. cases 1244 5   34   260  1543
## [2,] Percent   80.6 0.3 2.2  16.9 100

34B Impact (ordinal [0,1,2,3], v1_leq_E_34B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq34e_heirat,v1_con$v1_leq_e_leq34e,"v1_leq_E_34B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1240 3   3   13  24  260  1543
## [2,] Percent   80.4 0.2 0.2 0.8 1.6 16.9 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v1_leq_E_35A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq35a_veraend_naehe,v1_con$v1_leq_e_leq35a,"v1_leq_E_35A")
##                -999 bad good <NA>     
## [1,] No. cases 1087 114 82   260  1543
## [2,] Percent   70.4 7.4 5.3  16.9 100

35B Impact (ordinal [0,1,2,3], v1_leq_E_35B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq35e_veraend_naehe,v1_con$v1_leq_e_leq35e,"v1_leq_E_35B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1082 6   29  71  95  260  1543
## [2,] Percent   70.1 0.4 1.9 4.6 6.2 16.9 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v1_leq_E_36A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq36a_untreue,v1_con$v1_leq_e_leq36a,"v1_leq_E_36A")
##                -999 bad good <NA>     
## [1,] No. cases 1224 49  10   260  1543
## [2,] Percent   79.3 3.2 0.6  16.9 100

36B Impact (ordinal [0,1,2,3], v1_leq_E_36B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq36e_untreue,v1_con$v1_leq_e_leq36e,"v1_leq_E_36B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1221 13  11  8   30  260  1543
## [2,] Percent   79.1 0.8 0.7 0.5 1.9 16.9 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v1_leq_E_37A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq37a_konf_schwiege,v1_con$v1_leq_e_leq37a,"v1_leq_E_37A")
##                -999 bad good <NA>     
## [1,] No. cases 1224 51  8    260  1543
## [2,] Percent   79.3 3.3 0.5  16.9 100

37B Impact (ordinal [0,1,2,3], v1_leq_E_37B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq37e_konf_schwiege,v1_con$v1_leq_e_leq37e,"v1_leq_E_37B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1222 4   19  25  13  260  1543
## [2,] Percent   79.2 0.3 1.2 1.6 0.8 16.9 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v1_leq_E_38A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq38a_trennung_str,v1_con$v1_leq_e_leq38a,"v1_leq_E_38A")
##                -999 bad good <NA>     
## [1,] No. cases 1221 45  17   260  1543
## [2,] Percent   79.1 2.9 1.1  16.9 100

38B Impact (ordinal [0,1,2,3], v1_leq_E_38B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq38e_trennung_str,v1_con$v1_leq_e_leq38e,"v1_leq_E_38B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1221 3   5   20  34  260  1543
## [2,] Percent   79.1 0.2 0.3 1.3 2.2 16.9 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v1_leq_E_39A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq39a_trennung_ber,v1_con$v1_leq_e_leq39a,"v1_leq_E_39A")
##                -999 bad <NA>     
## [1,] No. cases 1266 17  260  1543
## [2,] Percent   82   1.1 16.9 100

39B Impact (ordinal [0,1,2,3], v1_leq_E_39B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq39e_trennung_ber,v1_con$v1_leq_e_leq39e,"v1_leq_E_39B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1264 6   1   6   6   260  1543
## [2,] Percent   81.9 0.4 0.1 0.4 0.4 16.9 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v1_leq_E_40A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq40a_versoehnung,v1_con$v1_leq_e_leq40a,"v1_leq_E_40A")
##                -999 bad good <NA>     
## [1,] No. cases 1210 6   67   260  1543
## [2,] Percent   78.4 0.4 4.3  16.9 100

40B Impact (ordinal [0,1,2,3], v1_leq_E_40B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq40e_versoehnung,v1_con$v1_leq_e_leq40e,"v1_leq_E_40B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1209 4   13  20  37  260  1543
## [2,] Percent   78.4 0.3 0.8 1.3 2.4 16.9 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v1_leq_E_41A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq41a_scheidung,v1_con$v1_leq_e_leq41a,"v1_leq_E_41A")
##                -999 bad good <NA>     
## [1,] No. cases 1260 16  7    260  1543
## [2,] Percent   81.7 1   0.5  16.9 100

41B Impact (ordinal [0,1,2,3], v1_leq_E_41B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq41e_scheidung,v1_con$v1_leq_e_leq41e,"v1_leq_E_41B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1259 4   1   6   13  260  1543
## [2,] Percent   81.6 0.3 0.1 0.4 0.8 16.9 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v1_leq_E_42A)

v1_leq_a_recode(v1_clin$v1_leq_e_leq42a_veraend_taet,v1_con$v1_leq_e_leq42a,"v1_leq_E_42A")
##                -999 bad good <NA>     
## [1,] No. cases 1212 29  42   260  1543
## [2,] Percent   78.5 1.9 2.7  16.9 100

42B Impact (ordinal [0,1,2,3], v1_leq_E_42B)

v1_leq_b_recode(v1_clin$v1_leq_e_leq42e_veraend_taet,v1_con$v1_leq_e_leq42e,"v1_leq_E_42B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1210 9   10  30  24  260  1543
## [2,] Percent   78.4 0.6 0.6 1.9 1.6 16.9 100

Create dataset

v1_leq_E<-data.frame(v1_leq_E_28A,v1_leq_E_28B,v1_leq_E_29A,v1_leq_E_29B,v1_leq_E_30A,
                     v1_leq_E_30B,v1_leq_E_31A,v1_leq_E_31B,v1_leq_E_32A,v1_leq_E_32B,
                     v1_leq_E_33A,v1_leq_E_33B,v1_leq_E_34A,v1_leq_E_34B,v1_leq_E_35A,
                     v1_leq_E_35B,v1_leq_E_36A,v1_leq_E_36B,v1_leq_E_37A,v1_leq_E_37B,
                     v1_leq_E_38A,v1_leq_E_38B,v1_leq_E_39A,v1_leq_E_39B,v1_leq_E_40A,
                     v1_leq_E_40B,v1_leq_E_41A,v1_leq_E_41B,v1_leq_E_42A,v1_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v1_leq_F_43A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq43a_neu_fmitglied,v1_con$v1_leq_f_g_leq43a,"v1_leq_F_43A")
##                -999 bad good <NA>     
## [1,] No. cases 1180 12  91   260  1543
## [2,] Percent   76.5 0.8 5.9  16.9 100

43B Impact (ordinal [0,1,2,3], v1_leq_F_43B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq43e_neu_fmitglied,v1_con$v1_leq_f_g_leq43e,"v1_leq_F_43B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1179 9   30  26  39  260  1543
## [2,] Percent   76.4 0.6 1.9 1.7 2.5 16.9 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v1_leq_F_44A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq44a_auszug_fm,v1_con$v1_leq_f_g_leq44a,"v1_leq_F_44A")
##                -999 bad good <NA>     
## [1,] No. cases 1226 26  31   260  1543
## [2,] Percent   79.5 1.7 2    16.9 100

44B Impact (ordinal [0,1,2,3], v1_leq_F_44B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq44e_auszug_fm,v1_con$v1_leq_f_g_leq44e,"v1_leq_F_44B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1224 7   13  21  18  260  1543
## [2,] Percent   79.3 0.5 0.8 1.4 1.2 16.9 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v1_leq_F_45A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq45a_gz_verh_fm,v1_con$v1_leq_f_g_leq45a,"v1_leq_F_45A")
##                -999 bad  good <NA>     
## [1,] No. cases 1045 215  23   260  1543
## [2,] Percent   67.7 13.9 1.5  16.9 100

45B Impact (ordinal [0,1,2,3], v1_leq_F_45B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq45e_gz_verh_fm,v1_con$v1_leq_f_g_leq45e,"v1_leq_F_45B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1044 5   40  89  105 260  1543
## [2,] Percent   67.7 0.3 2.6 5.8 6.8 16.9 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v1_leq_F_46A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq46a_tod_partner,v1_con$v1_leq_f_g_leq46a,"v1_leq_F_46A")
##                -999 bad <NA>     
## [1,] No. cases 1270 13  260  1543
## [2,] Percent   82.3 0.8 16.9 100

46B Impact (ordinal [0,1,2,3], v1_leq_F_46B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq46e_tod_partner,v1_con$v1_leq_f_g_leq46e,"v1_leq_F_46B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 1269 2   4   8   260  1543
## [2,] Percent   82.2 0.1 0.3 0.5 16.9 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v1_leq_F_47A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq47a_tod_kind,v1_con$v1_leq_f_g_leq47a,"v1_leq_F_47A")
##                -999 bad <NA>     
## [1,] No. cases 1273 10  260  1543
## [2,] Percent   82.5 0.6 16.9 100

47B Impact (ordinal [0,1,2,3], v1_leq_F_47B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq47e_tod_kind,v1_con$v1_leq_f_g_leq47e,"v1_leq_F_47B")
##                -999 0   3   <NA>     
## [1,] No. cases 1273 2   8   260  1543
## [2,] Percent   82.5 0.1 0.5 16.9 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v1_leq_F_48A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq48a_tod_fm_ef,v1_con$v1_leq_f_g_leq48a,"v1_leq_F_48A")
##                -999 bad good <NA>     
## [1,] No. cases 1176 104 3    260  1543
## [2,] Percent   76.2 6.7 0.2  16.9 100

48B Impact (ordinal [0,1,2,3], v1_leq_F_48B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq48e_tod_fm_ef,v1_con$v1_leq_f_g_leq48e,"v1_leq_F_48B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1175 9   22  34  43  260  1543
## [2,] Percent   76.2 0.6 1.4 2.2 2.8 16.9 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v1_leq_F_49A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq49a_geb_enkel,v1_con$v1_leq_f_g_leq49a,"v1_leq_F_49A")
##                -999 bad good <NA>     
## [1,] No. cases 1254 3   26   260  1543
## [2,] Percent   81.3 0.2 1.7  16.9 100

49B Impact (ordinal [0,1,2,3], v1_leq_F_49B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq49e_geb_enkel,v1_con$v1_leq_f_g_leq49e,"v1_leq_F_49B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1253 5   5   3   17  260  1543
## [2,] Percent   81.2 0.3 0.3 0.2 1.1 16.9 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v1_leq_F_50A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq50a_fstand_eltern,v1_con$v1_leq_f_g_leq50a,"v1_leq_F_50A")
##                -999 bad good <NA>     
## [1,] No. cases 1253 23  7    260  1543
## [2,] Percent   81.2 1.5 0.5  16.9 100

50B Impact (ordinal [0,1,2,3], v1_leq_F_50B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq50e_fstand_eltern,v1_con$v1_leq_f_g_leq50e,"v1_leq_F_50B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1252 4   7   10  10  260  1543
## [2,] Percent   81.1 0.3 0.5 0.6 0.6 16.9 100

Create dataset

v1_leq_F<-data.frame(v1_leq_F_43A,v1_leq_F_43B,v1_leq_F_44A,v1_leq_F_44B,v1_leq_F_45A,
                     v1_leq_F_45B,v1_leq_F_46A,v1_leq_F_46B,v1_leq_F_47A,v1_leq_F_47B,
                     v1_leq_F_48A,v1_leq_F_48B,v1_leq_F_49A,v1_leq_F_49B,v1_leq_F_50A,
                     v1_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v1_leq_G_51A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq51a_kindbetr,v1_con$v1_leq_f_g_leq51a,"v1_leq_G_51A")
##                -999 bad good <NA>     
## [1,] No. cases 1245 17  21   260  1543
## [2,] Percent   80.7 1.1 1.4  16.9 100

51B Impact (ordinal [0,1,2,3], v1_leq_G_51B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq51e_kindbetr,v1_con$v1_leq_f_g_leq51e,"v1_leq_G_51B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1244 5   5   12  17  260  1543
## [2,] Percent   80.6 0.3 0.3 0.8 1.1 16.9 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v1_leq_G_52A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq52a_konf_eschaft,v1_con$v1_leq_f_g_leq52a,"v1_leq_G_52A")
##                -999 bad good <NA>     
## [1,] No. cases 1244 33  6    260  1543
## [2,] Percent   80.6 2.1 0.4  16.9 100

52B Impact (ordinal [0,1,2,3], v1_leq_G_52B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq52e_konf_eschaft,v1_con$v1_leq_f_g_leq52e,"v1_leq_G_52B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1244 3   12  16 8   260  1543
## [2,] Percent   80.6 0.2 0.8 1  0.5 16.9 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v1_leq_G_53A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq53a_konf_geltern,v1_con$v1_leq_f_g_leq53a,"v1_leq_G_53A")
##                -999 bad good <NA>     
## [1,] No. cases 1264 16  3    260  1543
## [2,] Percent   81.9 1   0.2  16.9 100

53B Impact (ordinal [0,1,2,3], v1_leq_G_53B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq53e_konf_geltern,v1_con$v1_leq_f_g_leq53e,"v1_leq_G_53B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1264 2   7   3   7   260  1543
## [2,] Percent   81.9 0.1 0.5 0.2 0.5 16.9 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v1_leq_G_54A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq54a_alleinerz,v1_con$v1_leq_f_g_leq54a,"v1_leq_G_54A")
##                -999 bad good <NA>     
## [1,] No. cases 1263 12  8    260  1543
## [2,] Percent   81.9 0.8 0.5  16.9 100

54B Impact (ordinal [0,1,2,3], v1_leq_G_54B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq54e_alleinerz,v1_con$v1_leq_f_g_leq54e,"v1_leq_G_54B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1263 2   2   8   8   260  1543
## [2,] Percent   81.9 0.1 0.1 0.5 0.5 16.9 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v1_leq_G_55A)

v1_leq_a_recode(v1_clin$v1_leq_f_g_leq55a_sorgerecht,v1_con$v1_leq_f_g_leq55a,"v1_leq_G_55A")
##                -999 bad good <NA>     
## [1,] No. cases 1255 26  2    260  1543
## [2,] Percent   81.3 1.7 0.1  16.9 100

55B Impact (ordinal [0,1,2,3], v1_leq_G_55B)

v1_leq_b_recode(v1_clin$v1_leq_f_g_leq55e_sorgerecht,v1_con$v1_leq_f_g_leq55e,"v1_leq_G_55B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1253 2   11  7   10  260  1543
## [2,] Percent   81.2 0.1 0.7 0.5 0.6 16.9 100

Create dataset

v1_leq_G<-data.frame(v1_leq_G_51A,
                     v1_leq_G_51B,
                     v1_leq_G_52A,
                     v1_leq_G_52B,
                     v1_leq_G_53A,
                     v1_leq_G_53B,
                     v1_leq_G_54A,
                     v1_leq_G_54B,
                     v1_leq_G_55A,
                     v1_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v1_leq_H_56A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq56a_pers_leistung,v1_con$v1_leq_h_leq56a,"v1_leq_H_56A")
##                -999 bad good <NA>     
## [1,] No. cases 979  54  250  260  1543
## [2,] Percent   63.4 3.5 16.2 16.9 100

56B Impact (ordinal [0,1,2,3], v1_leq_H_56B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq56e_pers_leistung,v1_con$v1_leq_h_leq56e,"v1_leq_H_56B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 976  11  54  118 124 260  1543
## [2,] Percent   63.3 0.7 3.5 7.6 8   16.9 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v1_leq_H_57A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq57a_wicht_entsch,v1_con$v1_leq_h_leq57a,"v1_leq_H_57A")
##                -999 bad good <NA>     
## [1,] No. cases 796  112 375  260  1543
## [2,] Percent   51.6 7.3 24.3 16.9 100

57B Impact (ordinal [0,1,2,3], v1_leq_H_57B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq57e_wicht_entsch,v1_con$v1_leq_h_leq57e,"v1_leq_H_57B")
##                -999 0   1   2    3    <NA>     
## [1,] No. cases 787  11  60  165  260  260  1543
## [2,] Percent   51   0.7 3.9 10.7 16.9 16.9 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v1_leq_H_58A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq58a_pers_gewohn,v1_con$v1_leq_h_leq58a,"v1_leq_H_58A")
##                -999 bad good <NA>     
## [1,] No. cases 930  97  256  260  1543
## [2,] Percent   60.3 6.3 16.6 16.9 100

58B Impact (ordinal [0,1,2,3], v1_leq_H_58B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq58e_pers_gewohn,v1_con$v1_leq_h_leq58e,"v1_leq_H_58B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 926  12  81  139 125 260  1543
## [2,] Percent   60   0.8 5.2 9   8.1 16.9 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v1_leq_H_59A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq59a_relig_ueberz,v1_con$v1_leq_h_leq59a,"v1_leq_H_59A")
##                -999 bad good <NA>     
## [1,] No. cases 1180 17  86   260  1543
## [2,] Percent   76.5 1.1 5.6  16.9 100

59B Impact (ordinal [0,1,2,3], v1_leq_H_59B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq59e_relig_ueberz,v1_con$v1_leq_h_leq59e,"v1_leq_H_59B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1173 19  30  26  35  260  1543
## [2,] Percent   76   1.2 1.9 1.7 2.3 16.9 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v1_leq_H_60A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq60a_pol_ansichten,v1_con$v1_leq_h_leq60a,"v1_leq_H_60A")
##                -999 bad good <NA>     
## [1,] No. cases 1193 17  73   260  1543
## [2,] Percent   77.3 1.1 4.7  16.9 100

60B Impact (ordinal [0,1,2,3], v1_leq_H_60B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq60e_pol_ansichten,v1_con$v1_leq_h_leq60e,"v1_leq_H_60B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1186 25  27  32  13  260  1543
## [2,] Percent   76.9 1.6 1.7 2.1 0.8 16.9 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v1_leq_H_61A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq61a_pers_eigent,v1_con$v1_leq_h_leq61a,"v1_leq_H_61A")
##                -999 bad good <NA>     
## [1,] No. cases 1148 115 20   260  1543
## [2,] Percent   74.4 7.5 1.3  16.9 100

61B Impact (ordinal [0,1,2,3], v1_leq_H_61B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq61e_pers_eigent,v1_con$v1_leq_h_leq61e,"v1_leq_H_61B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1144 20  42  38  39  260  1543
## [2,] Percent   74.1 1.3 2.7 2.5 2.5 16.9 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v1_leq_H_62A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq62a_erholungsurl,v1_con$v1_leq_h_leq62a,"v1_leq_H_62A")
##                -999 bad good <NA>     
## [1,] No. cases 944  21  318  260  1543
## [2,] Percent   61.2 1.4 20.6 16.9 100

62B Impact (ordinal [0,1,2,3], v1_leq_H_62B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq62e_erholungsurl,v1_con$v1_leq_h_leq62e,"v1_leq_H_62B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 937  34  61 136 115 260  1543
## [2,] Percent   60.7 2.2 4  8.8 7.5 16.9 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v1_leq_H_63A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq63a_reise_andere,v1_con$v1_leq_h_leq63a,"v1_leq_H_63A")
##                -999 bad good <NA>     
## [1,] No. cases 1108 15  160  260  1543
## [2,] Percent   71.8 1   10.4 16.9 100

63B Impact (ordinal [0,1,2,3], v1_leq_H_63B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq63e_reise_andere,v1_con$v1_leq_h_leq63e,"v1_leq_H_63B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1104 18  42  65  54  260  1543
## [2,] Percent   71.5 1.2 2.7 4.2 3.5 16.9 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v1_leq_H_64A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq64a_familientreff,v1_con$v1_leq_h_leq64a,"v1_leq_H_64A")
##                -999 bad good <NA>     
## [1,] No. cases 1145 54  84   260  1543
## [2,] Percent   74.2 3.5 5.4  16.9 100

64B Impact (ordinal [0,1,2,3], v1_leq_H_64B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq64e_familientreff,v1_con$v1_leq_h_leq64e,"v1_leq_H_64B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1142 9   44  50  38  260  1543
## [2,] Percent   74   0.6 2.9 3.2 2.5 16.9 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v1_leq_H_65A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq65a_ges_unternehm,v1_con$v1_leq_h_leq65a,"v1_leq_H_65A")
##                -999 bad good <NA>     
## [1,] No. cases 1091 69  123  260  1543
## [2,] Percent   70.7 4.5 8    16.9 100

65B Impact (ordinal [0,1,2,3], v1_leq_H_65B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq65e_ges_unternehm,v1_con$v1_leq_h_leq65e,"v1_leq_H_65B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1088 9   58  72  56  260  1543
## [2,] Percent   70.5 0.6 3.8 4.7 3.6 16.9 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v1_leq_H_66A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq66a_neue_freunds,v1_con$v1_leq_h_leq66a,"v1_leq_H_66A")
##                -999 bad good <NA>     
## [1,] No. cases 879  18  386  260  1543
## [2,] Percent   57   1.2 25   16.9 100

66B Impact (ordinal [0,1,2,3], v1_leq_H_66B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq66e_neue_freunds,v1_con$v1_leq_h_leq66e,"v1_leq_H_66B")
##                -999 0  1   2    3   <NA>     
## [1,] No. cases 873  16 98  181  115 260  1543
## [2,] Percent   56.6 1  6.4 11.7 7.5 16.9 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v1_leq_H_67A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq67a_ende_freunds,v1_con$v1_leq_h_leq67a,"v1_leq_H_67A")
##                -999 bad good <NA>     
## [1,] No. cases 1090 136 57   260  1543
## [2,] Percent   70.6 8.8 3.7  16.9 100

67B Impact (ordinal [0,1,2,3], v1_leq_H_67B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq67e_ende_freunds,v1_con$v1_leq_h_leq67e,"v1_leq_H_67B")
##                -999 0   1  2   3  <NA>     
## [1,] No. cases 1089 13  46 74  61 260  1543
## [2,] Percent   70.6 0.8 3  4.8 4  16.9 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v1_leq_H_68A)

v1_leq_a_recode(v1_clin$v1_leq_h_leq68a_haustier,v1_con$v1_leq_h_leq68a,"v1_leq_H_68A")
##                -999 bad good <NA>     
## [1,] No. cases 1163 61  59   260  1543
## [2,] Percent   75.4 4   3.8  16.9 100

68B Impact (ordinal [0,1,2,3], v1_leq_H_68B)

v1_leq_b_recode(v1_clin$v1_leq_h_leq68e_haustier,v1_con$v1_leq_h_leq68e,"v1_leq_H_68B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1160 14  19  35  55  260  1543
## [2,] Percent   75.2 0.9 1.2 2.3 3.6 16.9 100

Create dataset

v1_leq_H<-data.frame(v1_leq_H_56A,
                     v1_leq_H_56B,
                     v1_leq_H_57A,
                     v1_leq_H_57B,
                     v1_leq_H_58A,
                     v1_leq_H_58B,
                     v1_leq_H_59A,
                     v1_leq_H_59B,
                     v1_leq_H_60A,
                     v1_leq_H_60B,
                     v1_leq_H_61A,
                     v1_leq_H_61B,
                     v1_leq_H_62A,
                     v1_leq_H_62B,
                     v1_leq_H_63A,
                     v1_leq_H_63B,
                     v1_leq_H_64A,
                     v1_leq_H_64B,
                     v1_leq_H_65A,
                     v1_leq_H_65B,
                     v1_leq_H_66A,
                     v1_leq_H_66B,
                     v1_leq_H_67A,
                     v1_leq_H_67B,
                     v1_leq_H_68A,
                     v1_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v1_leq_I_69A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq69a_finanz_sit,v1_con$v1_leq_i_j_k_leq69a,"v1_leq_I_69A")
##                -999 bad good <NA>     
## [1,] No. cases 888  232 163  260  1543
## [2,] Percent   57.6 15  10.6 16.9 100

69B Impact (ordinal [0,1,2,3], v1_leq_I_69B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq69e_finanz_sit,v1_con$v1_leq_i_j_k_leq69e,"v1_leq_I_69B")
##                -999 0   1   2   3    <NA>     
## [1,] No. cases 886  14  90  122 171  260  1543
## [2,] Percent   57.4 0.9 5.8 7.9 11.1 16.9 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v1_leq_I_70A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq70a_finanz_verpfl,v1_con$v1_leq_i_j_k_leq70a,"v1_leq_I_70A")
##                -999 bad good <NA>     
## [1,] No. cases 1140 57  86   260  1543
## [2,] Percent   73.9 3.7 5.6  16.9 100

70B Impact (ordinal [0,1,2,3], v1_leq_I_70B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq70e_finanz_verpfl,v1_con$v1_leq_i_j_k_leq70e,"v1_leq_I_70B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 1139 20  46 50  28  260  1543
## [2,] Percent   73.8 1.3 3  3.2 1.8 16.9 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v1_leq_I_71A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq71a_hypothek,v1_con$v1_leq_i_j_k_leq71a,"v1_leq_I_71A")
##                -999 bad good <NA>     
## [1,] No. cases 1229 31  23   260  1543
## [2,] Percent   79.7 2   1.5  16.9 100

71B Impact (ordinal [0,1,2,3], v1_leq_I_71B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq71e_hypothek,v1_con$v1_leq_i_j_k_leq71e,"v1_leq_I_71B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 1227 9   12  16 19  260  1543
## [2,] Percent   79.5 0.6 0.8 1  1.2 16.9 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v1_leq_I_72A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq72a_hypoth_kuend,v1_con$v1_leq_i_j_k_leq72a,"v1_leq_I_72A")
##                -999 bad good <NA>     
## [1,] No. cases 1256 12  15   260  1543
## [2,] Percent   81.4 0.8 1    16.9 100

72B Impact (ordinal [0,1,2,3], v1_leq_I_72B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq72e_hypoth_kuend,v1_con$v1_leq_i_j_k_leq72e,"v1_leq_I_72B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1256 6   4   5   12  260  1543
## [2,] Percent   81.4 0.4 0.3 0.3 0.8 16.9 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v1_leq_I_73A)

v1_leq_a_recode(v1_clin$v1_leq_i_j_k_leq73a_kreditwuerdk,v1_con$v1_leq_i_j_k_leq73a,"v1_leq_I_73A")
##                -999 bad good <NA>     
## [1,] No. cases 1193 84  6    260  1543
## [2,] Percent   77.3 5.4 0.4  16.9 100

73B Impact (ordinal [0,1,2,3], v1_leq_I_73B)

v1_leq_b_recode(v1_clin$v1_leq_i_j_k_leq73e_kreditwuerdk,v1_con$v1_leq_i_j_k_leq73e,"v1_leq_I_73B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 1192 11  18  23  39  260  1543
## [2,] Percent   77.3 0.7 1.2 1.5 2.5 16.9 100

Create dataset

v1_leq_I<-data.frame(v1_leq_I_69A,v1_leq_I_69B,v1_leq_I_70A,v1_leq_I_70B,v1_leq_I_71A,
                     v1_leq_I_71B,v1_leq_I_72A,v1_leq_I_72B,v1_leq_I_73A,v1_leq_I_73B)

Quality of Life Questionnaire (WHOQOL-BREF)

The WHOQOL-BREF instrument comprises 26 items, which measure the following broad domains: physical health, psychological health, social relationships, and environment. The past two weeks are assessed. All items are on a five-point scale with the following gradations:
Items 1, 15: “Very poor”-1, “Poor”-2, “Neither poor nor good”-3, “Good”-4, “Very good”-5 Items 2, 16-25: “Very dissatisfied”-1, “dissatisfied”-2, “Neither satisfied nor dissatisfied”-3, “satisfied”-4, “Very satisfied”-5 Items 3-14: “Not at all”-1, “A little”-2, “A moderate amount”-3, “Very much”-4, “An extreme amount”-5 Items 26: “Never”-1, “Seldom”-2, “Quite often”-3, “Very often”-4, “Always”-5

The coding of items number three, four and 26 has been reversed to keep directionality (see below). For all items higher scores now mean higher quality of life. Please see below for subscales (domain scores) of this questionnaire.

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v1_whoqol_itm1)

v1_quol_recode(v1_clin$v1_whoqol_bref_who1_lebensqualitaet,v1_con$v1_whoqol_bref_who1,"v1_whoqol_itm1",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 52  153 391  526  243  178  1543
## [2,] Percent   3.4 9.9 25.3 34.1 15.7 11.5 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v1_whoqol_itm2)”

v1_quol_recode(v1_clin$v1_whoqol_bref_who2_gesundheit,v1_con$v1_whoqol_bref_who2,"v1_whoqol_itm2",0)
##                1   2    3    4    5    NA's     
## [1,] No. cases 98  307  305  460  187  186  1543
## [2,] Percent   6.4 19.9 19.8 29.8 12.1 12.1 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v1_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v1_quol_recode(v1_clin$v1_whoqol_bref_who3_schmerzen,v1_con$v1_whoqol_bref_who3,"v1_whoqol_itm3",1)
##                1   2   3   4    5    NA's     
## [1,] No. cases 27  104 123 304  798  187  1543
## [2,] Percent   1.7 6.7 8   19.7 51.7 12.1 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v1_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v1_quol_recode(v1_clin$v1_whoqol_bref_who4_med_behand,v1_con$v1_whoqol_bref_who4,"v1_whoqol_itm4",1)
##                1    2    3    4    5    NA's     
## [1,] No. cases 168  294  213  229  451  188  1543
## [2,] Percent   10.9 19.1 13.8 14.8 29.2 12.2 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v1_whoqol_itm5)

v1_quol_recode(v1_clin$v1_whoqol_bref_who5_lebensgenuss,v1_con$v1_whoqol_bref_who5,"v1_whoqol_itm5",0)
##                1   2    3    4    5    NA's     
## [1,] No. cases 75  226  374  461  213  194  1543
## [2,] Percent   4.9 14.6 24.2 29.9 13.8 12.6 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v1_whoqol_itm6)

v1_quol_recode(v1_clin$v1_whoqol_bref_who6_lebenssinn,v1_con$v1_whoqol_bref_who6,"v1_whoqol_itm6",0)
##                1   2    3   4    5    NA's     
## [1,] No. cases 105 160  278 453  352  195  1543
## [2,] Percent   6.8 10.4 18  29.4 22.8 12.6 100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v1_whoqol_itm7)

v1_quol_recode(v1_clin$v1_whoqol_bref_who7_konzentration,v1_con$v1_whoqol_bref_who7,"v1_whoqol_itm7",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 43  231 499  496  89  185  1543
## [2,] Percent   2.8 15  32.3 32.1 5.8 12   100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v1_whoqol_itm8)

v1_quol_recode(v1_clin$v1_whoqol_bref_who8_sicherheit,v1_con$v1_whoqol_bref_who8,"v1_whoqol_itm8",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 66  144 335  545  266  187  1543
## [2,] Percent   4.3 9.3 21.7 35.3 17.2 12.1 100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v1_whoqol_itm9)

v1_quol_recode(v1_clin$v1_whoqol_bref_who9_umweltbed,v1_con$v1_whoqol_bref_who9,"v1_whoqol_itm9",0)
##                1   2  3    4    5   NA's     
## [1,] No. cases 23  62 310  634  324 190  1543
## [2,] Percent   1.5 4  20.1 41.1 21  12.3 100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v1_whoqol_itm10)

v1_quol_recode(v1_clin$v1_whoqol_bref_who10_energie,v1_con$v1_whoqol_bref_who10,"v1_whoqol_itm10",0)
##                1   2    3    4    5    NA's     
## [1,] No. cases 48  168  353  485  297  192  1543
## [2,] Percent   3.1 10.9 22.9 31.4 19.2 12.4 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v1_whoqol_itm11)

v1_quol_recode(v1_clin$v1_whoqol_bref_who11_aussehen,v1_con$v1_whoqol_bref_who11,"v1_whoqol_itm11",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 48  134 293 526  344  198  1543
## [2,] Percent   3.1 8.7 19  34.1 22.3 12.8 100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v1_whoqol_itm12)

v1_quol_recode(v1_clin$v1_whoqol_bref_who12_genug_geld,v1_con$v1_whoqol_bref_who12,"v1_whoqol_itm12",0)
##                1  2    3    4    5    NA's     
## [1,] No. cases 92 205  319  394  341  192  1543
## [2,] Percent   6  13.3 20.7 25.5 22.1 12.4 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v1_whoqol_itm13)

v1_quol_recode(v1_clin$v1_whoqol_bref_who13_infozugang,v1_con$v1_whoqol_bref_who13,"v1_whoqol_itm13",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 11  50  179  486  623  194  1543
## [2,] Percent   0.7 3.2 11.6 31.5 40.4 12.6 100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v1_whoqol_itm14)

v1_quol_recode(v1_clin$v1_whoqol_bref_who14_freizeitaktiv,v1_con$v1_whoqol_bref_who14,"v1_whoqol_itm14",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 20  138 286  438  468  193  1543
## [2,] Percent   1.3 8.9 18.5 28.4 30.3 12.5 100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v1_whoqol_itm15)”

v1_quol_recode(v1_clin$v1_whoqol_bref_who15_fortbewegung,v1_con$v1_whoqol_bref_who15,"v1_whoqol_itm15",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 8   67  219  491  565  193  1543
## [2,] Percent   0.5 4.3 14.2 31.8 36.6 12.5 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v1_whoqol_itm16)

v1_quol_recode(v1_clin$v1_whoqol_bref_who16_schlaf,v1_con$v1_whoqol_bref_who16,"v1_whoqol_itm16",0)
##                1   2    3    4    5    NA's     
## [1,] No. cases 69  245  252  573  233  171  1543
## [2,] Percent   4.5 15.9 16.3 37.1 15.1 11.1 100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v1_whoqol_itm17)

v1_quol_recode(v1_clin$v1_whoqol_bref_who17_alltag,v1_con$v1_whoqol_bref_who17,"v1_whoqol_itm17",0)
##                1   2    3    4    5    NA's     
## [1,] No. cases 59  239  250  557  265  173  1543
## [2,] Percent   3.8 15.5 16.2 36.1 17.2 11.2 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v1_whoqol_itm18)

v1_quol_recode(v1_clin$v1_whoqol_bref_who18_arbeitsfhgk,v1_con$v1_whoqol_bref_who18,"v1_whoqol_itm18",0)
##                1    2    3    4    5    NA's     
## [1,] No. cases 163  287  281  406  217  189  1543
## [2,] Percent   10.6 18.6 18.2 26.3 14.1 12.2 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v1_whoqol_itm19)

v1_quol_recode(v1_clin$v1_whoqol_bref_who19_selbstzufried,v1_con$v1_whoqol_bref_who19,"v1_whoqol_itm19",0)
##                1   2    3    4    5   NA's     
## [1,] No. cases 90  211  329  542  185 186  1543
## [2,] Percent   5.8 13.7 21.3 35.1 12  12.1 100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v1_whoqol_itm20)

v1_quol_recode(v1_clin$v1_whoqol_bref_who20_pers_bezieh,v1_con$v1_whoqol_bref_who20,"v1_whoqol_itm20",0)
##                1   2    3    4   5    NA's     
## [1,] No. cases 65  181  260  601 250  186  1543
## [2,] Percent   4.2 11.7 16.9 39  16.2 12.1 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v1_whoqol_itm21)

v1_quol_recode(v1_clin$v1_whoqol_bref_who21_sexualleben,v1_con$v1_whoqol_bref_who21,"v1_whoqol_itm21",0)
##                1   2    3   4    5    NA's     
## [1,] No. cases 200 248  385 353  162  195  1543
## [2,] Percent   13  16.1 25  22.9 10.5 12.6 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v1_whoqol_itm22)

v1_quol_recode(v1_clin$v1_whoqol_bref_who22_freunde,v1_con$v1_whoqol_bref_who22,"v1_whoqol_itm22",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 63  119 274  580  331  176  1543
## [2,] Percent   4.1 7.7 17.8 37.6 21.5 11.4 100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v1_whoqol_itm23)

v1_quol_recode(v1_clin$v1_whoqol_bref_who23_wohnbeding,v1_con$v1_whoqol_bref_who23,"v1_whoqol_itm23",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 88  134 208  530  410  173  1543
## [2,] Percent   5.7 8.7 13.5 34.3 26.6 11.2 100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v1_whoqol_itm24)

v1_quol_recode(v1_clin$v1_whoqol_bref_who24_gesundhdiens,v1_con$v1_whoqol_bref_who24,"v1_whoqol_itm24",0)
##                1  2   3    4    5    NA's     
## [1,] No. cases 31 43  212  613  472  172  1543
## [2,] Percent   2  2.8 13.7 39.7 30.6 11.1 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v1_whoqol_itm25)

v1_quol_recode(v1_clin$v1_whoqol_bref_who25_transport,v1_con$v1_whoqol_bref_who25,"v1_whoqol_itm25",0)
##                1   2  3    4    5    NA's     
## [1,] No. cases 35  77 190  577  484  180  1543
## [2,] Percent   2.3 5  12.3 37.4 31.4 11.7 100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v1_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v1_quol_recode(v1_clin$v1_whoqol_bref_who26_neg_gefuehle,v1_con$v1_whoqol_bref_who26,"v1_whoqol_itm26",1)
##                1   2    3    4    5    NA's     
## [1,] No. cases 65  288  328  454  210  198  1543
## [2,] Percent   4.2 18.7 21.3 29.4 13.6 12.8 100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v1_whoqol_dom_glob)

v1_whoqol_dom_glob_df<-data.frame(as.numeric(v1_whoqol_itm1),as.numeric(v1_whoqol_itm2))

v1_who_glob_no_nas<-rowSums(is.na(v1_whoqol_dom_glob_df))

v1_whoqol_dom_glob<-ifelse((v1_who_glob_no_nas==0) | (v1_who_glob_no_nas==1), 
                            rowMeans(v1_whoqol_dom_glob_df,na.rm=T)*4,NA)

v1_whoqol_dom_glob<-round(v1_whoqol_dom_glob,2)

summary(v1_whoqol_dom_glob)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     4.0    10.0    14.0    13.6    16.0    20.0     172

Physical Health (continuous [4-20],v1_whoqol_dom_phys)

v1_whoqol_dom_phys_df<-data.frame(as.numeric(v1_whoqol_itm3),as.numeric(v1_whoqol_itm10),as.numeric(v1_whoqol_itm16),as.numeric(v1_whoqol_itm15),as.numeric(v1_whoqol_itm17),as.numeric(v1_whoqol_itm4),as.numeric(v1_whoqol_itm18))

v1_who_phys_no_nas<-rowSums(is.na(v1_whoqol_dom_phys_df))

v1_whoqol_dom_phys<-ifelse((v1_who_phys_no_nas==0) | (v1_who_phys_no_nas==1), 
                            rowMeans(v1_whoqol_dom_phys_df,na.rm=T)*4,NA)

v1_whoqol_dom_phys<-round(v1_whoqol_dom_phys,2)

summary(v1_whoqol_dom_phys)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.14   12.57   14.86   14.62   17.14   20.00     192

Psychological (continuous [4-20],v1_whoqol_dom_psy)

v1_whoqol_dom_psy_df<-data.frame(as.numeric(v1_whoqol_itm5),as.numeric(v1_whoqol_itm7),as.numeric(v1_whoqol_itm19),as.numeric(v1_whoqol_itm11),as.numeric(v1_whoqol_itm26),as.numeric(v1_whoqol_itm6))

v1_who_psy_no_nas<-rowSums(is.na(v1_whoqol_dom_psy_df))

v1_whoqol_dom_psy<-ifelse((v1_who_psy_no_nas==0) | (v1_who_psy_no_nas==1), 
                            rowMeans(v1_whoqol_dom_psy_df,na.rm=T)*4,NA)

v1_whoqol_dom_psy<-round(v1_whoqol_dom_psy,2)

summary(v1_whoqol_dom_psy)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.67   11.33   14.40   13.79   16.67   20.00     196

Social relationships (continuous [4-20],v1_whoqol_dom_soc)

v1_whoqol_dom_soc_df<-data.frame(as.numeric(v1_whoqol_itm20),as.numeric(v1_whoqol_itm22),as.numeric(v1_whoqol_itm21))

v1_who_soc_no_nas<-rowSums(is.na(v1_whoqol_dom_soc_df))

v1_whoqol_dom_soc<-ifelse((v1_who_soc_no_nas==0) | (v1_who_soc_no_nas==1), 
                            rowMeans(v1_whoqol_dom_soc_df,na.rm=T)*4,NA)

v1_whoqol_dom_soc<-round(v1_whoqol_dom_soc,2)

summary(v1_whoqol_dom_soc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   13.80   16.00   20.00     178

Environment (continuous [4-20],v1_whoqol_dom_env)

v1_whoqol_dom_env_df<-data.frame(as.numeric(v1_whoqol_itm8),as.numeric(v1_whoqol_itm23),as.numeric(v1_whoqol_itm12),as.numeric(v1_whoqol_itm24),as.numeric(v1_whoqol_itm13),as.numeric(v1_whoqol_itm14),as.numeric(v1_whoqol_itm9),as.numeric(v1_whoqol_itm25))

v1_who_env_no_nas<-rowSums(is.na(v1_whoqol_dom_env_df))

v1_whoqol_dom_env<-ifelse((v1_who_env_no_nas==0) | (v1_who_env_no_nas==1), 
                            rowMeans(v1_whoqol_dom_env_df,na.rm=T)*4,NA)

v1_whoqol_dom_env<-round(v1_whoqol_dom_env,2)

summary(v1_whoqol_dom_env)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.50   13.50   16.00   15.48   17.50   20.00     194

Create dataset

v1_whoqol<-data.frame(v1_whoqol_itm1,v1_whoqol_itm2,v1_whoqol_itm3,v1_whoqol_itm4,
                      v1_whoqol_itm5,v1_whoqol_itm6,v1_whoqol_itm7,v1_whoqol_itm8,
                      v1_whoqol_itm9,v1_whoqol_itm10,v1_whoqol_itm11,v1_whoqol_itm12,
                      v1_whoqol_itm13,v1_whoqol_itm14,v1_whoqol_itm15,v1_whoqol_itm16,
                      v1_whoqol_itm17,v1_whoqol_itm18,v1_whoqol_itm19,v1_whoqol_itm20,
                      v1_whoqol_itm21,v1_whoqol_itm22,v1_whoqol_itm23,v1_whoqol_itm24,
                      v1_whoqol_itm25,v1_whoqol_itm26,v1_whoqol_dom_glob,
                      v1_whoqol_dom_phys,v1_whoqol_dom_psy,v1_whoqol_dom_soc,
                      v1_whoqol_dom_env)

Personality

This is a 10-item questionnaire measuring personality (Rammstedt & John, 2007). It is based on the well-known ‘Big Five’ model of personality. The five dimensions are the following: extraversion, neuroticism, conscientiousness, agreeableness, openness. Instruction: How well do the following statements describe your personality? Each statement starts with: “I see myself as someone who…”. Each item is to be rated on a five point scale (“disagree strongly”-1, “disagree a little”-2,“neither agree nor disagree”-3, “agree a little”-4,“agree strongly”-5). The coding of some items has been reversed so that higher scores on each item mean higher scores on the respective personality dimension. Below, we calculate sumscores for each personality dimension.

1. “…is reserved” (ordinal [1,2,3,4,5], v1_big_five_itm1)
Personality dimension: extraversion, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi1_reserviert,v1_con$v1_bfi_10_bfi1,"v1_big_five_itm1",1)
##                1    2    3    4    5    NA's     
## [1,] No. cases 157  480  222  349  160  175  1543
## [2,] Percent   10.2 31.1 14.4 22.6 10.4 11.3 100

2. “… is generally trusting” (ordinal [1,2,3,4,5], v1_big_five_itm2)
Personality dimension: agreeableness.

big_five_recode(v1_clin$v1_bfi_10_bfi2_vertrauen,v1_con$v1_bfi_10_bfi2,"v1_big_five_itm2",0)
##                1  2   3    4    5    NA's     
## [1,] No. cases 47 201 243  657  221  174  1543
## [2,] Percent   3  13  15.7 42.6 14.3 11.3 100

3. “…tends to be lazy” (ordinal [1,2,3,4,5], v1_big_five_itm3)
Personality dimension: conscientiousness, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi3_bequem,v1_con$v1_bfi_10_bfi3,"v1_big_five_itm3",1)
##                1   2    3   4    5    NA's     
## [1,] No. cases 86  351  263 408  256  179  1543
## [2,] Percent   5.6 22.7 17  26.4 16.6 11.6 100

4. “…is relaxed, handles stress well” (ordinal [1,2,3,4,5], v1_big_five_itm4)
Personality dimension: neuroticism, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi4_stress,v1_con$v1_bfi_10_bfi4,"v1_big_five_itm4",1)
##                1   2    3    4    5   NA's     
## [1,] No. cases 102 424  250  435  154 178  1543
## [2,] Percent   6.6 27.5 16.2 28.2 10  11.5 100

5. “… has few artistic interests” (ordinal [1,2,3,4,5], v1_big_five_itm5)
Personality dimension: openness, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi5_kunst,v1_con$v1_bfi_10_bfi5,"v1_big_five_itm5",1)
##                1   2   3    4    5    NA's     
## [1,] No. cases 141 263 178  403  375  183  1543
## [2,] Percent   9.1 17  11.5 26.1 24.3 11.9 100

6. “…is outgoing, sociable” (ordinal [1,2,3,4,5], v1_big_five_itm6)
Personality dimension: extraversion.

big_five_recode(v1_clin$v1_bfi_10_bfi6_gesellig,v1_con$v1_bfi_10_bfi6,"v1_big_five_itm6",0)
##                1   2    3    4    5    NA's     
## [1,] No. cases 87  314  261  499  206  176  1543
## [2,] Percent   5.6 20.3 16.9 32.3 13.4 11.4 100

7. “…tends to find fault with others” (ordinal [1,2,3,4,5], v1_big_five_itm7)
Personality dimension: agreeableness, coding reversed.

big_five_recode(v1_clin$v1_bfi_10_bfi7_kritik,v1_con$v1_bfi_10_bfi7,"v1_big_five_itm7",1)
##                1   2    3    4    5    NA's     
## [1,] No. cases 45  302  364  449  204  179  1543
## [2,] Percent   2.9 19.6 23.6 29.1 13.2 11.6 100

8. “… does a thorough job” (ordinal [1,2,3,4,5], v1_big_five_itm8)
Personality dimension: conscientiousness.

big_five_recode(v1_clin$v1_bfi_10_bfi8_gruendlich,v1_con$v1_bfi_10_bfi8,"v1_big_five_itm8",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 13  104 158  701  390  177  1543
## [2,] Percent   0.8 6.7 10.2 45.4 25.3 11.5 100

9. “…gets nervous easily” (ordinal [1,2,3,4,5], v1_big_five_itm9)
Personality dimension: neuroticism.

big_five_recode(v1_clin$v1_bfi_10_bfi9_unsicher1,v1_con$v1_bfi_10_bfi9,"v1_big_five_itm9",0)
##                1   2    3   4    5   NA's     
## [1,] No. cases 149 380  277 419  145 173  1543
## [2,] Percent   9.7 24.6 18  27.2 9.4 11.2 100

10. “…habe eine aktive Vorstellungskraft, bin phantasievoll.” (ordinal [1,2,3,4,5], v1_big_five_itm10)
Personality dimension: openness.

big_five_recode(v1_clin$v1_bfi_10_bfi10_phantasie,v1_con$v1_bfi_10_bfi10,"v1_big_five_itm10",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 49  154 195  603  368  174  1543
## [2,] Percent   3.2 10  12.6 39.1 23.8 11.3 100

Big-Five personality dimension

The scoring instructions are described in Rammstedt et al. (2012).

Extraversion (continuous, [1,2,3,4,5], v1_big_five_extra)

v1_big_five_extra<-(as.numeric(v1_big_five_itm1)+as.numeric(v1_big_five_itm6))/2
summary(v1_big_five_extra)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.000   3.000   3.108   4.000   5.000     181

Neuroticism (continuous, [1,2,3,4,5], v1_big_five_neuro)

v1_big_five_neuro<-(as.numeric(v1_big_five_itm4)+as.numeric(v1_big_five_itm9))/2
summary(v1_big_five_neuro)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   2.500   3.000   3.052   4.000   5.000     180

Openness (continuous, [1,2,3,4,5], v1_big_five_openn)

v1_big_five_openn<-(as.numeric(v1_big_five_itm5)+as.numeric(v1_big_five_itm10))/2
summary(v1_big_five_openn)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   3.000   3.500   3.624   4.500   5.000     187

Conscientiousness (continuous, [1,2,3,4,5], v1_big_five_consc)

v1_big_five_consc<-(as.numeric(v1_big_five_itm3)+as.numeric(v1_big_five_itm8))/2
summary(v1_big_five_consc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00    3.00    3.50    3.64    4.50    5.00     186

Agreeableness (continuous, [1,2,3,4,5], v1_big_five_agree)

v1_big_five_agree<-(as.numeric(v1_big_five_itm2)+as.numeric(v1_big_five_itm7))/2
summary(v1_big_five_agree)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   3.000   3.500   3.461   4.000   5.000     182

Create dataset

v1_pers<-data.frame(v1_big_five_itm1,v1_big_five_itm2,v1_big_five_itm3,v1_big_five_itm4,
                    v1_big_five_itm5,v1_big_five_itm6,v1_big_five_itm7,v1_big_five_itm8,
                    v1_big_five_itm9,v1_big_five_itm10,v1_big_five_extra,v1_big_five_neuro,
                    v1_big_five_openn,v1_big_five_consc,v1_big_five_agree)

Visit 1: Create dataframe

v1_df<-data.frame(v1_id,
                  v1_rec,
                  v1_dem,
                  v1_eth,
                  v1_psy_trtmt,
                  v1_med,
                  v1_fam_hist,
                  v1_som_dsrdr,
                  v1_subst,
                  v1_scid,
                  v1_symp_panss,
                  v1_symp_ids_c,
                  v1_symp_ymrs,
                  v1_ill_sev,
                  v1_nrpsy,
                  v1_rlgn,
                  v1_cape,
                  v1_sf12,
                  v1_med_adh,
                  v1_bdi2,
                  v1_asrm,
                  v1_mss,
                  v1_leq,
                  v1_whoqol,
                  v1_pers)

Visit 2: Data preparation

Read in data of clinical participants

## [1] 1344

Read in data of control participants

## [1] 329

Modify column names

Only include subjects for which data for the first visit is present

v2_clin<-subset(v2_clin, as.character(v2_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v2_clin)[1]
## [1] 1223
v2_con<-subset(v2_con, as.character(v2_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v2_con)[1]
## [1] 320

Participant identity column (categorical [id], v2_id)

v2_id<-as.factor(c(as.character(v2_clin$mnppsd),as.character(v2_con$mnppsd)))

Visit 2: Recruitment data

Date of interview (categorical [year-month-day], v2_interv_date)

v2_interv_date<-c(as.Date(as.character(v2_clin$v2_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v2_con$v2_rekru_visit_rekr_datum), "%Y%m%d"))

Age at second interview (continuous [years], v2_age)

v2_age_years_clin<-as.numeric(substr(v2_clin$v2_ausschluss1_rekr_datum,1,4))-
  as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))

v2_age_years_con<-as.numeric(substr(v2_con$v2_rekru_visit_rekr_datu,1,4))-
  as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v2_age_years<-c(v2_age_years_clin,v2_age_years_con)

v2_age<-ifelse(c(as.numeric(substr(v2_clin$v2_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v2_con$v2_rekru_visit_rekr_datu,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v2_age_years-1,v2_age_years)
summary(v2_age) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   18.00   30.00   44.00   42.78   53.00   86.00     542

Create dataset

v2_rec<-data.frame(v2_age,v2_interv_date)

Visit 2: Illness episodes between study visits in clinical participants

Clinical study participant are asked whether an acute illness episode occurred since the last study visit. Possible answers are “Y”-yes, “N”-no and “C”-chronic symptomatology. The latter category is for people which continually experience symptoms. If the answer was yes, additional questions were asked about the episodes, if not these are omitted. For participants with chronic symptomatology, the participant is asked about the nature of the chronic symptomatology (manic/depressive/mixed/psychotic) and answers are coded in the questions “Did you experience … symptoms during this illness episode?” (first illness episode).
Importantly, if the participant experienced multiple illness episodes since the last study visit, a set of questions (see below) was supposed to be answered for each illness episode. As most interviewers answered these questions only for a maximum of two illness episodes and few participants experienced more than two illness episodes, data are included only for the first two illness episodes.

Illness episodes since last study visit (only in clinical participants)

“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v2_clin_ill_ep_snc_lst)

v2_clin_ill_ep_snc_lst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_ill_ep_snc_lst<-ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==1,"Y",
                          ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==2,"N",
                            ifelse(c(v2_clin$v2_aktu_situat_aenderung_akt_sit,rep(-999,dim(v2_con)[1]))==3,"C",v2_clin_ill_ep_snc_lst)))

v2_clin_ill_ep_snc_lst<-factor(v2_clin_ill_ep_snc_lst)                         
descT(v2_clin_ill_ep_snc_lst)
##                -999 C   N    Y   <NA>     
## [1,] No. cases 320  88  420  231 484  1543
## [2,] Percent   20.7 5.7 27.2 15  31.4 100

“If yes, how many illness episodes? (continuous [no. illness episodes], v2_clin_no_ep)”

v2_clin_no_ep<-ifelse(v2_clin_ill_ep_snc_lst=="Y",c(v2_clin$v2_aktu_situat_anzahl_episoden,rep(-999,dim(v2_con)[1])),-999)
descT(v2_clin_no_ep)
##                -999 1    2   3   4   5   99  <NA>     
## [1,] No. cases 828  177  29  8   3   2   1   495  1543
## [2,] Percent   53.7 11.5 1.9 0.5 0.2 0.1 0.1 32.1 100

In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).

First illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_man)

v2_clin_fst_ill_ep_man<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_man<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_fst_ill_ep_man)
##                -999 Y   <NA>     
## [1,] No. cases 1039 35  469  1543
## [2,] Percent   67.3 2.3 30.4 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_dep)

v2_clin_fst_ill_ep_dep<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_dep<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_fst_ill_ep_dep)
##                -999 Y   <NA>     
## [1,] No. cases 935  139 469  1543
## [2,] Percent   60.6 9   30.4 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v2_clin_fst_ill_ep_mx<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_mx<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                         -999)

descT(v2_clin_fst_ill_ep_mx)
##                -999 Y  <NA>     
## [1,] No. cases 1060 15 468  1543
## [2,] Percent   68.7 1  30.3 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v2_clin_fst_ill_ep_psy)

v2_clin_fst_ill_ep_psy<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_psy<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_fst_ill_ep_psy)
##                -999 Y   <NA>     
## [1,] No. cases 1011 64  468  1543
## [2,] Percent   65.5 4.1 30.3 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v2_clin_fst_ill_ep_dur)

v2_clin_fst_ill_ep_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_dur<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",
                               ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==2,"two to four weeks", 
                                      ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
                                             ifelse(v2_clin_ill_ep_snc_lst=="N",-999,v2_clin_fst_ill_ep_dur))))

v2_clin_fst_ill_ep_dur<-ordered(v2_clin_fst_ill_ep_dur, 
                               levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_fst_ill_ep_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 740  39                  57               
## [2,] Percent   48   2.5                 3.7              
##      more than four weeks <NA>     
## [1,] 125                  582  1543
## [2,] 8.1                  37.7 100

“During this episode, were you hospitalized?” (dichotomous, v2_clin_fst_ill_ep_hsp)

v2_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="N",-999,
                          ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                            c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v2_con)[1]))==2,"N",
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                                c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y", v2_clin_fst_ill_ep_hsp)))

descT(v2_clin_fst_ill_ep_hsp)
##                -999 N   Y   <NA>     
## [1,] No. cases 740  106 118 579  1543
## [2,] Percent   48   6.9 7.6 37.5 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v2_clin_fst_ill_ep_hsp_dur)

v2_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_hsp_dur<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",  
                              ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
                                ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",    
                                            -999)))

v2_clin_fst_ill_ep_hsp_dur<-ordered(v2_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v2_clin_fst_ill_ep_hsp_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 934  18                  25               
## [2,] Percent   60.5 1.2                 1.6              
##      more than four weeks <NA>     
## [1,] 69                   497  1543
## [2,] 4.5                  32.2 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):

Reason for hospitalization: symptom worsensing (checkbox [Y], v2_clin_fst_ill_ep_symp_wrs)

v2_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_symp_wrs<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v2_con)[1]))==1,"Y",-999)
  
descT(v2_clin_fst_ill_ep_symp_wrs)
##                -999 Y   <NA>     
## [1,] No. cases 976  97  470  1543
## [2,] Percent   63.3 6.3 30.5 100

Reason for hospitalization: self-endangerment (checkbox [Y], v2_clin_fst_ill_ep_slf_end)

v2_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_slf_end<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y", 
                              -999)

descT(v2_clin_fst_ill_ep_slf_end)
##                -999 Y   <NA>     
## [1,] No. cases 1061 14  468  1543
## [2,] Percent   68.8 0.9 30.3 100

Reason for hospitalization: suicidality (checkbox [Y], v2_clin_fst_ill_ep_suic)

v2_clin_fst_ill_ep_suic<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_suic<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y", 
                           -999)

descT(v2_clin_fst_ill_ep_suic)
##                -999 Y   <NA>     
## [1,] No. cases 1056 19  468  1543
## [2,] Percent   68.4 1.2 30.3 100

Reason for hospitalization: endangerment of others (checkbox [Y], v2_clin_fst_ill_ep_oth_end)

v2_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_oth_end<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_fst_ill_ep_oth_end)
##                -999 Y   <NA>     
## [1,] No. cases 1071 4   468  1543
## [2,] Percent   69.4 0.3 30.3 100

Reason for hospitalization: medication change (checkbox [Y], v2_clin_fst_ill_ep_med_chg)

v2_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_fst_ill_ep_med_chg<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_fst_ill_ep_med_chg)
##                -999 Y   <NA>     
## [1,] No. cases 1057 17  469  1543
## [2,] Percent   68.5 1.1 30.4 100

Reason for hospitalization: other (checkbox [Y], v2_clin_fst_ill_ep_othr)

v2_clin_fst_ill_ep_othr<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_fst_ill_ep_othr<-ifelse(v2_clin_fst_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
 
descT(v2_clin_fst_ill_ep_othr)
##                -999 Y   <NA>     
## [1,] No. cases 1050 25  468  1543
## [2,] Percent   68   1.6 30.3 100

Second illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_man)

v2_clin_sec_ill_ep_man<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_man<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",-999)

descT(v2_clin_sec_ill_ep_man)
##                -999 Y   <NA>     
## [1,] No. cases 858  5   680  1543
## [2,] Percent   55.6 0.3 44.1 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_dep) #frstill

v2_clin_sec_ill_ep_dep<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_dep<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",
                          -999)

descT(v2_clin_sec_ill_ep_dep)
##                -999 Y   <NA>     
## [1,] No. cases 841  22  680  1543
## [2,] Percent   54.5 1.4 44.1 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v2_clin_sec_ill_ep_mx<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_mx<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y", 
                         -999)

descT(v2_clin_sec_ill_ep_mx)
##                -999 Y   <NA>     
## [1,] No. cases 861  2   680  1543
## [2,] Percent   55.8 0.1 44.1 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v2_clin_sec_ill_ep_psy)

v2_clin_sec_ill_ep_psy<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_psy<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y", 
                          -999)

descT(v2_clin_sec_ill_ep_psy)
##                -999 Y   <NA>     
## [1,] No. cases 855  8   680  1543
## [2,] Percent   55.4 0.5 44.1 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v2_clin_sec_ill_ep_dur)

v2_clin_sec_ill_ep_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_dur<-ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==1,"less than two weeks", 
                           ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",    
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",  
                                ifelse(v2_clin_ill_ep_snc_lst=="N",-999,v2_clin_sec_ill_ep_dur))))
 
v2_clin_sec_ill_ep_dur<-ordered(v2_clin_sec_ill_ep_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_sec_ill_ep_dur)
##                less than two weeks two to four weeks more than four weeks
## [1,] No. cases 8                   13                14                  
## [2,] Percent   0.5                 0.8               0.9                 
##      <NA>     
## [1,] 1508 1543
## [2,] 97.7 100

“During this episode, were you hospitalized?” (dichotomous, v2_clin_sec_ill_ep_hsp)

v2_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="N",-999,
                          ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                            c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v2_con)[1]))==2,"N",
                              ifelse(v2_clin_ill_ep_snc_lst=="Y" &
                                c(v2_clin$v2_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y", v2_clin_sec_ill_ep_hsp)))

v2_clin_sec_ill_ep_hsp<-factor(v2_clin_sec_ill_ep_hsp)                         
descT(v2_clin_sec_ill_ep_hsp)
##                -999 N  Y   <NA>     
## [1,] No. cases 740  16 18  769  1543
## [2,] Percent   48   1  1.2 49.8 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v2_clin_sec_ill_ep_hsp_dur)

v2_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_hsp_dur<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==1,"less than two weeks",  
                              ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
                                ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",    
                                            -999)))

v2_clin_sec_ill_ep_hsp_dur<-ordered(v2_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v2_clin_sec_ill_ep_hsp_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 844  1                   4                
## [2,] Percent   54.7 0.1                 0.3              
##      more than four weeks <NA>     
## [1,] 9                    685  1543
## [2,] 0.6                  44.4 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v2_clin_sec_ill_ep_symp_wrs)

v2_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_symp_wrs<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v2_con)[1]))==1,"Y",-999)
  
descT(v2_clin_sec_ill_ep_symp_wrs)
##                -999 Y   <NA>     
## [1,] No. cases 852  11  680  1543
## [2,] Percent   55.2 0.7 44.1 100

Reason for hospitalization: self-endangerment (checkbox [Y], v2_clin_sec_ill_ep_slf_end)

v2_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_slf_end<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y", 
                              -999)

descT(v2_clin_sec_ill_ep_slf_end)
##                -999 Y   <NA>     
## [1,] No. cases 862  1   680  1543
## [2,] Percent   55.9 0.1 44.1 100

Reason for hospitalization: suicidality (checkbox [Y], v2_clin_sec_ill_ep_suic)

v2_clin_sec_ill_ep_suic<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_suic<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y", 
                           -999)

descT(v2_clin_sec_ill_ep_suic)
##                -999 Y   <NA>     
## [1,] No. cases 860  3   680  1543
## [2,] Percent   55.7 0.2 44.1 100

Reason for hospitalization: endangerment of others (checkbox [Y], v2_clin_sec_ill_ep_oth_end)

v2_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_oth_end<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_sec_ill_ep_oth_end)
##                -999 <NA>     
## [1,] No. cases 863  680  1543
## [2,] Percent   55.9 44.1 100

Reason for hospitalization: medication change (checkbox [Y], v2_clin_sec_ill_ep_med_chg)

v2_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_sec_ill_ep_med_chg<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)

descT(v2_clin_sec_ill_ep_med_chg)
##                -999 Y   <NA>     
## [1,] No. cases 862  1   680  1543
## [2,] Percent   55.9 0.1 44.1 100

Reason for hospitalization: other (checkbox [Y], v2_clin_sec_ill_ep_othr)

v2_clin_sec_ill_ep_othr<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_sec_ill_ep_othr<-ifelse(v2_clin_sec_ill_ep_hsp=="Y" & v2_clin_ill_ep_snc_lst=="Y" & c(v2_clin$v2_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v2_con)[1]))==1, "Y",-999)
 
descT(v2_clin_sec_ill_ep_othr)
##                -999 Y   <NA>     
## [1,] No. cases 858  5   680  1543
## [2,] Percent   55.6 0.3 44.1 100

Additional psychiatric hospitalization as in- or daypatient? (dichotomous, v2_clin_add_oth_hsp)

v2_clin_add_oth_hsp<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_add_oth_hsp<-ifelse(v2_clin_ill_ep_snc_lst=="Y" &
  c(v2_clin$v2_aktu_situat_aenderung_aufent,rep(-999,dim(v2_con)[1]))==1,"Y","N")

descT(v2_clin_add_oth_hsp)
##                N    Y   <NA>     
## [1,] No. cases 1019 24  500  1543
## [2,] Percent   66   1.6 32.4 100

If yes, how many other hospitalizations? (continous [no. of hospitalizations], v2_clin_oth_hsp_nmb)

v2_clin_oth_hsp_nmb<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_oth_hsp_nmb<-ifelse(v2_clin_add_oth_hsp=="Y",
          c(v2_clin$v2_aktu_situat_aenderung_anzahl,rep(-999,dim(v2_con)[1])),-999)

descT(v2_clin_oth_hsp_nmb)
##                -999 1   2   3   <NA>     
## [1,] No. cases 1019 20  1   1   502  1543
## [2,] Percent   66   1.3 0.1 0.1 32.5 100

If yes, duration of other hospitalizations? (ordinal, v2_clin_oth_hsp_dur)

v2_clin_oth_hsp_dur<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_clin_oth_hsp_dur<-
  ifelse(v2_clin_add_oth_hsp=="Y" & 
           c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==1,"less than two weeks", 
   ifelse(v2_clin_add_oth_hsp=="Y" & 
            c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==2,"two to four weeks",
    ifelse(v2_clin_add_oth_hsp=="Y" & 
             c(v2_clin$v2_aktu_situat_aenderung_dauer,rep(-999,dim(v2_con)[1]))==3,"more than four weeks",
     ifelse(v2_clin_add_oth_hsp=="N",-999,v2_clin_add_oth_hsp))))

v2_clin_oth_hsp_dur<-ordered(v2_clin_oth_hsp_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v2_clin_oth_hsp_dur)
##                less than two weeks two to four weeks more than four weeks
## [1,] No. cases 7                   6                 10                  
## [2,] Percent   0.5                 0.4               0.6                 
##      <NA>     
## [1,] 1520 1543
## [2,] 98.5 100

If yes, reason for other hospitalization(s) medication change? (checkbox [Y], v2_clin_othr_psy_med)

v2_clin_othr_psy_med<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_clin_othr_psy_med<-ifelse(v2_clin_add_oth_hsp=="Y" & v2_clin_add_oth_hsp=="Y" & 
      c(v2_clin$v2_aktu_situat_aenderung_medikament,rep(-999,dim(v2_con)[1]))==1,"Y",
                        ifelse(v2_clin_add_oth_hsp=="N",-999,v2_clin_othr_psy_med))

descT(v2_clin_othr_psy_med)
##                -999 Y   <NA>     
## [1,] No. cases 1019 2   522  1543
## [2,] Percent   66   0.1 33.8 100

Current psychiatric treatment of both clinical and control participants (ordinal [1,2,3,4], v2_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v2_clin_cur_psy_trm<-rep(NA,dim(v2_clin)[1])
v2_con_cur_psy_trm<-rep(NA,dim(v2_con)[1])

v2_clin_cur_psy_trm<-ifelse(v2_clin$v2_aktu_situat_psybehandlung==0,"1",
                        ifelse(v2_clin$v2_aktu_situat_psybehandlung==3,"2", 
                          ifelse(v2_clin$v2_aktu_situat_psybehandlung==2,"3",
                            ifelse(v2_clin$v2_aktu_situat_psybehandlung==1,"4",v2_clin_cur_psy_trm)))) 

v2_con_cur_psy_trm<-ifelse(v2_con$v2_bildung_beruf_psybehandlung==0,"1",
                      ifelse(v2_con$v2_bildung_beruf_psybehandlung==3,"2",
                        ifelse(v2_con$v2_bildung_beruf_psybehandlung==2,"3",
                          ifelse(v2_con$v2_bildung_beruf_psybehandlung==1,"4",v2_con_cur_psy_trm))))

v2_cur_psy_trm<-factor(c(v2_clin_cur_psy_trm,v2_con_cur_psy_trm),ordered=T)
descT(v2_cur_psy_trm)
##                1    2    3   4   <NA>     
## [1,] No. cases 300  618  11  34  580  1543
## [2,] Percent   19.4 40.1 0.7 2.2 37.6 100

Create dataset

v2_clin_ill_ep<-data.frame(v2_clin_ill_ep_snc_lst,
                           v2_clin_no_ep,
                           v2_clin_fst_ill_ep_man,
                           v2_clin_fst_ill_ep_dep,
                           v2_clin_fst_ill_ep_mx,
                           v2_clin_fst_ill_ep_psy,
                           v2_clin_fst_ill_ep_dur,
                           v2_clin_fst_ill_ep_hsp,
                           v2_clin_fst_ill_ep_hsp_dur,
                           v2_clin_fst_ill_ep_symp_wrs,
                           v2_clin_fst_ill_ep_slf_end,
                           v2_clin_fst_ill_ep_suic,
                           v2_clin_fst_ill_ep_oth_end,
                           v2_clin_fst_ill_ep_med_chg,
                           v2_clin_fst_ill_ep_othr,
                           v2_clin_sec_ill_ep_man,
                           v2_clin_sec_ill_ep_dep,
                           v2_clin_sec_ill_ep_mx,
                           v2_clin_sec_ill_ep_psy,
                           v2_clin_sec_ill_ep_dur,
                           v2_clin_sec_ill_ep_hsp,
                           v2_clin_sec_ill_ep_hsp_dur,
                           v2_clin_sec_ill_ep_symp_wrs,
                           v2_clin_sec_ill_ep_slf_end,
                           v2_clin_sec_ill_ep_suic,
                           v2_clin_sec_ill_ep_oth_end,
                           v2_clin_sec_ill_ep_med_chg,
                           v2_clin_sec_ill_ep_othr,
                           v2_clin_add_oth_hsp,
                           v2_clin_oth_hsp_nmb,
                           v2_clin_oth_hsp_dur,
                           v2_clin_othr_psy_med,
                           v2_cur_psy_trm)

Visit 2: Demographic information

Did your marital status change since the last study visit? (dichotomous, v2_cng_mar_stat)

v2_clin_cng_mar_stat<-rep(NA,dim(v2_clin)[1]) 
v2_clin_cng_mar_stat<-ifelse(v2_clin$v2_aktu_situat_fam_stand==1, "Y", 
                        ifelse(v2_clin$v2_aktu_situat_fam_stand==2, "N", v2_clin_cng_mar_stat))

v2_con_cng_mar_stat<-rep(NA,dim(v2_con)[1]) 
v2_con_cng_mar_stat<-ifelse(v2_con$v2_famil_wohn_fam_stand==1, "Y", 
                        ifelse(v2_con$v2_famil_wohn_fam_stand==2, "N", v2_con_cng_mar_stat))

v2_cng_mar_stat<-factor(c(v2_clin_cng_mar_stat,v2_con_cng_mar_stat))

Marital status (categorical [married, married but living separately, single, divorced, widowed], v2_marital_stat)

v2_clin_marital_stat<-rep(NA,dim(v2_clin)[1])
v2_clin_marital_stat<-ifelse(v2_clin$v2_aktu_situat_fam_familienstand==1,"Married", 
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==2,"Married_living_sep",
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==3,"Single",
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==4,"Divorced",
                 ifelse(v2_clin$v2_aktu_situat_fam_familienstand==5,"Widowed",v2_clin_marital_stat)))))

v2_con_marital_stat<-rep(NA,dim(v2_con)[1])
v2_con_marital_stat<-ifelse(v2_con$v2_famil_wohn_fam_famstand==1,"Married", 
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==2,"Married_living_sep",
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==3,"Single",
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==4,"Divorced",
                 ifelse(v2_con$v2_famil_wohn_fam_famstand==5,"Widowed",v2_con_marital_stat)))))

v2_marital_stat<-factor(c(v2_clin_marital_stat,v2_con_marital_stat))
desc(v2_marital_stat)
##                Divorced Married Married_living_sep Single Widowed NA's
## [1,] No. cases 127      232     37                 564    17      566 
## [2,] Percent   8.2      15      2.4                36.6   1.1     36.7
##          
## [1,] 1543
## [2,] 100

Relationship status

“Do you currently have a partner?” (dichotomous, v2_partner)

v2_clin_partner<-rep(NA,dim(v2_clin)[1])
v2_clin_partner<-ifelse(v2_clin$v2_aktu_situat_fam_fest_partner==1,"Y",
            ifelse(v2_clin$v2_aktu_situat_fam_fest_partner==2,"N",v2_clin_partner))

v2_con_partner<-rep(NA,dim(v2_con)[1])
v2_con_partner<-ifelse(v2_con$v2_famil_wohn_fam_partner==1,"Y",
            ifelse(v2_con$v2_famil_wohn_fam_partner==2,"N",v2_con_partner))


v2_partner<-factor(c(v2_clin_partner,v2_con_partner))
descT(v2_partner)
##                N    Y    <NA>     
## [1,] No. cases 473  484  586  1543
## [2,] Percent   30.7 31.4 38   100

Children

Biological (continuous [number], v2_no_bio_chld)

v2_no_bio_chld<-c(v2_clin$v2_aktu_situat_fam_kind_gesamt,v2_con$v2_famil_wohn_fam_lkind)
descT(v2_no_bio_chld)
##                0    1    2   3   4   5   <NA>     
## [1,] No. cases 600  171  118 59  13  5   577  1543
## [2,] Percent   38.9 11.1 7.6 3.8 0.8 0.3 37.4 100

Non-biological

Adoptive children (continuous [number], v2_no_adpt_chld)

v2_no_adpt_chld<-c(v2_clin$v2_aktu_situat_fam_adopt_gesamt,v2_con$v2_famil_wohn_fam_adkind)
descT(v2_no_adpt_chld)  
##                0   1   2   <NA>     
## [1,] No. cases 941 2   2   598  1543
## [2,] Percent   61  0.1 0.1 38.8 100

Step children (continuous [number], v2_stp_chld)

v2_stp_chld<-c(v2_clin$v2_aktu_situat_fam_stift_gesamt,v2_con$v2_famil_wohn_fam_skind)
descT(v2_stp_chld)      
##                0    1   2   3   4   <NA>     
## [1,] No. cases 871  42  17  4   2   607  1543
## [2,] Percent   56.4 2.7 1.1 0.3 0.1 39.3 100

Change in housing situation since last study visit? (dichotomous, v2_chg_hsng)

v2_clin_chg_hsng<-rep(NA,dim(v2_clin)[1])
v2_clin_chg_hsng<-ifelse(v2_clin$v2_wohnsituation_wohn_aenderung==1,"Y",
                  ifelse(v2_clin$v2_wohnsituation_wohn_aenderung==2,"N",v2_clin_chg_hsng))  

v2_con_chg_hsng<-rep(NA,dim(v2_con)[1])
v2_con_chg_hsng<-ifelse(v2_con$v2_famil_wohn_wohn_stand==1,"Y",
                 ifelse(v2_con$v2_famil_wohn_wohn_stand==2,"N",v2_con_chg_hsng))

v2_chg_hsng<-factor(c(v2_clin_chg_hsng,v2_con_chg_hsng))
descT(v2_chg_hsng)
##                N    Y   <NA>     
## [1,] No. cases 863  129 551  1543
## [2,] Percent   55.9 8.4 35.7 100

Living alone (dichotomous, v2_liv_aln)

v2_clin_liv_aln<-rep(NA,dim(v2_clin)[1])
v2_clin_liv_aln<-ifelse(v2_clin$v2_wohnsituation_wohn_allein==1,"Y",    
                 ifelse(v2_clin$v2_wohnsituation_wohn_allein==0,"N",v2_clin_liv_aln))   

v2_con_liv_aln<-rep(NA,dim(v2_con)[1])
v2_con_liv_aln<-ifelse(v2_con$v2_famil_wohn_wohn_allein==1,"Y", 
                 ifelse(v2_con$v2_famil_wohn_wohn_allein==0,"N",v2_con_liv_aln))
                 
v2_liv_aln<-factor(c(v2_clin_liv_aln,v2_con_liv_aln))
descT(v2_liv_aln)
##                N    Y    <NA>     
## [1,] No. cases 640  374  529  1543
## [2,] Percent   41.5 24.2 34.3 100

Employment

Did your employment situation change since the last study visit?

v2_clin_chg_empl_stat<-rep(NA,dim(v2_clin)[1]) 
v2_clin_chg_empl_stat<-ifelse(v2_clin$v2_wohnsituation_erwerb_aenderung==1, "Y", 
                  ifelse(v2_clin$v2_wohnsituation_erwerb_aenderung==2, "N",v2_clin_chg_empl_stat))

v2_con_chg_empl_stat<-rep(NA,dim(v2_con)[1]) 
v2_con_chg_empl_stat<-ifelse(v2_con$v2_bildung_beruf_bild_stand==1, "Y", 
                  ifelse(v2_con$v2_bildung_beruf_bild_stand==2, "N",v2_con_chg_empl_stat))

v2_chg_empl_stat<-factor(c(v2_clin_chg_empl_stat,v2_con_chg_empl_stat))
descT(v2_chg_empl_stat)
##                N    Y   <NA>     
## [1,] No. cases 842  142 559  1543
## [2,] Percent   54.6 9.2 36.2 100

Currently paid employment (dichotomous, v2_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v2_clin_curr_paid_empl<-rep(NA,dim(v2_clin)[1])
v2_clin_curr_paid_empl<-ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==1,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==2,"Y",   
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==3,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==4,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==5,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==6,-999,  
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==7,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==8,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==9,"Y",
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==10,"Y",  
                        ifelse(v2_clin$v2_wohnsituation_erwerbstaetig==11,"N",v2_clin_curr_paid_empl)))))))))))

v2_con_curr_paid_empl<-rep(NA,dim(v2_con)[1])
v2_con_curr_paid_empl<-ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==1,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==2,"Y",    
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==3,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==4,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==5,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==6,-999,   
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==7,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==8,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==9,"Y",
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==10,"Y",   
                        ifelse(v2_con$v2_bildung_beruf_erwerb_taetig==11,"N",v2_con_curr_paid_empl)))))))))))

v2_curr_paid_empl<-factor(c(v2_clin_curr_paid_empl,v2_con_curr_paid_empl))
descT(v2_curr_paid_empl)
##                -999 N    Y    <NA>     
## [1,] No. cases 23   454  495  571  1543
## [2,] Percent   1.5  29.4 32.1 37   100

Disability pension due to psychological/psychiatric illness (dichotomous, v2_disabl_pens)

NB: Not available (-999) in control participants

v2_clin_disabl_pens<-rep(NA,dim(v2_clin)[1])
v2_clin_disabl_pens<-ifelse(v2_clin$v2_wohnsituation_rente_psych==1,"Y",        
                     ifelse(v2_clin$v2_wohnsituation_rente_psych==2,"N",v2_clin_disabl_pens))       

v2_con_disabl_pens<-rep(-999,dim(v2_con)[1])

v2_disabl_pens<-factor(c(v2_clin_disabl_pens,v2_con_disabl_pens))
descT(v2_disabl_pens)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  306  261  656  1543
## [2,] Percent   20.7 19.8 16.9 42.5 100

Employed in workshop for handicapped persons (dichotomous, v2_spec_emp)

v2_clin_spec_emp<-rep(NA,dim(v2_clin)[1])
v2_clin_spec_emp<-ifelse(v2_clin$v2_wohnsituation_erwerb_werk==1,"Y",           
                  ifelse(v2_clin$v2_wohnsituation_erwerb_werk==2,"N",v2_clin_spec_emp))         

v2_con_spec_emp<-rep(NA,dim(v2_con)[1])
v2_con_spec_emp<-ifelse(v2_con$v2_bildung_beruf_erwerb_wfbm==1,"Y",         
                 ifelse(v2_con$v2_bildung_beruf_erwerb_wfbm==2,"N",v2_con_spec_emp))            


v2_spec_emp<-factor(c(v2_clin_spec_emp,v2_con_spec_emp))
descT(v2_spec_emp)
##                N   Y  <NA>     
## [1,] No. cases 355 62 1126 1543
## [2,] Percent   23  4  73   100

Weeks of work absence due to psychological distress in past six months (continuous [weeks], v2_wrk_abs_pst_6_mths)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.

v2_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v2_clin)[1])
v2_clin_wrk_abs_pst_6_mths<-ifelse((v2_clin$v2_wohnsituation_erwerb_unbekannt==1 | v2_clin$v2_wohnsituation_erwerb_rente==1 |  
                                 v2_clin$v2_wohnsituation_erwerb_fehlen>26),-999, v2_clin$v2_wohnsituation_erwerb_fehlen)

v2_con_wrk_abs_pst_6_mths<-rep(NA,dim(v2_con)[1])
v2_con_wrk_abs_pst_6_mths<-ifelse((v2_con$v2_bildung_beruf_erwerb_ausfallu==1 | v2_con$v2_bildung_beruf_erwerb_rente==1 |  
                                 v2_con$v2_bildung_beruf_erwerb_ausfallm>26),-999, v2_con$v2_bildung_beruf_erwerb_ausfallm)

v2_wrk_abs_pst_6_mths<-c(v2_clin_wrk_abs_pst_6_mths,v2_con_wrk_abs_pst_6_mths)
descT(v2_wrk_abs_pst_6_mths)
##                -999 0    1   2   3   4   5   6   7   8   10  12  13  14 
## [1,] No. cases 351  274  10  9   10  14  5   9   1   10  4   8   2   1  
## [2,] Percent   22.7 17.8 0.6 0.6 0.6 0.9 0.3 0.6 0.1 0.6 0.3 0.5 0.1 0.1
##      15  16  18  19  20  24  25  26 <NA>     
## [1,] 1   6   1   1   8   32  4   15 767  1543
## [2,] 0.1 0.4 0.1 0.1 0.5 2.1 0.3 1  49.7 100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v2_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v2_clin_cur_work_restr<-rep(NA,dim(v2_clin)[1])
v2_clin_cur_work_restr<-ifelse(v2_clin$v2_wohnsituation_erwerb_psysymptom==1,"Y",   
                    ifelse(v2_clin$v2_wohnsituation_erwerb_psysymptom==2,"N",v2_clin_cur_work_restr))    

v2_con_cur_work_restr<-rep(NA,dim(v2_con)[1])
v2_con_cur_work_restr<-ifelse(v2_con$v2_bildung_beruf_erwerb_psyeinsch==1,"Y",  
                    ifelse(v2_con$v2_bildung_beruf_erwerb_psyeinsch==2,"N",v2_con_cur_work_restr))   

v2_cur_work_restr<-factor(c(v2_clin_cur_work_restr,v2_con_cur_work_restr))
descT(v2_cur_work_restr)
##                N    Y    <NA>     
## [1,] No. cases 568  341  634  1543
## [2,] Percent   36.8 22.1 41.1 100

Self-reported Weight (continuous [kilograms], v2_weight)

v2_weight<-c(v2_clin$v2_wohnsituation_erwerb_gewicht,v2_con$v2_bildung_beruf_erwerb_gewicht)
summary(v2_weight)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   43.00   69.00   80.00   83.52   95.00  171.00     575

BMI (continuous [BMI], v2_bmi)

We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v2_bmi<-v2_weight/(v1_height/100)^2
summary(v2_bmi)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.90   23.15   26.57   27.54   30.79   66.17     579

Create dataset

v2_dem<-data.frame(v2_cng_mar_stat,v2_marital_stat,v2_partner,v2_no_bio_chld,v2_no_adpt_chld,v2_stp_chld,v2_chg_hsng,v2_liv_aln,
                    v2_chg_empl_stat,v2_curr_paid_empl,v2_disabl_pens,v2_spec_emp,v2_wrk_abs_pst_6_mths,v2_cur_work_restr,
                    v2_weight,v2_bmi)

Visit 2: Events precipitating first illness episode (categorical [N,U,Y], v2_evnt_prcp_illn)

The participant is asked the following question: “Before your first illness episode, was there a special event that could have triggered the disease? If yes, please describe.” The following answering alternatives were given: N-“No”, U-“Unclear if trigger, namely”, Y-“yes, namely”.

v2_evnt_prcp_illn<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_evnt_prcp_illn<-ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==1,"N",
                   ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==2,"U",
                   ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==3,"Y",
                   ifelse(c(v2_clin$v2_ergaenz_leq_ergaenz_frage1,rep(-999,dim(v2_con)[1]))==-999,-999,v2_evnt_prcp_illn))))

v2_evnt_prcp_illn<-factor(v2_evnt_prcp_illn)
descT(v2_evnt_prcp_illn)
##                -999 N    U   Y    <NA>     
## [1,] No. cases 320  159  90  314  660  1543
## [2,] Percent   20.7 10.3 5.8 20.3 42.8 100

If unclear or yes, find the event described in the following item (categorical [text], v2_evnt_prcp_illn_txt) Output masked, as this is very sensitive information.

Create dataset

v2_ev_prc_fst_ep<-data.frame(v2_evnt_prcp_illn,v2_evnt_prcp_illn_txt)

Visit 2: Life events precipitating illness episode between study visits (only in clinical participants)

If there occurred one or more illness episodes between study visits, participants were asked whether one of the life events they experienced and coded in the LEQ may have precipitated the episode.

To systematically evaluate this, each life event coded by the participant in the LEQ was afterwards evaluated if it:

A Occurred before the episode B Was, in the opinion of the participant, a precipitating factor for the illness episode C Which ife event, expressed as the corresponding item of the LEQ (“item number…”) she or he experienced

As these items were answered by only a fraction of the patients, these were autmatically recoded using a for loop and descriptive statistics on each item are not given here. The items are, however, included in the dataset as “v2_evnt_prcp_b4_1” to “v2_evnt_prcp_b4_31” (A), “v2_evnt_prcp_it_1” to “v2_evnt_prcp_it_31” (B), and “v2_evnt_prcp_it_1” to “v2_evnt_prcp_it_31” (C).

**Life events: Occurred before illness episode? (dichotomous, v2_evnt_prcp_b4_*)**

for(i in 1:length(grep("v2_ergaenz_leq_leq_zeit_31055_",names(v2_clin)))){
  b4_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_zeit_31055_",names(v2_clin))[i]],
                   paste("v2_evnt_prcp_b4_",i,sep=""))
}

**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v2_evnt_prcp_f_*)**

for(i in 1:length(grep("v2_ergaenz_leq_leq_ausloeser_31055_",names(v2_clin)))){
  prcp_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_ausloeser_31055_",names(v2_clin))[i]],
                   paste("v2_evnt_prcp_f_",i,sep=""))
}

**Life events: LEQ item number (categorical [LEQ item number], v2_evnt_prcp_it_*)**

for(i in 1:length(grep("v2_ergaenz_leq_leq_item_31055_",names(v2_clin)))){
  leq_event_recode_v2(v2_clin[,grep("v2_ergaenz_leq_leq_item_31055_",names(v2_clin))[i]],
                   paste("v2_evnt_prcp_it_",i,sep=""))
}

Create dataset

v2_leprcp<-data.frame(v2_evnt_prcp_it_1,v2_evnt_prcp_b4_1,v2_evnt_prcp_f_1,
                      v2_evnt_prcp_it_2,v2_evnt_prcp_b4_2,v2_evnt_prcp_f_2,
                      v2_evnt_prcp_it_3,v2_evnt_prcp_b4_3,v2_evnt_prcp_f_3,
                      v2_evnt_prcp_it_4,v2_evnt_prcp_b4_4,v2_evnt_prcp_f_4,
                      v2_evnt_prcp_it_5,v2_evnt_prcp_b4_5,v2_evnt_prcp_f_5,
                      v2_evnt_prcp_it_6,v2_evnt_prcp_b4_6,v2_evnt_prcp_f_6,
                      v2_evnt_prcp_it_7,v2_evnt_prcp_b4_7,v2_evnt_prcp_f_7,
                      v2_evnt_prcp_it_8,v2_evnt_prcp_b4_8,v2_evnt_prcp_f_8,
                      v2_evnt_prcp_it_9,v2_evnt_prcp_b4_9,v2_evnt_prcp_f_9,
                      v2_evnt_prcp_it_10,v2_evnt_prcp_b4_10,v2_evnt_prcp_f_10,
                      v2_evnt_prcp_it_11,v2_evnt_prcp_b4_11,v2_evnt_prcp_f_11,
                      v2_evnt_prcp_it_12,v2_evnt_prcp_b4_12,v2_evnt_prcp_f_12,
                      v2_evnt_prcp_it_13,v2_evnt_prcp_b4_13,v2_evnt_prcp_f_13,
                      v2_evnt_prcp_it_14,v2_evnt_prcp_b4_14,v2_evnt_prcp_f_14,
                      v2_evnt_prcp_it_15,v2_evnt_prcp_b4_15,v2_evnt_prcp_f_15,
                      v2_evnt_prcp_it_16,v2_evnt_prcp_b4_16,v2_evnt_prcp_f_16,
                      v2_evnt_prcp_it_17,v2_evnt_prcp_b4_17,v2_evnt_prcp_f_17,
                      v2_evnt_prcp_it_18,v2_evnt_prcp_b4_18,v2_evnt_prcp_f_18,
                      v2_evnt_prcp_it_19,v2_evnt_prcp_b4_19,v2_evnt_prcp_f_19,
                      v2_evnt_prcp_it_20,v2_evnt_prcp_b4_20,v2_evnt_prcp_f_20,
                      v2_evnt_prcp_it_21,v2_evnt_prcp_b4_21,v2_evnt_prcp_f_21,
                      v2_evnt_prcp_it_22,v2_evnt_prcp_b4_22,v2_evnt_prcp_f_22,
                      v2_evnt_prcp_it_23,v2_evnt_prcp_b4_23,v2_evnt_prcp_f_23,
                      v2_evnt_prcp_it_24,v2_evnt_prcp_b4_24,v2_evnt_prcp_f_24,
                      v2_evnt_prcp_it_25,v2_evnt_prcp_b4_25,v2_evnt_prcp_f_25,
                      v2_evnt_prcp_it_26,v2_evnt_prcp_b4_26,v2_evnt_prcp_f_26,
                      v2_evnt_prcp_it_27,v2_evnt_prcp_b4_27,v2_evnt_prcp_f_27,
                      v2_evnt_prcp_it_28,v2_evnt_prcp_b4_28,v2_evnt_prcp_f_28,
                      v2_evnt_prcp_it_29,v2_evnt_prcp_b4_29,v2_evnt_prcp_f_29,
                      v2_evnt_prcp_it_30,v2_evnt_prcp_b4_30,v2_evnt_prcp_f_30,
                      v2_evnt_prcp_it_31,v2_evnt_prcp_b4_31,v2_evnt_prcp_f_31)

Visit 2: Suicide attempts and suicidal ideation since last study visit

Please note that all of the following questions refer to suicide attempts and suicidal ideation occurring since the last study visit.

Suicidal ideation

Suicidal ideation since last study visit (dichotomous, v2_suic_ide_snc_lst_vst)

Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.

v2_suic_ide_snc_lst_vst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_suic_ide_snc_lst_vst<-ifelse(c(v2_clin$v2_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v2_con)[1]))==1, "N", 
                            ifelse(c(v2_clin$v2_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v2_con)[1]))==3, "Y",                                                 v2_suic_ide_snc_lst_vst))

v2_suic_ide_snc_lst_vst<-factor(v2_suic_ide_snc_lst_vst)
descT(v2_suic_ide_snc_lst_vst)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  527  192  504  1543
## [2,] Percent   20.7 34.2 12.4 32.7 100

Suicidal ideation detailed (ordinal [1,2,3,4], v2_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v2_scid_suic_ide<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_scid_suic_ide<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==3, "3",
                                       ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v2_con)[1]))==4, "4",-999))))

v2_scid_suic_ide<-factor(v2_scid_suic_ide,ordered=T)                                   
descT(v2_scid_suic_ide)
##                -999 1   2   3   4  <NA>     
## [1,] No. cases 847  113 24  36  16 507  1543
## [2,] Percent   54.9 7.3 1.6 2.3 1  32.9 100

Thoughts about methods (ordinal [1,2,3], v2_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.

v2_scid_suic_thght_mth<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_scid_suic_thght_mth<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v2_con)[1]))==3, "3",-999)))

v2_scid_suic_thght_mth<-factor(v2_scid_suic_thght_mth,ordered=T)                                   
descT(v2_scid_suic_thght_mth)
##                -999 1   2  3   <NA>     
## [1,] No. cases 847  99  61 29  507  1543
## [2,] Percent   54.9 6.4 4  1.9 32.9 100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v2_scid_suic_note_thgts)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v2_scid_suic_note_thgts<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_scid_suic_note_thgts<-ifelse(v2_suic_ide_snc_lst_vst=="Y"&c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==3, "3",
                                       ifelse(v2_suic_ide_snc_lst_vst=="Y" & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==4, "4",-999))))

v2_scid_suic_note_thgts<-factor(v2_scid_suic_note_thgts,ordered=T)                                   
descT(v2_scid_suic_note_thgts)
##                -999 1    2   3   4   <NA>     
## [1,] No. cases 847  171  9   3   5   508  1543
## [2,] Percent   54.9 11.1 0.6 0.2 0.3 32.9 100

Suicide attemps

Suicide attempt since last study visit (ordinal [1,2,3], v2_suic_attmpt_snc_lst_vst)

This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.

v2_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_suic_attmpt_snc_lst_vst<-ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==1, "1",
                         ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==2, "2",
                                ifelse(c(v2_clin$v2_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v2_con)[1]))==3, "3",-999)))

v2_suic_attmpt_snc_lst_vst<-factor(v2_suic_attmpt_snc_lst_vst,ordered=T)                                   
descT(v2_suic_attmpt_snc_lst_vst)
##                -999 1    2   3   <NA>     
## [1,] No. cases 320  686  2   13  522  1543
## [2,] Percent   20.7 44.5 0.1 0.8 33.8 100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v2_no_suic_attmpt)

This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.

v2_no_suic_attmpt<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))
v2_no_suic_attmpt<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999, ifelse(v2_suic_attmpt_snc_lst_vst>1, c(v2_clin$v2_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v2_con)[1])),v2_no_suic_attmpt))

v2_no_suic_attmpt<-factor(v2_no_suic_attmpt,ordered=T)
descT(v2_no_suic_attmpt)
##                -999 1   3   <NA>     
## [1,] No. cases 1006 12  1   524  1543
## [2,] Percent   65.2 0.8 0.1 34   100

Preparation of suicide attempt (ordinal [1,2,3,4], v2_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.

v2_prep_suic_attp_ord<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_prep_suic_attp_ord<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999, 
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==1, "1",
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==2, "2",             
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==3, "3",
                              ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v2_con)[1]))==4, "4",
                              v2_prep_suic_attp_ord))))) 

v2_prep_suic_attp_ord<-factor(v2_prep_suic_attp_ord,ordered=T)
descT(v2_prep_suic_attp_ord)
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1006 5   1   4   4   523  1543
## [2,] Percent   65.2 0.3 0.1 0.3 0.3 33.9 100

Suicidal attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v2_suic_note_attmpt)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v2_suic_note_attmpt<-c(rep(NA,dim(v2_clin)[1]),rep(-999,dim(v2_con)[1]))

v2_suic_note_attmpt<-ifelse(v2_suic_attmpt_snc_lst_vst==1, -999, 
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==1, "1",
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==2, "2",
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==3, "3",
                            ifelse(v2_suic_attmpt_snc_lst_vst>1 & c(v2_clin$v2_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v2_con)[1]))==4, "4",
                            v2_suic_note_attmpt))))) 

v2_suic_note_attmpt<-factor(v2_suic_note_attmpt,ordered=T)
descT(v2_suic_note_attmpt)
##                -999 1   3   4   <NA>     
## [1,] No. cases 1006 8   1   3   525  1543
## [2,] Percent   65.2 0.5 0.1 0.2 34   100

Create dataset

v2_suic<-data.frame(v2_suic_ide_snc_lst_vst,v2_scid_suic_ide,v2_scid_suic_thght_mth,v2_scid_suic_note_thgts,
                    v2_suic_attmpt_snc_lst_vst,v2_no_suic_attmpt,v2_prep_suic_attp_ord,
                    v2_suic_note_attmpt)

Visit 2: Medication

PsyCourse 3.1 contains now medication data. The code below creates the following variables for each person:

Number of antidepressants prescribed (continuous [number], v2_Antidepressants) Number of antipsychotics prescribed (continuous [number], v2_Antipsychotics) Number of mood stabilizers prescribed (continuous [number], v2_Mood_Stabilizers) Number of tranquilizers prescribed (continuous [number], v2_Tranquilizers) Number of other psychiatric medications (continuous [number], v2_Other_psychiatric)

Clinical participants

#get the following variables from v2_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v2_clin_medication_variables_1<-as.data.frame(v2_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v2_clin))])
dim(v2_clin_medication_variables_1) #[1] 1223   61
## [1] 1223   61
#recode the variables that are coded as characters/logicals in the "v2_clin_medication_variables_1" as factors
v2_clin_medication_variables_1$v2_medikabehand3_depot_medi_200170_3<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_depot_medi_200170_3)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_9<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_9)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_9)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_10<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_medi_199584_10)

v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v2_clin_medication_variables_1$v2_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v2_clin_medications_duplicated_1<-as.data.frame(t(apply(v2_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v2_clin_medications_duplicated_1) #1223   30
## [1] 1223   30
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_clin, not as character
v2_clin_medication_variables_1[,!c(TRUE, FALSE)][v2_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v2_clin_medication_variables_1) #1223   61
## [1] 1223   61
#bind columns id and medication names, but not categories together 
v2_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v2_clin_medication_variables_1[,1], v2_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v2_clin_medication_name_1) #1223   31
## [1] 1223   31
#get the medication categories from the "_medication_variables_1" dataframe
v2_clin_medication_categories_1<-as.data.frame(v2_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v2_clin_medication_categories_1) #1223   31
## [1] 1223   31
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_clin, not as character
#Important: v2_clin_medication_name_1=="NA" replaced with is.na(v2_clin_medication_name_1)
v2_clin_medication_categories_1[is.na(v2_clin_medication_name_1)] <- NA
#write.csv(v2_clin_medication_categories_1, file="v2_clin_medication_group_1.csv") 

#Make a count table of medications
v2_clin_med_table<-data.frame("mnppsd"=v2_clin$mnppsd)
v2_clin_med_table$v2_Antidepressants<-rowSums(v2_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v2_clin_med_table$v2_Antipsychotics<-rowSums(v2_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v2_clin_med_table$v2_Mood_stabilizers<-rowSums(v2_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v2_clin_med_table$v2_Tranquilizers<-rowSums(v2_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v2_clin_med_table$v2_Other_psychiatric<-rowSums(v2_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v2_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v2_con_medication_variables_1<-as.data.frame(v2_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v2_con))])
dim(v2_con_medication_variables_1) #[1] 320 29 
## [1] 320  29
#recode the variables that are coded as characters/logicals in the "v2_con_medication_variables_1" as factors
v2_con_medication_variables_1$v2_medikabehand3_med_medi_200705_8<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_med_medi_200705_8)

v2_con_medication_variables_1$v2_medikabehand3_med_kategorie_200705_8<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_med_kategorie_200705_8)

v2_con_medication_variables_1$v2_medikabehand3_depot_medi_201224_2<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_depot_medi_201224_2)

v2_con_medication_variables_1$v2_medikabehand3_depot_kategorie_201224_2<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_depot_kategorie_201224_2)

v2_con_medication_variables_1$v2_medikabehand3_bedarf_medi_201187_4<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_bedarf_medi_201187_4)

v2_con_medication_variables_1$v2_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v2_con_medication_variables_1$v2_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v2_con_medications_duplicated_1<-as.data.frame(t(apply(v2_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v2_con_medications_duplicated_1) #320  14
## [1] 320  14
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_con, not as character
v2_con_medication_variables_1[,!c(TRUE, FALSE)][v2_con_medications_duplicated_1=="TRUE"] <- NA
dim(v2_con_medication_variables_1) #320   29
## [1] 320  29
#bind columns id and medication names, but not categories together 
v2_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v2_con_medication_variables_1[,1], v2_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v2_con_medication_name_1) #320  15
## [1] 320  15
#get the medication categories from the "_medication_variables_1" dataframe
v2_con_medication_categories_1<-as.data.frame(v2_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v2_con_medication_categories_1) #320  15
## [1] 320  15
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v2_con, not as character
#Important: v2_con_medication_name_1=="NA" replaced with is.na(v2_con_medication_name_1)
v2_con_medication_categories_1[is.na(v2_con_medication_name_1)] <- NA
#write.csv(v2_con_medication_categories_1, file="v2_con_medication_group_1.csv")

#Make a count table of medications
v2_con_med_table<-data.frame("mnppsd"=v2_con$mnppsd)
v2_con_med_table$v2_Antidepressants<-rowSums(v2_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v2_con_med_table$v2_Antipsychotics<-rowSums(v2_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v2_con_med_table$v2_Mood_stabilizers<-rowSums(v2_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v2_con_med_table$v2_Tranquilizers<-rowSums(v2_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v2_con_med_table$v2_Other_psychiatric<-rowSums(v2_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v2_clin and v2_con together by rows

v2_drugs<-rbind(v2_clin_med_table,v2_con_med_table)
dim(v2_drugs) #1543    6
## [1] 1543    6
#check if the id column of v2_drugs and v1_id match
table(droplevels(v2_drugs[,1])==v1_id)
## 
## TRUE 
## 1543

Adverse events under current medication (only in clinical participants) (dichotomous, v2_adv)

v2_clin_adv<-ifelse(v2_clin$v2_medikabehand_medi2_nebenwirk==1,"Y","N")
v2_con_adv<-rep("-999",dim(v2_con)[1])
v2_adv<-factor(c(v2_clin_adv,v2_con_adv))
descT(v2_adv)
##                -999 N    Y   <NA>     
## [1,] No. cases 320  217  355 651  1543
## [2,] Percent   20.7 14.1 23  42.2 100

Psychiatric medication change during the past six months (only in clinical participants) (dichotomous, v2_medchange)

v2_clin_medchange<-rep(NA,dim(v2_clin)[1])
v2_clin_medchange<-ifelse(v2_clin$v2_medikabehand_medi3_mediaenderung==1,"Y","N")
v2_con_medchange<-rep("-999",dim(v2_con)[1])

v2_medchange<-as.factor(c(v2_clin_medchange,v2_con_medchange))
descT(v2_medchange)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  191  382  650  1543
## [2,] Percent   20.7 12.4 24.8 42.1 100

Lithium (only in clinical participants)

Please see the section in Visit 1 for explanation.

“Did you ever take lithium?” (dichotomous, v2_lith)

v2_clin_lith<-rep(NA,dim(v2_clin)[1])
v2_clin_lith<-ifelse(v2_clin$v2_medikabehand_med_zusatz_lithium==1,"Y","N")
v2_con_lith<-rep("-999",dim(v2_con)[1])

v2_lith<-as.factor(c(v2_clin_lith,v2_con_lith))
v2_lith<-as.factor(v2_lith)

descT(v2_lith)
##                -999 N    Y   <NA>     
## [1,] No. cases 320  215  133 875  1543
## [2,] Percent   20.7 13.9 8.6 56.7 100

“If yes, for how long?” (only in clinical participants) (dichotomous, v2_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.

v2_clin_lith_prd<-rep(NA,dim(v2_clin)[1])
v2_con_lith_prd<-rep(-999,dim(v2_con)[1])

v2_clin_lith_prd<-ifelse(v2_clin_lith=="N", -999, ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v2_clin$v2_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v2_lith_prd<-factor(c(v2_clin_lith_prd,v2_con_lith_prd))
descT(v2_lith_prd)
##                -999 1   2   3  <NA>     
## [1,] No. cases 535  53  18  62 875  1543
## [2,] Percent   34.7 3.4 1.2 4  56.7 100

Create dataset

v2_med<-data.frame(v2_drugs[,2:6],v2_adv,v2_medchange,v2_lith,v2_lith_prd)

Visit 2: Substance abuse

Tobacco

For more explanation, see Visit 1

“Did you start or stop smoking during the past six months?” (categorical [NS,NN,YSP,YST], v2_smk_strt_stp)

This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.

v2_clin_smk_strt_stp<-rep(NA,dim(v2_clin)[1])
v2_clin_smk_strt_stp<-ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==1,"NS",
                        ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==2,"NN",
                          ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==3,"YSP",
                            ifelse(v2_clin$v2_tabalk1_ta1_jemals_rauch==4,"YST",v2_clin_smk_strt_stp))))
       
#ATTENTION: answering alternative: e-cigarette only in controls
v2_con_smk_strt_stp<-rep(NA,dim(v2_clin)[1])
v2_con_smk_strt_stp<-ifelse(v2_con$v2_tabalk_folge_tabak1==1 | v2_con$v2_tabalk_folge_tabak1==2,"NS",
                        ifelse(v2_con$v2_tabalk_folge_tabak1==3,"NN",
                          ifelse(v2_con$v2_tabalk_folge_tabak1==4,"YSP",
                            ifelse(v2_con$v2_tabalk_folge_tabak1==5,"YST",v2_con_smk_strt_stp))))
                        
v2_smk_strt_stp<-c(v2_clin_smk_strt_stp,v2_con_smk_strt_stp)
descT(v2_smk_strt_stp)
##                NN   NS   YSP YST <NA>     
## [1,] No. cases 346  610  27  15  545  1543
## [2,] Percent   22.4 39.5 1.7 1   35.3 100

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v2_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.

v2_no_cig<-c(rep(NA,dim(v2_clin)[1]),rep(NA,dim(v2_con)[1]))

v2_no_cig<-ifelse((v2_smk_strt_stp=="NN" | v2_smk_strt_stp=="YSP"), -999, 
            ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") & 
                     c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==1,                                
                     c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*365,
            ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") & 
                     c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==2,                                
                     c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*52,
            ifelse((v2_smk_strt_stp=="NS" | v2_smk_strt_stp=="YST") & 
                     c(v2_clin$v2_tabalk1_ta3_zig_pro_zeit,v2_con$v2_tabalk_folge_tabak2_zeit)==3,                                
                     c(v2_clin$v2_tabalk1_ta3_anz_zig,v2_con$v2_tabalk_folge_tabak2_anz)*12,
                      v2_no_cig))))

summary(v2_no_cig[v2_no_cig>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0    3650    6935    6320    7300   23725     754

Alcohol

“How often did you consume alcoholic beverages during the past six months?” (ordinal [1,2,3,4,5,6,7], v2_alc_pst6_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v2_alc_pst6_mths<-c(v2_clin$v2_tabalk1_ta9_alkkonsum,v2_con$v2_tabalk_folge_alkohol4)
v2_alc_pst6_mths<-factor(v2_alc_pst6_mths, ordered=T)

descT(v2_alc_pst6_mths)
##                1    2    3   4    5   6   7   <NA>     
## [1,] No. cases 251  203  98  239  119 43  38  552  1543
## [2,] Percent   16.3 13.2 6.4 15.5 7.7 2.8 2.5 35.8 100

“On how many occasions during the past six months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?”" (ordinal [1,2,3,4,5,6,7,8,9], v2_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v2_alc_5orm<-ifelse(v2_alc_pst6_mths<4,-999,
                    ifelse(is.na(c(v2_clin$v2_tabalk1_ta10_alk_haeufigk_m1,v2_con$v2_tabalk_folge_alkohol5))==T,   
                            c(v2_clin$v2_tabalk1_ta11_alk_haeufigk_f1,v2_con$v2_tabalk_folge_alkohol6),
                            c(v2_clin$v2_tabalk1_ta10_alk_haeufigk_m1,v2_con$v2_tabalk_folge_alkohol5)))

v2_alc_5orm<-factor(v2_alc_5orm, ordered=T)

descT(v2_alc_5orm)
##                -999 1    2   3   4   5   6  7  8   9   <NA>     
## [1,] No. cases 552  193  80  57  20  29  31 15 3   7   556  1543
## [2,] Percent   35.8 12.5 5.2 3.7 1.3 1.9 2  1  0.2 0.5 36   100

Illicit drugs

On follow-up visits, participant were asked whether they had consumed illicit drugs since the last visit. If yes, the following information was collected:

  • the name of each illicit drug consumed since the last visit
  • the category of each illicit drug consumed since the last visit
  • the frequency of consumption since the last visit
  • whether the individual developed tolerance to the drug

Here, we include only the information whether, since the last study visit, the participant consumed any illicit drugs. More detailed information is available on request.

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v2_pst6_ill_drg)

v2_pst6_ill_drg<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_pst6_ill_drg<-ifelse(c(v2_clin$v2_drogen1_dg1_konsum,v2_con$v2_drogen_folge_drogenkonsum)==2, "Y", "N")

descT(v2_pst6_ill_drg)
##                N    Y  <NA>     
## [1,] No. cases 915  77 551  1543
## [2,] Percent   59.3 5  35.7 100

Create dataset

v2_subst<-data.frame(v2_smk_strt_stp,
                     v2_no_cig,
                     v2_alc_pst6_mths,
                     v2_alc_5orm,
                     v2_pst6_ill_drg)

Visit 2: Symptom rating scales (interviewer rates patient)

PANSS

For more information on the scale, please see Visit 1

P1 Delusions (ordinal [1,2,3,4,5,6,7], v2_panss_p1)

v2_panss_p1<-c(v2_clin$v2_panss_p_p1_wahnideen,v2_con$v2_panss_p_p1_wahnideen)
v2_panss_p1<-factor(v2_panss_p1, ordered=T)

descT(v2_panss_p1)
##                1    2   3  4   5  6   <NA>     
## [1,] No. cases 763  55  62 27  16 11  609  1543
## [2,] Percent   49.4 3.6 4  1.7 1  0.7 39.5 100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v2_panss_p2)

v2_panss_p2<-c(v2_clin$v2_panss_p_p2_form_denkst,v2_con$v2_panss_p_p2_form_denkst)
v2_panss_p2<-factor(v2_panss_p2, ordered=T)

descT(v2_panss_p2)
##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 683  93 103 44  8   3   609  1543
## [2,] Percent   44.3 6  6.7 2.9 0.5 0.2 39.5 100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v2_panss_p3)

v2_panss_p3<-c(v2_clin$v2_panss_p_p3_halluz,v2_con$v2_panss_p_p3_halluz)
v2_panss_p3<-factor(v2_panss_p3, ordered=T)

descT(v2_panss_p3)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 826  34  28  29  12  6   608  1543
## [2,] Percent   53.5 2.2 1.8 1.9 0.8 0.4 39.4 100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v2_panss_p4)

v2_panss_p4<-c(v2_clin$v2_panss_p_p4_erregung,v2_con$v2_panss_p_p4_erregung)
v2_panss_p4<-factor(v2_panss_p4, ordered=T)

descT(v2_panss_p4)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 718  78  109 22  4   3   609  1543
## [2,] Percent   46.5 5.1 7.1 1.4 0.3 0.2 39.5 100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v2_panss_p5)

v2_panss_p5<-c(v2_clin$v2_panss_p_p5_groessenideen,v2_con$v2_panss_p_p5_groessenideen)
v2_panss_p5<-factor(v2_panss_p5, ordered=T)

descT(v2_panss_p5)
##                1   2   3   4   5   6   <NA>     
## [1,] No. cases 864 29  28  6   4   1   611  1543
## [2,] Percent   56  1.9 1.8 0.4 0.3 0.1 39.6 100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v2_panss_p6)

v2_panss_p6<-c(v2_clin$v2_panss_p_p6_misstr_verfolg,v2_con$v2_panss_p_p6_misstr_verfolg)
v2_panss_p6<-factor(v2_panss_p6, ordered=T)

descT(v2_panss_p6)
##                1    2   3   4   5  6   7   <NA>     
## [1,] No. cases 761  60  73  20  15 4   1   609  1543
## [2,] Percent   49.3 3.9 4.7 1.3 1  0.3 0.1 39.5 100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v2_panss_p7)

v2_panss_p7<-c(v2_clin$v2_panss_p_p7_feindseligkeit,v2_con$v2_panss_p_p7_feindseligkeit)
v2_panss_p7<-factor(v2_panss_p7, ordered=T)

descT(v2_panss_p7)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 850  43  32  6   1   1   610  1543
## [2,] Percent   55.1 2.8 2.1 0.4 0.1 0.1 39.5 100

PANSS Positive sum score (continuous [7-49], v2_panss_sum_pos)

v2_panss_sum_pos<-as.numeric.factor(v2_panss_p1)+
                  as.numeric.factor(v2_panss_p2)+
                  as.numeric.factor(v2_panss_p3)+
                  as.numeric.factor(v2_panss_p4)+
                  as.numeric.factor(v2_panss_p5)+
                  as.numeric.factor(v2_panss_p6)+
                  as.numeric.factor(v2_panss_p7)

summary(v2_panss_sum_pos)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    7.00    9.27   10.00   32.00     614

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v2_panss_n1)

v2_panss_n1<-c(v2_clin$v2_panss_n_n1_affektverflachung,v2_con$v2_panss_n_n1_affektverflachung)
v2_panss_n1<-factor(v2_panss_n1, ordered=T)

descT(v2_panss_n1)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 553  108 128 88  45  8   2   611  1543
## [2,] Percent   35.8 7   8.3 5.7 2.9 0.5 0.1 39.6 100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v2_panss_n2)

v2_panss_n2<-c(v2_clin$v2_panss_n_n2_emot_rueckzug,v2_con$v2_panss_n_n2_emot_rueckzug)
v2_panss_n2<-factor(v2_panss_n2, ordered=T)

descT(v2_panss_n2)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 646  95  99  70  21  4   608  1543
## [2,] Percent   41.9 6.2 6.4 4.5 1.4 0.3 39.4 100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v2_panss_n3)

v2_panss_n3<-c(v2_clin$v2_panss_n_n3_mang_aff_rapp,v2_con$v2_panss_n_n3_mang_aff_rapp)
v2_panss_n3<-factor(v2_panss_n3, ordered=T)

descT(v2_panss_n3)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 696  87  101 37  9   4   609  1543
## [2,] Percent   45.1 5.6 6.5 2.4 0.6 0.3 39.5 100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v2_panss_n4)

v2_panss_n4<-c(v2_clin$v2_panss_n_n4_soz_pass_apath,v2_con$v2_panss_n_n4_soz_pass_apath)
v2_panss_n4<-factor(v2_panss_n4, ordered=T)

descT(v2_panss_n4)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 655  75  123 55  24  3   608  1543
## [2,] Percent   42.4 4.9 8   3.6 1.6 0.2 39.4 100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v2_panss_n5)

v2_panss_n5<-c(v2_clin$v2_panss_n_n5_abstr_denken,v2_con$v2_panss_n_n5_abstr_denken)
v2_panss_n5<-factor(v2_panss_n5, ordered=T)

descT(v2_panss_n5)
##                1    2   3   4   5  6   <NA>     
## [1,] No. cases 620  98  135 56  15 5   614  1543
## [2,] Percent   40.2 6.4 8.7 3.6 1  0.3 39.8 100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v2_panss_n6)

v2_panss_n6<-c(v2_clin$v2_panss_n_n6_spon_fl_sprache,v2_con$v2_panss_n_n6_spon_fl_sprache)
v2_panss_n6<-factor(v2_panss_n6, ordered=T)

descT(v2_panss_n6)
##                1    2   3   4   5  6   <NA>     
## [1,] No. cases 719  74  87  33  15 2   613  1543
## [2,] Percent   46.6 4.8 5.6 2.1 1  0.1 39.7 100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v2_panss_n7)

v2_panss_n7<-c(v2_clin$v2_panss_n_n7_stereotyp_ged,v2_con$v2_panss_n_n7_stereotyp_ged)
v2_panss_n7<-factor(v2_panss_n7, ordered=T)

descT(v2_panss_n7)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 777  68  63  19  4   612  1543
## [2,] Percent   50.4 4.4 4.1 1.2 0.3 39.7 100

PANSS Negative sum score (continuous [7-49], v2_panss_sum_neg)

v2_panss_sum_neg<-as.numeric.factor(v2_panss_n1)+
                  as.numeric.factor(v2_panss_n2)+
                  as.numeric.factor(v2_panss_n3)+
                  as.numeric.factor(v2_panss_n4)+
                  as.numeric.factor(v2_panss_n5)+
                  as.numeric.factor(v2_panss_n6)+
                  as.numeric.factor(v2_panss_n7)

summary(v2_panss_sum_neg)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    9.00   11.08   13.00   39.00     620

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v2_panss_g1)

v2_panss_g1<-c(v2_clin$v2_panss_g_g1_sorge_gesundh,v2_con$v2_panss_g_g1_sorge_gesundh)
v2_panss_g1<-factor(v2_panss_g1, ordered=T)

descT(v2_panss_g1)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 656  120 97  49  10  2   1   608  1543
## [2,] Percent   42.5 7.8 6.3 3.2 0.6 0.1 0.1 39.4 100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v2_panss_g2)

v2_panss_g2<-c(v2_clin$v2_panss_g_g2_angst,v2_con$v2_panss_g_g2_angst)
v2_panss_g2<-factor(v2_panss_g2, ordered=T)

descT(v2_panss_g2)
##                1    2   3    4  5   6   7   <NA>     
## [1,] No. cases 598  81  183  47 24  1   1   608  1543
## [2,] Percent   38.8 5.2 11.9 3  1.6 0.1 0.1 39.4 100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v2_panss_g3)

v2_panss_g3<-c(v2_clin$v2_panss_g_g3_schuldgefuehle,v2_con$v2_panss_g_g3_schuldgefuehle)
v2_panss_g3<-factor(v2_panss_g3, ordered=T)

descT(v2_panss_g3)
##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 704  76  93 41  13  4   612  1543
## [2,] Percent   45.6 4.9 6  2.7 0.8 0.3 39.7 100

G4 Tension (ordinal [1,2,3,4,5,6,7], v2_panss_g4)

v2_panss_g4<-c(v2_clin$v2_panss_g_g4_anspannung,v2_con$v2_panss_g_g4_anspannung)
v2_panss_g4<-factor(v2_panss_g4, ordered=T)

descT(v2_panss_g4)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 609  108 150 51  11  4   1   609  1543
## [2,] Percent   39.5 7   9.7 3.3 0.7 0.3 0.1 39.5 100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v2_panss_g5)

v2_panss_g5<-c(v2_clin$v2_panss_g_g5_manier_koerperh,v2_con$v2_panss_g_g5_manier_koerperh)
v2_panss_g5<-factor(v2_panss_g5, ordered=T)

descT(v2_panss_g5)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 851  36  30  9   6   1   610  1543
## [2,] Percent   55.2 2.3 1.9 0.6 0.4 0.1 39.5 100

G6 Depression (ordinal [1,2,3,4,5,6,7], v2_panss_g6)

v2_panss_g6<-c(v2_clin$v2_panss_g_g6_depression,v2_con$v2_panss_g_g6_depression)
v2_panss_g6<-factor(v2_panss_g6, ordered=T)

descT(v2_panss_g6)
##                1    2   3    4   5   6  <NA>     
## [1,] No. cases 530  80  168  87  54  15 609  1543
## [2,] Percent   34.3 5.2 10.9 5.6 3.5 1  39.5 100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v2_panss_g7)

v2_panss_g7<-c(v2_clin$v2_panss_g_g7_mot_verlangs,v2_con$v2_panss_g_g7_mot_verlangs)
v2_panss_g7<-factor(v2_panss_g7, ordered=T)

descT(v2_panss_g7)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 646  85  136 57  7   2   610  1543
## [2,] Percent   41.9 5.5 8.8 3.7 0.5 0.1 39.5 100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v2_panss_g8)

v2_panss_g8<-c(v2_clin$v2_panss_g_g8_unkoop_verh,v2_con$v2_panss_g_g8_unkoop_verh)
v2_panss_g8<-factor(v2_panss_g8, ordered=T)

descT(v2_panss_g8)
##                1   2   3   4   5   6   <NA>     
## [1,] No. cases 864 27  36  4   1   1   610  1543
## [2,] Percent   56  1.7 2.3 0.3 0.1 0.1 39.5 100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v2_panss_g9)

v2_panss_g9<-c(v2_clin$v2_panss_g_g9_ungew_denkinh,v2_con$v2_panss_g_g9_ungew_denkinh)
v2_panss_g9<-factor(v2_panss_g9, ordered=T)

descT(v2_panss_g9)
##                1   2   3   4   5   6   <NA>     
## [1,] No. cases 772 50  75  21  12  4   609  1543
## [2,] Percent   50  3.2 4.9 1.4 0.8 0.3 39.5 100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v2_panss_g10)

v2_panss_g10<-c(v2_clin$v2_panss_g_g10_desorient,v2_con$v2_panss_g_g10_desorient)
v2_panss_g10<-factor(v2_panss_g10, ordered=T)

descT(v2_panss_g10)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 874  39  18  2   2   608  1543
## [2,] Percent   56.6 2.5 1.2 0.1 0.1 39.4 100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v2_panss_g11)

v2_panss_g11<-c(v2_clin$v2_panss_g_g11_mang_aufmerks,v2_con$v2_panss_g_g11_mang_aufmerks)
v2_panss_g11<-factor(v2_panss_g11, ordered=T)

descT(v2_panss_g11)
##                1    2   3    4   5   6   <NA>     
## [1,] No. cases 578  96  189  56  8   2   614  1543
## [2,] Percent   37.5 6.2 12.2 3.6 0.5 0.1 39.8 100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v2_panss_g12)

v2_panss_g12<-c(v2_clin$v2_panss_g_g12_mang_urt_einsi,v2_con$v2_panss_g_g12_mang_urt_einsi)

v2_panss_g12<-factor(v2_panss_g12, ordered=T)
descT(v2_panss_g12)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 812  51  48  8   11  2   611  1543
## [2,] Percent   52.6 3.3 3.1 0.5 0.7 0.1 39.6 100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v2_panss_g13)

v2_panss_g13<-c(v2_clin$v2_panss_g_g13_willensschwae,v2_con$v2_panss_g_g13_willensschwae)
v2_panss_g13<-factor(v2_panss_g13, ordered=T)

descT(v2_panss_g13)
##                1    2   3   4  5   6   <NA>     
## [1,] No. cases 820  40  56  15 1   1   610  1543
## [2,] Percent   53.1 2.6 3.6 1  0.1 0.1 39.5 100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v2_panss_g14)

v2_panss_g14<-c(v2_clin$v2_panss_g_g14_mang_impulsk,v2_con$v2_panss_g_g14_mang_impulsk)
v2_panss_g14<-factor(v2_panss_g14, ordered=T)

descT(v2_panss_g14)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 819  33  69  12  1   609  1543
## [2,] Percent   53.1 2.1 4.5 0.8 0.1 39.5 100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v2_panss_g15)

v2_panss_g15<-c(v2_clin$v2_panss_g_g15_selbstbezog,v2_con$v2_panss_g_g15_selbstbezog)
v2_panss_g15<-factor(v2_panss_g15, ordered=T)

descT(v2_panss_g15)
##                1    2   3  4  5   <NA>     
## [1,] No. cases 830  54  31 16 2   610  1543
## [2,] Percent   53.8 3.5 2  1  0.1 39.5 100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v2_panss_g16)

v2_panss_g16<-c(v2_clin$v2_panss_g_g16_aktsoz_vermeid,v2_con$v2_panss_g_g16_aktsoz_vermeid)
v2_panss_g16<-factor(v2_panss_g16, ordered=T)

descT(v2_panss_g16)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 717  69  98  29  18  2   610  1543
## [2,] Percent   46.5 4.5 6.4 1.9 1.2 0.1 39.5 100

PANSS General Psychopathology sum score (continuous [16-112], v2_panss_sum_gen)

v2_panss_sum_gen<-as.numeric.factor(v2_panss_g1)+
                  as.numeric.factor(v2_panss_g2)+
                  as.numeric.factor(v2_panss_g3)+
                  as.numeric.factor(v2_panss_g4)+
                  as.numeric.factor(v2_panss_g5)+
                  as.numeric.factor(v2_panss_g6)+
                  as.numeric.factor(v2_panss_g7)+
                  as.numeric.factor(v2_panss_g8)+
                  as.numeric.factor(v2_panss_g9)+
                  as.numeric.factor(v2_panss_g10)+
                  as.numeric.factor(v2_panss_g11)+
                  as.numeric.factor(v2_panss_g12)+
                  as.numeric.factor(v2_panss_g13)+
                  as.numeric.factor(v2_panss_g14)+
                  as.numeric.factor(v2_panss_g15)+
                  as.numeric.factor(v2_panss_g16)

summary(v2_panss_sum_gen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   17.00   20.00   22.88   27.00   68.00     631

Create PANSS Total score (continuous [30-210], v2_panss_sum_tot)

v2_panss_sum_tot<-v2_panss_sum_pos+v2_panss_sum_neg+v2_panss_sum_gen
summary(v2_panss_sum_tot)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   31.00   38.00   43.25   50.00  137.00     645

Create dataset

v2_symp_panss<-data.frame(v2_panss_p1,v2_panss_p2,v2_panss_p3,v2_panss_p4,v2_panss_p5,v2_panss_p6,v2_panss_p7,
                          v2_panss_n1,v2_panss_n2,v2_panss_n3,v2_panss_n4,v2_panss_n5,v2_panss_n6,v2_panss_n7,
                          v2_panss_g1,v2_panss_g2,v2_panss_g3,v2_panss_g4,v2_panss_g5,v2_panss_g6,v2_panss_g7,
                          v2_panss_g8,v2_panss_g9,v2_panss_g10,v2_panss_g11,v2_panss_g12,v2_panss_g13,v2_panss_g14,
                          v2_panss_g15,v2_panss_g16,v2_panss_sum_pos,v2_panss_sum_neg,v2_panss_sum_gen,
                          v2_panss_sum_tot)

IDS-C30

For more information on the scale, please see Visit 1

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v2_idsc_itm1)

v2_idsc_itm1<-c(v2_clin$v2_ids_c_s1_ids1_einschlafschw,v2_con$v2_ids_c_s1_ids1_einschlafschw)
v2_idsc_itm1<-factor(v2_idsc_itm1, ordered=T)

descT(v2_idsc_itm1)
##                0    1   2   3   <NA>     
## [1,] No. cases 661  123 81  65  613  1543
## [2,] Percent   42.8 8   5.2 4.2 39.7 100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v2_idsc_itm2)

v2_idsc_itm2<-c(v2_clin$v2_ids_c_s1_ids2_naechtl_aufw,v2_con$v2_ids_c_s1_ids2_naechtl_aufw)
v2_idsc_itm2<-factor(v2_idsc_itm2, ordered=T)

descT(v2_idsc_itm2)
##                0    1   2   3   <NA>     
## [1,] No. cases 570  139 145 76  613  1543
## [2,] Percent   36.9 9   9.4 4.9 39.7 100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v2_idsc_itm3)

v2_idsc_itm3<-c(v2_clin$v2_ids_c_s1_ids3_frueh_aufw,v2_con$v2_ids_c_s1_ids3_frueh_aufw)
v2_idsc_itm3<-factor(v2_idsc_itm3, ordered=T)

descT(v2_idsc_itm3)
##                0    1  2   3   <NA>     
## [1,] No. cases 790  61 41  37  614  1543
## [2,] Percent   51.2 4  2.7 2.4 39.8 100

Item 4 Hypersomnia (ordinal [0,1,2,3], v2_idsc_itm4)

v2_idsc_itm4<-c(v2_clin$v2_ids_c_s1_ids4_hypersomnie,v2_con$v2_ids_c_s1_ids4_hypersomnie)
v2_idsc_itm4<-factor(v2_idsc_itm4, ordered=T)

descT(v2_idsc_itm4)
##                0    1    2   3   <NA>     
## [1,] No. cases 580  239  90  20  614  1543
## [2,] Percent   37.6 15.5 5.8 1.3 39.8 100

Item 5 Mood (sad) (ordinal [0,1,2,3], v2_idsc_itm5)

v2_idsc_itm5<-c(v2_clin$v2_ids_c_s1_ids5_stimmung_trgk,v2_con$v2_ids_c_s1_ids5_stimmung_trgk)
v2_idsc_itm5<-factor(v2_idsc_itm5, ordered=T)

descT(v2_idsc_itm5)
##                0    1    2   3  <NA>     
## [1,] No. cases 575  211  96  47 614  1543
## [2,] Percent   37.3 13.7 6.2 3  39.8 100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v2_idsc_itm6)

v2_idsc_itm6<-c(v2_clin$v2_ids_c_s1_ids6_stimmung_grzt,v2_con$v2_ids_c_s1_ids6_stimmung_grzt)
v2_idsc_itm6<-factor(v2_idsc_itm6, ordered=T)

descT(v2_idsc_itm6)
##                0    1    2  3   <NA>     
## [1,] No. cases 652  204  62 12  613  1543
## [2,] Percent   42.3 13.2 4  0.8 39.7 100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v2_idsc_itm7)

v2_idsc_itm7<-c(v2_clin$v2_ids_c_s1_ids7_stimmung_agst,v2_con$v2_ids_c_s1_ids7_stimmung_agst)
v2_idsc_itm7<-factor(v2_idsc_itm7, ordered=T)

descT(v2_idsc_itm7)
##                0    1   2   3  <NA>     
## [1,] No. cases 612  200 84  31 616  1543
## [2,] Percent   39.7 13  5.4 2  39.9 100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v2_idsc_itm8)

v2_idsc_itm8<-c(v2_clin$v2_ids_c_s1_ids8_reakt_stimmung,v2_con$v2_ids_c_s1_ids8_reakt_stimmung)
v2_idsc_itm8<-factor(v2_idsc_itm8, ordered=T)

descT(v2_idsc_itm8)
##                0    1   2   3   <NA>     
## [1,] No. cases 745  108 48  24  618  1543
## [2,] Percent   48.3 7   3.1 1.6 40.1 100

Item 9 Mood Variation (ordinal [0,1,2,3], v2_idsc_itm9)

v2_idsc_itm9<-c(v2_clin$v2_ids_c_s1_ids9_stimmungsschw,v2_con$v2_ids_c_s1_ids9_stimmungsschw)
v2_idsc_itm9<-factor(v2_idsc_itm9, ordered=T)

descT(v2_idsc_itm9)
##                0    1   2   3   <NA>     
## [1,] No. cases 700  83  41  98  621  1543
## [2,] Percent   45.4 5.4 2.7 6.4 40.2 100

Item 9A (categorical [M, A, N], v2_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v2_idsc_itm9a_pre<-c(v2_clin$v2_ids_c_s1_ids9a_stimmungsschw,v2_con$v2_ids_c_s1_ids9a_stimmungsschw)

v2_idsc_itm9a<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==1, "M", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==2, "A", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9a_pre==3, "N", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9a))
v2_idsc_itm9a<-factor(v2_idsc_itm9a, ordered=F)

descT(v2_idsc_itm9a)
##                -999 A  M   N   <NA>     
## [1,] No. cases 700  16 117 36  674  1543
## [2,] Percent   45.4 1  7.6 2.3 43.7 100

Item 9B (dichotomous, v2_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v2_idsc_itm9b_pre<-c(v2_clin$v2_ids_c_s1_ids9b_stimmungsschw,v2_con$v2_ids_c_s1_ids9b_stimmungsschw)

v2_idsc_itm9b<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm9b<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9b_pre==0, "N", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9b))
v2_idsc_itm9b<-ifelse(v2_idsc_itm9!=0 & v2_idsc_itm9b_pre==1, "Y", ifelse(v2_idsc_itm9==0, -999, v2_idsc_itm9b))
v2_idsc_itm9b<-factor(v2_idsc_itm9b, ordered=F)

descT(v2_idsc_itm9b)
##                -999 N   Y   <NA>     
## [1,] No. cases 700  71  57  715  1543
## [2,] Percent   45.4 4.6 3.7 46.3 100

Item 10 Quality of mood (ordinal [0,1,2,3], v2_idsc_itm10)

v2_idsc_itm10<-c(v2_clin$v2_ids_c_s1_ids10_quali_stimmung,v2_con$v2_ids_c_s1_ids10_quali_stimmung)
v2_idsc_itm10<-factor(v2_idsc_itm10, ordered=T)

descT(v2_idsc_itm10)
##                0    1   2   3   <NA>     
## [1,] No. cases 782  57  34  51  619  1543
## [2,] Percent   50.7 3.7 2.2 3.3 40.1 100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v2_idsc_itm11)

v2_idsc_app_verm<-c(v2_clin$v2_ids_c_s2_ids11_appetit_verm,v2_con$v2_ids_c_s2_ids11_appetit_verm)
v2_idsc_app_gest<-c(v2_clin$v2_ids_c_s2_ids12_appetit_steig,v2_con$v2_ids_c_s2_ids12_appetit_steig)

v2_idsc_itm11<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm11<-ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==T,          
                         v2_idsc_app_verm, 
                      ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &    
                     (v2_idsc_app_verm>v2_idsc_app_gest), v2_idsc_app_verm,                                            ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &                                                         (v2_idsc_app_gest>=v2_idsc_app_verm),-999,v2_idsc_itm11)))))

#Important: do not code as factor, see after calculation of sum score!
descT(v2_idsc_itm11)
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 275  552  74  25  3   614  1543
## [2,] Percent   17.8 35.8 4.8 1.6 0.2 39.8 100

Item 12 (ordinal [0,1,2,3], v2_idsc_itm12)

v2_idsc_itm12<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm12<-ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v2_idsc_app_verm)==T & is.na(v2_idsc_app_gest)==F,    
                         v2_idsc_app_gest,                
                  ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==T,          
                         -999, 
                      ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &    
                     (v2_idsc_app_verm>v2_idsc_app_gest), -999,                                            
                     ifelse(is.na(v2_idsc_app_verm)==F & is.na(v2_idsc_app_gest)==F &                                                         (v2_idsc_app_gest>=v2_idsc_app_verm),
                            v2_idsc_app_gest,v2_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v2_idsc_itm12)
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 654  110 100 42  23  614  1543
## [2,] Percent   42.4 7.1 6.5 2.7 1.5 39.8 100

Item 13 (ordinal [0,1,2,3], v2_idsc_itm13)

v2_idsc_gew_abn<-c(v2_clin$v2_ids_c_s2_ids13_gewichtsabn,v2_con$v2_ids_c_s2_ids13_gewichtsabn)
v2_idsc_gew_zun<-c(v2_clin$v2_ids_c_s2_ids14_gewichtszun,v2_con$v2_ids_c_s2_ids14_gewichtszun)

v2_idsc_itm13<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm13<-ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==T,          
                         v2_idsc_gew_abn, 
                      ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &    
                     (v2_idsc_gew_abn>v2_idsc_gew_zun), v2_idsc_gew_abn,                                            ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F & (v2_idsc_gew_zun >= v2_idsc_gew_abn),-999,v2_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v2_idsc_itm13)
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 299  533  45  37  17  612  1543
## [2,] Percent   19.4 34.5 2.9 2.4 1.1 39.7 100

Item 14 (ordinal [0,1,2,3], v2_idsc_itm14)

v2_idsc_itm14<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_idsc_itm14<-ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v2_idsc_gew_abn)==T & is.na(v2_idsc_gew_zun)==F,    
                         v2_idsc_gew_zun,                
                  ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==T,          
                         -999, 
                      ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &    
                     (v2_idsc_gew_abn>v2_idsc_gew_zun), -999,                                            
                     ifelse(is.na(v2_idsc_gew_abn)==F & is.na(v2_idsc_gew_zun)==F &                                                         (v2_idsc_gew_zun>=v2_idsc_gew_abn),
                            v2_idsc_gew_zun,v2_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v2_idsc_itm14)
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 632  159  66  50  24  612  1543
## [2,] Percent   41   10.3 4.3 3.2 1.6 39.7 100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v2_idsc_itm15)

v2_idsc_itm15<-c(v2_clin$v2_ids_c_s2_ids15_konz_entscheid,v2_con$v2_ids_c_s2_ids15_konz_entscheid)
v2_idsc_itm15<-factor(v2_idsc_itm15, ordered=T)

descT(v2_idsc_itm15)
##                0    1    2   3   <NA>     
## [1,] No. cases 531  241  136 20  615  1543
## [2,] Percent   34.4 15.6 8.8 1.3 39.9 100

Item 16 Outlook (self) (ordinal [0,1,2,3], v2_idsc_itm16)

v2_idsc_itm16<-c(v2_clin$v2_ids_c_s2_ids16_selbstbild,v2_con$v2_ids_c_s2_ids16_selbstbild)
v2_idsc_itm16<-factor(v2_idsc_itm16, ordered=T)

descT(v2_idsc_itm16)
##                0    1   2   3   <NA>     
## [1,] No. cases 676  155 49  49  614  1543
## [2,] Percent   43.8 10  3.2 3.2 39.8 100

Item 17 Outlook (future) (ordinal [0,1,2,3], v2_idsc_itm17)

v2_idsc_itm17<-c(v2_clin$v2_ids_c_s2_ids17_zukunftssicht,v2_con$v2_ids_c_s2_ids17_zukunftssicht)
v2_idsc_itm17<-factor(v2_idsc_itm17, ordered=T)

descT(v2_idsc_itm17)
##                0   1    2   3   <NA>     
## [1,] No. cases 602 230  86  10  615  1543
## [2,] Percent   39  14.9 5.6 0.6 39.9 100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v2_idsc_itm18)

v2_idsc_itm18<-c(v2_clin$v2_ids_c_s2_ids18_selbstmordged,v2_con$v2_ids_c_s2_ids18_selbstmordged)
v2_idsc_itm18<-factor(v2_idsc_itm18, ordered=T)

descT(v2_idsc_itm18)
##                0    1  2   3   <NA>     
## [1,] No. cases 830  46 50  3   614  1543
## [2,] Percent   53.8 3  3.2 0.2 39.8 100

Item 19 Involvement (ordinal [0,1,2,3], v2_idsc_itm19)

v2_idsc_itm19<-c(v2_clin$v2_ids_c_s2_ids19_interess_aktiv,v2_con$v2_ids_c_s2_ids19_interess_aktiv)
v2_idsc_itm19<-factor(v2_idsc_itm19, ordered=T)

descT(v2_idsc_itm19)
##                0    1   2   3  <NA>     
## [1,] No. cases 749  138 29  16 611  1543
## [2,] Percent   48.5 8.9 1.9 1  39.6 100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v2_idsc_itm20)

v2_idsc_itm20<-c(v2_clin$v2_ids_c_s2_ids20_energ_ermued,v2_con$v2_ids_c_s2_ids20_energ_ermued)
v2_idsc_itm20<-factor(v2_idsc_itm20, ordered=T)

descT(v2_idsc_itm20)
##                0   1    2   3   <NA>     
## [1,] No. cases 571 234  112 14  612  1543
## [2,] Percent   37  15.2 7.3 0.9 39.7 100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v2_idsc_itm21)

v2_idsc_itm21<-c(v2_clin$v2_ids_c_s3_ids21_vergn_genuss,v2_con$v2_ids_c_s3_ids21_vergn_genuss)
v2_idsc_itm21<-factor(v2_idsc_itm21, ordered=T)

descT(v2_idsc_itm21)
##                0    1   2   3   <NA>     
## [1,] No. cases 760  119 44  7   613  1543
## [2,] Percent   49.3 7.7 2.9 0.5 39.7 100

Item 22 Sexual interest (ordinal [0,1,2,3], v2_idsc_itm22)

v2_idsc_itm22<-c(v2_clin$v2_ids_c_s3_ids22_sex_interesse,v2_con$v2_ids_c_s3_ids22_sex_interesse)
v2_idsc_itm22<-factor(v2_idsc_itm22, ordered=T)

descT(v2_idsc_itm22)
##                0    1   2   3  <NA>     
## [1,] No. cases 667  78  102 77 619  1543
## [2,] Percent   43.2 5.1 6.6 5  40.1 100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v2_idsc_itm23)

v2_idsc_itm23<-c(v2_clin$v2_ids_c_s3_ids23_psymo_hemm,v2_con$v2_ids_c_s3_ids23_psymo_hemm)
v2_idsc_itm23<-factor(v2_idsc_itm23, ordered=T)

descT(v2_idsc_itm23)
##                0   1    2   3   <NA>     
## [1,] No. cases 725 161  43  1   613  1543
## [2,] Percent   47  10.4 2.8 0.1 39.7 100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v2_idsc_itm24)

v2_idsc_itm24<-c(v2_clin$v2_ids_c_s3_ids24_psymo_agitht,v2_con$v2_ids_c_s3_ids24_psymo_agitht)
v2_idsc_itm24<-factor(v2_idsc_itm24, ordered=T)

descT(v2_idsc_itm24)
##                0    1   2   3   <NA>     
## [1,] No. cases 764  110 50  5   614  1543
## [2,] Percent   49.5 7.1 3.2 0.3 39.8 100

Item 25 Somatic complaints (ordinal [0,1,2,3], v2_idsc_itm25)

v2_idsc_itm25<-c(v2_clin$v2_ids_c_s3_ids25_som_beschw,v2_con$v2_ids_c_s3_ids25_som_beschw)
v2_idsc_itm25<-factor(v2_idsc_itm25, ordered=T)

descT(v2_idsc_itm25)
##                0    1    2  3   <NA>     
## [1,] No. cases 621  242  46 21  613  1543
## [2,] Percent   40.2 15.7 3  1.4 39.7 100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v2_idsc_itm26)

v2_idsc_itm26<-c(v2_clin$v2_ids_c_s3_ids26_veg_erreg,v2_con$v2_ids_c_s3_ids26_veg_erreg)
v2_idsc_itm26<-factor(v2_idsc_itm26, ordered=T)

descT(v2_idsc_itm26)
##                0    1    2   3   <NA>     
## [1,] No. cases 659  219  45  7   613  1543
## [2,] Percent   42.7 14.2 2.9 0.5 39.7 100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v2_idsc_itm27)

v2_idsc_itm27<-c(v2_clin$v2_ids_c_s3_ids27_panik_phob,v2_con$v2_ids_c_s3_ids27_panik_phob)
v2_idsc_itm27<-factor(v2_idsc_itm27, ordered=T)

descT(v2_idsc_itm27)
##                0    1   2   3   <NA>     
## [1,] No. cases 816  73  32  9   613  1543
## [2,] Percent   52.9 4.7 2.1 0.6 39.7 100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v2_idsc_itm28)

v2_idsc_itm28<-c(v2_clin$v2_ids_c_s3_ids28_verdauung,v2_con$v2_ids_c_s3_ids28_verdauung)
v2_idsc_itm28<-factor(v2_idsc_itm28, ordered=T)

descT(v2_idsc_itm28)
##                0    1   2   3  <NA>     
## [1,] No. cases 763  101 49  15 615  1543
## [2,] Percent   49.4 6.5 3.2 1  39.9 100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v2_idsc_itm29)

v2_idsc_itm29<-c(v2_clin$v2_ids_c_s3_ids29_pers_bezieh,v2_con$v2_ids_c_s3_ids29_pers_bezieh)
v2_idsc_itm29<-factor(v2_idsc_itm29, ordered=T)

descT(v2_idsc_itm29)
##                0    1   2  3  <NA>     
## [1,] No. cases 758  111 46 16 612  1543
## [2,] Percent   49.1 7.2 3  1  39.7 100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v2_idsc_itm30)

v2_idsc_itm30<-c(v2_clin$v2_ids_c_s3_ids30_schwgf_k_energ,v2_con$v2_ids_c_s3_ids30_schwgf_k_energ)
v2_idsc_itm30<-factor(v2_idsc_itm30, ordered=T)

descT(v2_idsc_itm30)
##                0    1   2   3  <NA>     
## [1,] No. cases 761  119 33  16 614  1543
## [2,] Percent   49.3 7.7 2.1 1  39.8 100

Create IDS-C30 total score (continuous [0-84], v2_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v2_idsc_sum<-as.numeric.factor(v2_idsc_itm1)+
             as.numeric.factor(v2_idsc_itm2)+
             as.numeric.factor(v2_idsc_itm3)+
             as.numeric.factor(v2_idsc_itm4)+
             as.numeric.factor(v2_idsc_itm5)+
             as.numeric.factor(v2_idsc_itm6)+
             as.numeric.factor(v2_idsc_itm7)+
             as.numeric.factor(v2_idsc_itm8)+
             as.numeric.factor(v2_idsc_itm9)+
             as.numeric.factor(v2_idsc_itm10)+
  
 ifelse(is.na(v2_idsc_itm11)==T & is.na(v2_idsc_itm12)==T, NA, 
        ifelse((v2_idsc_itm11==-999 & v2_idsc_itm12!=-999), v2_idsc_itm12,                
              ifelse((v2_idsc_itm11!=-999 & v2_idsc_itm12==-999),v2_idsc_itm11, NA)))+
  
   ifelse(is.na(v2_idsc_itm13)==T & is.na(v2_idsc_itm14)==T, NA, 
        ifelse((v2_idsc_itm13==-999 & v2_idsc_itm14!=-999), v2_idsc_itm14,                
              ifelse((v2_idsc_itm13!=-999 & v2_idsc_itm14==-999),v2_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v2_idsc_itm15)+
             as.numeric.factor(v2_idsc_itm16)+
             as.numeric.factor(v2_idsc_itm17)+
             as.numeric.factor(v2_idsc_itm18)+
             as.numeric.factor(v2_idsc_itm19)+
             as.numeric.factor(v2_idsc_itm20)+
             as.numeric.factor(v2_idsc_itm21)+
             as.numeric.factor(v2_idsc_itm22)+
             as.numeric.factor(v2_idsc_itm23)+
             as.numeric.factor(v2_idsc_itm24)+
             as.numeric.factor(v2_idsc_itm25)+
             as.numeric.factor(v2_idsc_itm26)+
             as.numeric.factor(v2_idsc_itm27)+
             as.numeric.factor(v2_idsc_itm28)+
             as.numeric.factor(v2_idsc_itm29)+
             as.numeric.factor(v2_idsc_itm30)

summary(v2_idsc_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0     3.0     8.0    10.9    16.0    55.0     693

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v2_idsc_itm11<-factor(v2_idsc_itm11,ordered=T)
v2_idsc_itm12<-factor(v2_idsc_itm12,ordered=T)
v2_idsc_itm13<-factor(v2_idsc_itm13,ordered=T)
v2_idsc_itm14<-factor(v2_idsc_itm14,ordered=T)

Create dataset

v2_symp_ids_c<-data.frame(v2_idsc_itm1,v2_idsc_itm2,v2_idsc_itm3,v2_idsc_itm4,v2_idsc_itm5,v2_idsc_itm6,v2_idsc_itm7,
                          v2_idsc_itm8,v2_idsc_itm9,v2_idsc_itm9a,v2_idsc_itm9b,v2_idsc_itm10,v2_idsc_itm11,v2_idsc_itm12,
                          v2_idsc_itm13,v2_idsc_itm14,v2_idsc_itm15,v2_idsc_itm16,v2_idsc_itm17,v2_idsc_itm18,v2_idsc_itm19,
                          v2_idsc_itm20,v2_idsc_itm21,v2_idsc_itm22,v2_idsc_itm23,v2_idsc_itm24,v2_idsc_itm25,v2_idsc_itm26,
                          v2_idsc_itm27,v2_idsc_itm28,v2_idsc_itm29,v2_idsc_itm30,v2_idsc_sum)

YMRS

For more information on the scale, please see Visit 1

Item 1 Elevated mood (ordinal [0,1,2,3,4], v2_ymrs_itm1)

v2_ymrs_itm1<-c(v2_clin$v2_ymrs_ymrs1_gehob_stimm,v2_con$v2_ymrs_ymrs1_gehob_stimm)
v2_ymrs_itm1<-factor(v2_ymrs_itm1, ordered=T)

descT(v2_ymrs_itm1)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 782  108 36  4   1   612  1543
## [2,] Percent   50.7 7   2.3 0.3 0.1 39.7 100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v2_ymrs_itm2)

v2_ymrs_itm2<-c(v2_clin$v2_ymrs_ymrs2_gest_aktiv,v2_con$v2_ymrs_ymrs2_gest_aktiv)
v2_ymrs_itm2<-factor(v2_ymrs_itm2, ordered=T)

descT(v2_ymrs_itm2)
##                0   1   2   3   4   <NA>     
## [1,] No. cases 818 72  32  4   1   616  1543
## [2,] Percent   53  4.7 2.1 0.3 0.1 39.9 100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v2_ymrs_itm3)

v2_ymrs_itm3<-c(v2_clin$v2_ymrs_ymrs3_sex_interesse,v2_con$v2_ymrs_ymrs3_sex_interesse)
v2_ymrs_itm3<-factor(v2_ymrs_itm3, ordered=T)

descT(v2_ymrs_itm3)
##                0    1   2   3   <NA>     
## [1,] No. cases 887  27  14  1   614  1543
## [2,] Percent   57.5 1.7 0.9 0.1 39.8 100

Item 4 Sleep (ordinal [0,1,2,3,4], v2_ymrs_itm4)

v2_ymrs_itm4<-c(v2_clin$v2_ymrs_ymrs4_schlaf,v2_con$v2_ymrs_ymrs4_schlaf)
v2_ymrs_itm4<-factor(v2_ymrs_itm4, ordered=T)

descT(v2_ymrs_itm4)
##                0    1   2   3   <NA>     
## [1,] No. cases 859  37  23  12  612  1543
## [2,] Percent   55.7 2.4 1.5 0.8 39.7 100

Item 5 Irritability (ordinal [0,2,4,6,8], v2_ymrs_itm5)

v2_ymrs_itm5<-c(v2_clin$v2_ymrs_ymrs5_reizbarkeit,v2_con$v2_ymrs_ymrs5_reizbarkeit)
v2_ymrs_itm5<-factor(v2_ymrs_itm5, ordered=T)

descT(v2_ymrs_itm5)
##                0    2   4   6   <NA>     
## [1,] No. cases 796  114 17  4   612  1543
## [2,] Percent   51.6 7.4 1.1 0.3 39.7 100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v2_ymrs_itm6)

v2_ymrs_itm6<-c(v2_clin$v2_ymrs_ymrs6_sprechweise,v2_con$v2_ymrs_ymrs6_sprechweise)
v2_ymrs_itm6<-factor(v2_ymrs_itm6, ordered=T)

descT(v2_ymrs_itm6)
##                0    2   4   6   <NA>     
## [1,] No. cases 808  71  41  9   614  1543
## [2,] Percent   52.4 4.6 2.7 0.6 39.8 100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v2_ymrs_itm7)

v2_ymrs_itm7<-c(v2_clin$v2_ymrs_ymrs7_sprachstoer,v2_con$v2_ymrs_ymrs7_sprachstoer)
v2_ymrs_itm7<-factor(v2_ymrs_itm7, ordered=T)

descT(v2_ymrs_itm7)
##                0    1   2   3   <NA>     
## [1,] No. cases 823  86  17  4   613  1543
## [2,] Percent   53.3 5.6 1.1 0.3 39.7 100

Item 8 Content (ordinal [0,2,4,6,8], v2_ymrs_itm8)

v2_ymrs_itm8<-c(v2_clin$v2_ymrs_ymrs8_inhalte,v2_con$v2_ymrs_ymrs8_inhalte)
v2_ymrs_itm8<-factor(v2_ymrs_itm8, ordered=T)

descT(v2_ymrs_itm8)
##                0    2   4   6   8   <NA>     
## [1,] No. cases 887  25  3   7   8   613  1543
## [2,] Percent   57.5 1.6 0.2 0.5 0.5 39.7 100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v2_ymrs_itm9)

v2_ymrs_itm9<-c(v2_clin$v2_ymrs_ymrs9_exp_aggr_verh,v2_con$v2_ymrs_ymrs9_exp_aggr_verh)
v2_ymrs_itm9<-factor(v2_ymrs_itm9, ordered=T)

descT(v2_ymrs_itm9)
##                0    2   4   6   <NA>     
## [1,] No. cases 893  29  3   1   617  1543
## [2,] Percent   57.9 1.9 0.2 0.1 40   100

Item 10 Appearance (ordinal [0,1,2,3,4], v2_ymrs_itm10)

v2_ymrs_itm10<-c(v2_clin$v2_ymrs_ymrs10_erscheinung,v2_con$v2_ymrs_ymrs10_erscheinung)
v2_ymrs_itm10<-factor(v2_ymrs_itm10, ordered=T)

descT(v2_ymrs_itm10)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 840  71  13  2   1   616  1543
## [2,] Percent   54.4 4.6 0.8 0.1 0.1 39.9 100

Item 11 Insight (ordinal [0,1,2,3,4], v2_ymrs_itm11)

v2_ymrs_itm11<-c(v2_clin$v2_ymrs_ymrs11_krkh_einsicht,v2_con$v2_ymrs_ymrs11_krkh_einsicht)
v2_ymrs_itm11<-factor(v2_ymrs_itm11, ordered=T)

descT(v2_ymrs_itm11)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 897  13  9   3   3   618  1543
## [2,] Percent   58.1 0.8 0.6 0.2 0.2 40.1 100

Create YMRS total score (continuous [0-60], v2_ymrs_sum)

v2_ymrs_sum<-(as.numeric.factor(v2_ymrs_itm1)+
        as.numeric.factor(v2_ymrs_itm2)+
        as.numeric.factor(v2_ymrs_itm3)+
        as.numeric.factor(v2_ymrs_itm4)+
        as.numeric.factor(v2_ymrs_itm5)+
        as.numeric.factor(v2_ymrs_itm6)+
        as.numeric.factor(v2_ymrs_itm7)+
        as.numeric.factor(v2_ymrs_itm8)+
        as.numeric.factor(v2_ymrs_itm9)+
        as.numeric.factor(v2_ymrs_itm10)+
        as.numeric.factor(v2_ymrs_itm11))

summary(v2_ymrs_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   1.873   2.000  36.000     627

Create dataset

v2_symp_ymrs<-data.frame(v2_ymrs_itm1,
                         v2_ymrs_itm2,
                         v2_ymrs_itm3,
                         v2_ymrs_itm4,
                         v2_ymrs_itm5,
                         v2_ymrs_itm6,
                         v2_ymrs_itm7,
                         v2_ymrs_itm8,
                         v2_ymrs_itm9,
                         v2_ymrs_itm10,
                         v2_ymrs_itm11,
                         v2_ymrs_sum)

CGI

Please see Visit 1 for more details and explicit rating instructions. A zero on this scale means “not assessable” (both of the following two items) and was coded as “-999”, as were all control participants.

Illness severity (ordinal [1,2,3,4,5,6,7], v2_cgi_s)

v2_cgi_s<-c(v2_clin$v2_cgi1_cgi1_schweregrad,rep(-999,dim(v2_con)[1]))

v2_cgi_s[v2_cgi_s==0]<- -999
v2_cgi_s<-factor(v2_cgi_s, ordered=T)

descT(v2_cgi_s)
##                -999 1   2   3    4    5   6   7   <NA>     
## [1,] No. cases 324  30  72  219  238  127 40  1   492  1543
## [2,] Percent   21   1.9 4.7 14.2 15.4 8.2 2.6 0.1 31.9 100

Change since last study visit (ordinal [1,2,3,4,5,6,7], v2_cgi_c)

During follow-up visits, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. These range from “very much improved”-1 to “very much worse”-7.

v2_cgi_c<-c(v2_clin$v2_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v2_con)[1]))

v2_cgi_c[v2_cgi_c==0]<- -999
v2_cgi_c<-factor(v2_cgi_c, ordered=T)

descT(v2_cgi_c)
##                -999 1   2   3    4    5   6   7   <NA>     
## [1,] No. cases 336  30  136 191  206  79  19  2   544  1543
## [2,] Percent   21.8 1.9 8.8 12.4 13.4 5.1 1.2 0.1 35.3 100

GAF (continuous [1-100], v2_gaf)

Please see Visit 1 for more details and explicit rating instructions. A zero on this scale means “not assessable” and was coded as “-999”.

v2_gaf<-c(v2_clin$v2_gaf_gaf_code,v2_con$v2_gaf_gaf_code)
v2_gaf[v2_gaf==0]<- -999

summary(v2_gaf[v2_gaf>0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   20.00   55.00   67.00   67.17   81.00  100.00     598

Boxplot of GAF scores of both CLINICAL and CONTROL study participants

boxplot(v2_gaf[v2_gaf>0 & v1_stat=="CLINICAL"], v2_gaf[v2_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v2_ill_sev<-data.frame(v2_cgi_s,v2_cgi_c,v2_gaf)

Visit 2: Neuropsychology (cognitive tests)

There are two differences compared to the test battery assessed in Visit 1:

  1. The “Verbaler Lern- und Merkfähigkeitstest” is added (assesses learning and memory). This test is also implemented in all following visits. Parallel versions of the test are alternately used to avoid recall bias.

  2. The MWT-B is omitted as results of this test are not expected to vary much during the timeframe of the study.

General comments on the testing (character, v2_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v2_nrpsy_lng)

v2_nrpsy_lng<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_nrpsy_lng<-ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==0, "mother tongue", 
                     ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==1, "good", 
                            ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==2, "sufficient", 
                              ifelse(c(v2_clin$v2_npu1_np_sprach,v2_con$v2_npu_folge_np_sprach)==3, "not sufficient",v2_nrpsy_lng))))
                            
v2_nrpsy_lng<-factor(v2_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v2_nrpsy_lng)
##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 895           63   6          1              578  1543
## [2,] Percent   58            4.1  0.4        0.1            37.5 100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v2_nrpsy_mtv)

v2_nrpsy_mtv_pre<-c(v2_clin$v2_npu1_np_mot,v2_con$v2_npu_folge_np_mot)

v2_nrpsy_mtv<-ifelse(v2_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v2_nrpsy_mtv_pre==1, "average", 
                      ifelse(v2_nrpsy_mtv_pre==2, "good", NA)))

v2_nrpsy_mtv<-factor(v2_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))
descT(v2_nrpsy_mtv)
##                poor average good <NA>     
## [1,] No. cases 12   85      854  592  1543
## [2,] Percent   0.8  5.5     55.3 38.4 100

Learning and memory: Verbaler Lern- und Merkfähigkeitstest (VLMT)

The VLMT (Helmstaedter, Lendt, & Lux, 2001) assesses learning and memory. A list of 15 words (list 1) is verbally presented to the participant for five times. After each presentation, the subject is required to recall as many words from the list as he remembers and the interviewer writes those down. After the fifth time, another list of words (list 2; distraction) is presented to the subject, with the same instruction (“recall as many words as possible from the list after I read it to you”). After writing down the recalled words from list 2, the interviewer asks the participant to recall the words from list 1 and writes those down. After a time interval of 25-30 minutes, during which other tests are performed, the interviewer asks the participant again and writes down the recalled words form list 1. Following this free recall phase, the interviewer tests recognition of the words from list 1 by verbally presenting 50 words (from list 1, list 2 and completely new words) and asking the participant whether each word belongs to list 1 or not.

Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.

VLMT_introcheck (categorical [0, 1, 9], v2_nrpsy_vlmt_check) This variable indicates whether a test was:

  • Completed -> “1”
  • Aborted -> “9”
  • Not assessed -> “0”

In contrast to previous versions of the dataset, data are not filtered according to this item but all tests are included.

v2_nrpsy_vlmt_check<-c(v2_clin$v2_vlmt_vlmt_introcheck1,v2_con$v2_npu_folge_np_vlmt)
descT(v2_nrpsy_vlmt_check)
##                0   1    9   <NA>     
## [1,] No. cases 72  858  45  568  1543
## [2,] Percent   4.7 55.6 2.9 36.8 100

Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v2_nrpsy_vlmt_corr)

v2_nrpsy_vlmt_corr<-c(v2_clin$v2_vlmt_vlmt3_sw_a5d,v2_con$v2_npu_folge_np_vlmt_gl)
summary(v2_nrpsy_vlmt_corr)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   40.00   50.00   48.74   59.00   75.00     638

Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v2_nrpsy_vlmt_lss_d)

v2_nrpsy_vlmt_lss_d<-c(v2_clin$v2_vlmt_vlmt5_aw_ilsd6,v2_con$v2_npu_folge_np_vlmt_vni)
summary(v2_nrpsy_vlmt_lss_d)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -6.000   0.000   2.000   1.779   3.000   9.000     642

Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v2_nrpsy_vlmt_lss_t)

v2_nrpsy_vlmt_lss_t<-c(v2_clin$v2_vlmt_vlmt6_aw_vwd7,v2_con$v2_npu_folge_np_vlmt_vnzv)
summary(v2_nrpsy_vlmt_lss_t)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -5.000   0.000   2.000   1.889   3.000  13.000     655

Recognition performance (corrected for falsely recognized words) (continuous [number of words], v2_nrpsy_vlmt_rec)

v2_nrpsy_vlmt_rec<-c(v2_clin$v2_vlmt_vlmt8_kwl,v2_con$v2_npu_folge_np_vlmt_kw)
summary(v2_nrpsy_vlmt_rec)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -12.00   10.00   13.00   11.62   15.00   15.00     665

TMT

For a description of the test, see Visit 1.

TMT Part A, time (continuous [seconds], v2_nrpsy_tmt_A_rt)

v2_nrpsy_tmt_A_rt<-c(v2_clin$v2_npu1_tmt_001,v2_con$v2_npu_folge_np_tmt_001)
summary(v2_nrpsy_tmt_A_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   21.00   28.00   32.07   38.00  142.00     576

TMT Part A, errors (continuous [number of errors], v2_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).

v2_nrpsy_tmt_A_err<-c(v2_clin$v2_npu1_tmt_af_001,v2_con$v2_npu_folge_np_tmtfehler_001)
summary(v2_nrpsy_tmt_A_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   0.146   0.000   4.000     584

TMT Part B, time (continuous [seconds], v2_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v2_nrpsy_tmt_B_rt<-c(v2_clin$v2_npu1_tmt_002,v2_con$v2_npu_folge_tmt_002)
v2_nrpsy_tmt_B_rt[v2_nrpsy_tmt_B_rt>300]<-300

summary(v2_nrpsy_tmt_B_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   22.00   49.00   64.00   74.33   89.00  300.00     626

TMT Part B, errors (continuous [number of errors], v2_nrpsy_tmt_B_err)

v2_nrpsy_tmt_B_err<-c(v2_clin$v2_npu1_tmt_af_002,v2_con$v2_npu_folge_tmt_af_002)
summary(v2_nrpsy_tmt_B_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.5011  1.0000 20.0000     633

Verbal digit span

For a description of the test, see Visit 1.

Forward (continuous [number of items], v2_nrpsy_dgt_sp_frw)

v2_nrpsy_dgt_sp_frw<-c(v2_clin$v2_npu1_zns_001,v2_con$v2_npu_folge_np_wie_001)
summary(v2_nrpsy_dgt_sp_frw)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00    8.00   10.00    9.69   11.00   16.00     585

Backward (continuous [number of items], v2_nrpsy_dgt_sp_bck)

v2_nrpsy_dgt_sp_bck<-c(v2_clin$v2_npu1_zns_002,v2_con$v2_npu_folge_np_wie_002)
summary(v2_nrpsy_dgt_sp_bck)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.000   6.000   6.535   8.000  14.000     588

DST (continuous [number of correct symbols], v2_nrpsy_dg_sym)

For a description of the test and the coding of incompletet tests, see Visit 1.

v2_introcheck3<-c(v2_clin$v2_npu1_np_introcheck3,v2_con$v2_npu_folge_np_hawier)
v2_nrpsy_dg_sym_pre<-c(v2_clin$v2_npu1_zst_001,v2_con$v2_npu_folge_np_hawier_001)

v2_nrpsy_dg_sym<-ifelse(v2_introcheck3==1, v2_nrpsy_dg_sym_pre, 
                           ifelse(v2_introcheck3==9,-999,
                                  ifelse(v2_introcheck3==0,NA,NA)))

summary(subset(v2_nrpsy_dg_sym,v2_nrpsy_dg_sym>=0))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   17.00   51.00   66.00   66.27   82.00  133.00

Create dataset

v2_nrpsy<-data.frame(v2_nrpsy_com,
                     v2_nrpsy_lng,
                     v2_nrpsy_mtv,
                     v2_nrpsy_vlmt_check,
                     v2_nrpsy_vlmt_corr,
                     v2_nrpsy_vlmt_lss_d,
                     v2_nrpsy_vlmt_lss_t,
                     v2_nrpsy_vlmt_rec,
                     v2_nrpsy_tmt_A_rt,
                     v2_nrpsy_tmt_A_err,
                     v2_nrpsy_tmt_B_rt,
                     v2_nrpsy_tmt_B_err,
                     v2_nrpsy_dgt_sp_frw,
                     v2_nrpsy_dgt_sp_bck,
                     v2_nrpsy_dg_sym)

Visit 2: Questionnaires (patient rates her/himself)

All participants were asked to fill out questionnaires on the following topics: current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months (LEQ), and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 1 and 2) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1, all questionnaires were checked on whether they were filled out correctly or not and only those correctly filled-out are included in this dataset.

SF-12

For explanation, please refer to the section in Visit 1

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v2_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v2_sf12_recode(v2_con$v2_sf12_sf_allgemein,"v2_sf12_itm0")
##                -999 1   2   3   4   5   6   7   8   9   10  <NA>     
## [1,] No. cases 1223 1   2   6   6   8   9   32  94  58  30  74   1543
## [2,] Percent   79.3 0.1 0.1 0.4 0.4 0.5 0.6 2.1 6.1 3.8 1.9 4.8  100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v2_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v2_sf12_recode(v2_con$v2_sf12_sf1,"v2_sf12_itm1")
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1223 49  114 83  11  63   1543
## [2,] Percent   79.3 3.2 7.4 5.4 0.7 4.1  100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v2_sf12_itm2)

Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v2_sf12_recode(v2_con$v2_sf12_sf2,"v2_sf12_itm2")
##                -999 1   2   3    <NA>     
## [1,] No. cases 1223 3   30  223  64   1543
## [2,] Percent   79.3 0.2 1.9 14.5 4.1  100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v2_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v2_sf12_recode(v2_con$v2_sf12_sf3,"v2_sf12_itm3")
##                -999 1   2   3    <NA>     
## [1,] No. cases 1223 5   35  215  65   1543
## [2,] Percent   79.3 0.3 2.3 13.9 4.2  100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v2_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf4,"v2_sf12_itm4")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 35  219  66   1543
## [2,] Percent   79.3 2.3 14.2 4.3  100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v2_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf5,"v2_sf12_itm5")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 21  233  66   1543
## [2,] Percent   79.3 1.4 15.1 4.3  100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v2_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf6,"v2_sf12_itm6")
##                -999 1   2   <NA>     
## [1,] No. cases 1223 24  231 65   1543
## [2,] Percent   79.3 1.6 15  4.2  100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v2_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v2_sf12_recode(v2_con$v2_sf12_sf7,"v2_sf12_itm7")
##                -999 1  2    <NA>     
## [1,] No. cases 1223 16 240  64   1543
## [2,] Percent   79.3 1  15.6 4.1  100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v2_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.

v2_sf12_recode(v2_con$v2_sf12_st8,"v2_sf12_itm8")
##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1223 132 53  33  24  9   2   67   1543
## [2,] Percent   79.3 8.6 3.4 2.1 1.6 0.6 0.1 4.3  100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v2_sf12_itm9)

v2_sf12_recode(v2_con$v2_sf12_st9,"v2_sf12_itm9")
##                -999 1  2    3   4   5   6   <NA>     
## [1,] No. cases 1223 16 166  48  17  8   1   64   1543
## [2,] Percent   79.3 1  10.8 3.1 1.1 0.5 0.1 4.1  100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v2_sf12_itm10)

v2_sf12_recode(v2_con$v2_sf12_st10,"v2_sf12_itm10")
##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1223 10  96  81  55  13  1   64   1543
## [2,] Percent   79.3 0.6 6.2 5.2 3.6 0.8 0.1 4.1  100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v2_sf12_itm11)

v2_sf12_recode(v2_con$v2_sf12_st11,"v2_sf12_itm11")
##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1223 1   4   9   30  125 87  64   1543
## [2,] Percent   79.3 0.1 0.3 0.6 1.9 8.1 5.6 4.1  100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v2_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

ATTENTION: there appears to be something wrong with the coding of this item, i.e. the export of the item and the display in the GUI of the database are inconsistent. NOT CONTAINED IN DATASET FOR NOW

v2_sf12_recode(v2_con$v2_sf12_st12,"v2_sf12_itm12")

Create dataset

v2_sf12<-data.frame(v2_sf12_itm0,
                    v2_sf12_itm1,
                    v2_sf12_itm2,
                    v2_sf12_itm3,
                    v2_sf12_itm4,
                    v2_sf12_itm5,
                    v2_sf12_itm6,
                    v2_sf12_itm7,
                    v2_sf12_itm8,
                    v2_sf12_itm9,
                    v2_sf12_itm10,
                    v2_sf12_itm11)

#INCLUDE v2_sf12_itm12 when issues are settled

Medication adherence (compliance)

For a description of the questionnaire, see Visit 1.

Past seven days (ordinal [1,2,3,4,5,6], v2_med_pst_wk)

v2_med_chk<-c(v2_clin$v2_compl_verwer_fragebogen,rep(1,dim(v2_con)[1]))
v2_med_pst_wk_pre<-c(v2_clin$v2_compl_psychopharm_7_tag,rep(-999,dim(v2_con)[1]))
  
v2_med_pst_wk<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_med_pst_wk<-ifelse((is.na(v2_med_chk) | v2_med_chk!=2), 
                      v2_med_pst_wk_pre, v2_med_pst_wk)

descT(v2_med_pst_wk)
##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 320  574  71  27  3   1   18  529  1543
## [2,] Percent   20.7 37.2 4.6 1.7 0.2 0.1 1.2 34.3 100

Past six months (ordinal [1,2,3,4,5,6], v2_med_pst_sx_mths)

v2_med_pre<-c(v2_clin$v2_compl_psychopharm_6_mon,rep(-999,dim(v2_con)[1]))

v2_med_pst_sx_mths<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])
v2_med_pst_sx_mths<-ifelse((is.na(v2_med_chk) | v2_med_chk!=2),
                           v2_med_pre, v2_med_pst_sx_mths)

descT(v2_med_pst_sx_mths)
##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 320  510  105 57  8   5   10  528  1543
## [2,] Percent   20.7 33.1 6.8 3.7 0.5 0.3 0.6 34.2 100

Create dataset

v2_med_adh<-data.frame(v2_med_pst_wk,v2_med_pst_sx_mths)

BDI-II

For explanation, please refer to the section in Visit 1

1. Sadness (ordinal [0,1,2,3], v2_bdi2_itm1)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi1_traurigkeit,v2_con$v2_bdi2_s1_bdi1,"v2_bdi2_itm1")
##                0    1    2   3   <NA>     
## [1,] No. cases 671  241  21  22  588  1543
## [2,] Percent   43.5 15.6 1.4 1.4 38.1 100

2. Pessimism (ordinal [0,1,2,3], v2_bdi2_itm2)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi2_pessimismus,v2_con$v2_bdi2_s1_bdi2,"v2_bdi2_itm2")
##                0    1   2   3   <NA>     
## [1,] No. cases 729  125 86  14  589  1543
## [2,] Percent   47.2 8.1 5.6 0.9 38.2 100

3. Past failure (ordinal [0,1,2,3], v2_bdi2_itm3)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi3_versagensgef,v2_con$v2_bdi2_s1_bdi3,"v2_bdi2_itm3")
##                0   1    2   3   <NA>     
## [1,] No. cases 633 166  136 18  590  1543
## [2,] Percent   41  10.8 8.8 1.2 38.2 100

4. Loss of pleasure (ordinal [0,1,2,3], v2_bdi2_itm4)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi4_verlust_freude,v2_con$v2_bdi2_s1_bdi4,"v2_bdi2_itm4")
##                0    1    2   3   <NA>     
## [1,] No. cases 592  266  68  22  595  1543
## [2,] Percent   38.4 17.2 4.4 1.4 38.6 100

5. Guilty feelings (ordinal [0,1,2,3], v2_bdi2_itm5)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi5_schuldgef,v2_con$v2_bdi2_s1_bdi5,"v2_bdi2_itm5")
##                0    1    2   3   <NA>     
## [1,] No. cases 678  237  18  18  592  1543
## [2,] Percent   43.9 15.4 1.2 1.2 38.4 100

6. Punishment feelings (ordinal [0,1,2,3], v2_bdi2_itm6)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi6_bestrafungsgef,v2_con$v2_bdi2_s1_bdi6,"v2_bdi2_itm6")
##                0    1   2   3  <NA>     
## [1,] No. cases 789  105 14  46 589  1543
## [2,] Percent   51.1 6.8 0.9 3  38.2 100

7. Self-dislike (ordinal [0,1,2,3], v2_bdi2_itm7)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi7_selbstablehnung,v2_con$v2_bdi2_s1_bdi7,"v2_bdi2_itm7")
##                0    1   2   3   <NA>     
## [1,] No. cases 713  140 81  17  592  1543
## [2,] Percent   46.2 9.1 5.2 1.1 38.4 100

8. Self-criticalness (ordinal [0,1,2,3], v2_bdi2_itm8)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi8_selbstvorwuerfe,v2_con$v2_bdi2_s1_bdi8,"v2_bdi2_itm8")
##                0    1    2  3   <NA>     
## [1,] No. cases 623  230  77 22  591  1543
## [2,] Percent   40.4 14.9 5  1.4 38.3 100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v2_bdi2_itm9)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi9_selbstmordged,v2_con$v2_bdi2_s1_bdi9,"v2_bdi2_itm9")
##                0    1    2   3   <NA>     
## [1,] No. cases 775  167  11  1   589  1543
## [2,] Percent   50.2 10.8 0.7 0.1 38.2 100

10. Crying (ordinal [0,1,2,3], v2_bdi2_itm10)

v2_bdi2_recode(v2_clin$v2_bdi2_s1_bdi10_weinen,v2_con$v2_bdi2_s1_bdi10,"v2_bdi2_itm10")
##                0    1   2   3   <NA>     
## [1,] No. cases 780  80  22  72  589  1543
## [2,] Percent   50.6 5.2 1.4 4.7 38.2 100

11. Agitation (ordinal [0,1,2,3], v2_bdi2_itm11)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi11_unruhe,v2_con$v2_bdi2_s2_bdi11,"v2_bdi2_itm11")
##                0    1   2   3  <NA>     
## [1,] No. cases 669  232 27  15 600  1543
## [2,] Percent   43.4 15  1.7 1  38.9 100

12. Loss of interest (ordinal [0,1,2,3], v2_bdi2_itm12)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi12_interessverl,v2_con$v2_bdi2_s2_bdi12,"v2_bdi2_itm12")
##                0   1    2  3   <NA>     
## [1,] No. cases 664 203  47 28  601  1543
## [2,] Percent   43  13.2 3  1.8 39   100

13. Indecisiveness (ordinal [0,1,2,3], v2_bdi2_itm13)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi13_entschlussunf,v2_con$v2_bdi2_s2_bdi13,"v2_bdi2_itm13")
##                0    1    2   3   <NA>     
## [1,] No. cases 638  218  50  36  601  1543
## [2,] Percent   41.3 14.1 3.2 2.3 39   100

14. Worthlessness (ordinal [0,1,2,3], v2_bdi2_itm14)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi14_wertlosigkeit,v2_con$v2_bdi2_s2_bdi14,"v2_bdi2_itm14")
##                0    1   2  3   <NA>     
## [1,] No. cases 729  129 61 24  600  1543
## [2,] Percent   47.2 8.4 4  1.6 38.9 100

15. Loss of energy (ordinal [0,1,2,3], v2_bdi2_itm15)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi15_energieverlust,v2_con$v2_bdi2_s2_bdi15,"v2_bdi2_itm15")
##                0    1    2   3  <NA>     
## [1,] No. cases 528  312  89  15 599  1543
## [2,] Percent   34.2 20.2 5.8 1  38.8 100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v2_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep“. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v2_itm_bdi2_chk<-c(v2_clin$v2_bdi2_s1_verwer_fragebogen,v2_con$v2_bdi2_s1_bdi_korrekt)
v2_itm_bdi2_itm16_clin_con<-c(v2_clin$v2_bdi2_s2_bdi16_schlafgewohn,v2_con$v2_bdi2_s2_bdi16)

v2_bdi2_itm16<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])

v2_bdi2_itm16<-ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & v2_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                           (v2_itm_bdi2_itm16_clin_con==1 | v2_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm16_clin_con==2 | v2_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm16_clin_con==3 | v2_itm_bdi2_itm16_clin_con==300), 3, v2_bdi2_itm16))))  

v2_bdi2_itm16<-factor(v2_bdi2_itm16,ordered=T)
descT(v2_bdi2_itm16)
##                0    1    2   3   <NA>     
## [1,] No. cases 495  330  75  41  602  1543
## [2,] Percent   32.1 21.4 4.9 2.7 39   100

17. Irritability (ordinal [0,1,2,3], v2_bdi2_itm17)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi17_reizbarkeit,v2_con$v2_bdi2_s2_bdi17,"v2_bdi2_itm17")
##                0   1    2  3   <NA>     
## [1,] No. cases 740 162  31 10  600  1543
## [2,] Percent   48  10.5 2  0.6 38.9 100

18. Change in appetite (ordinal [0,1,2,3], v2_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v2_itm_bdi2_itm18_clin_con<-c(v2_clin$v2_bdi2_s2_bdi18_appetit,v2_con$v2_bdi2_s2_bdi18)
v2_bdi2_itm18<-rep(NA,dim(v2_clin)[1]+dim(v2_con)[1])

v2_bdi2_itm18<-ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & v2_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                           (v2_itm_bdi2_itm18_clin_con==1 | v2_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm18_clin_con==2 | v2_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v2_itm_bdi2_chk) | v2_itm_bdi2_chk!=2) & 
                               (v2_itm_bdi2_itm18_clin_con==3 | v2_itm_bdi2_itm18_clin_con==300), 3, v2_bdi2_itm18))))  

v2_bdi2_itm18<-factor(v2_bdi2_itm18,ordered=T)
descT(v2_bdi2_itm18)
##                0    1   2  3   <NA>     
## [1,] No. cases 625  247 46 25  600  1543
## [2,] Percent   40.5 16  3  1.6 38.9 100

19. Concentration difficulty (ordinal [0,1,2,3], v2_bdi2_itm19)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi19_konzschw,v2_con$v2_bdi2_s2_bdi19,"v2_bdi2_itm19")
##                0    1    2   3   <NA>     
## [1,] No. cases 554  249  127 9   604  1543
## [2,] Percent   35.9 16.1 8.2 0.6 39.1 100

20. Tiredness or fatigue (ordinal [0,1,2,3], v2_bdi2_itm20)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi20_ermued_ersch,v2_con$v2_bdi2_s2_bdi20,"v2_bdi2_itm20")
##                0    1   2   3   <NA>     
## [1,] No. cases 554  293 78  18  600  1543
## [2,] Percent   35.9 19  5.1 1.2 38.9 100

21. Loss of interest in sex (ordinal [0,1,2,3], v2_bdi2_itm21)

v2_bdi2_recode(v2_clin$v2_bdi2_s2_bdi21_sex_interess,v2_con$v2_bdi2_s2_bdi21,"v2_bdi2_itm21")
##                0    1    2   3   <NA>     
## [1,] No. cases 652  161  42  81  607  1543
## [2,] Percent   42.3 10.4 2.7 5.2 39.3 100

BDI-II sum score calculation (continuous [0-63], v2_bdi2_sum)

v2_bdi2_sum<-as.numeric.factor(v2_bdi2_itm1)+
              as.numeric.factor(v2_bdi2_itm2)+
              as.numeric.factor(v2_bdi2_itm3)+
              as.numeric.factor(v2_bdi2_itm4)+
              as.numeric.factor(v2_bdi2_itm5)+
              as.numeric.factor(v2_bdi2_itm6)+
              as.numeric.factor(v2_bdi2_itm7)+
              as.numeric.factor(v2_bdi2_itm8)+
              as.numeric.factor(v2_bdi2_itm9)+
              as.numeric.factor(v2_bdi2_itm10)+
              as.numeric.factor(v2_bdi2_itm11)+
              as.numeric.factor(v2_bdi2_itm12)+
              as.numeric.factor(v2_bdi2_itm13)+
              as.numeric.factor(v2_bdi2_itm14)+
              as.numeric.factor(v2_bdi2_itm15)+
              as.numeric.factor(v2_bdi2_itm16)+
              as.numeric.factor(v2_bdi2_itm17)+
              as.numeric.factor(v2_bdi2_itm18)+
              as.numeric.factor(v2_bdi2_itm19)+
              as.numeric.factor(v2_bdi2_itm20)+
              as.numeric.factor(v2_bdi2_itm21)

summary(v2_bdi2_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   5.000   8.831  13.000  54.000     643

Create dataset

v2_bdi2<-data.frame(v2_bdi2_itm1,v2_bdi2_itm2,v2_bdi2_itm3,v2_bdi2_itm4,v2_bdi2_itm5,
                    v2_bdi2_itm6,v2_bdi2_itm7,v2_bdi2_itm8,v2_bdi2_itm9,v2_bdi2_itm10,
                    v2_bdi2_itm11,v2_bdi2_itm12,v2_bdi2_itm13,v2_bdi2_itm14,
                    v2_bdi2_itm15,v2_bdi2_itm16,v2_bdi2_itm17,v2_bdi2_itm18,
                    v2_bdi2_itm19,v2_bdi2_itm20,v2_bdi2_itm21, v2_bdi2_sum)

ASRM

For explanation, please refer to the section in Visit 1

1. Positive Mood (ordinal [0,1,2,3,4], v2_asrm_itm1)

v2_asrm_recode(v2_clin$v2_asrm_asrm1_gluecklich,v2_con$v2_asrm_asrm1,"v2_asrm_itm1")
##                0    1    2   3   4   <NA>     
## [1,] No. cases 674  208  39  23  8   591  1543
## [2,] Percent   43.7 13.5 2.5 1.5 0.5 38.3 100

2 Self-Confidence (ordinal [0,1,2,3,4], v2_asrm_itm2)

v2_asrm_recode(v2_clin$v2_asrm_asrm2_selbstbewusst,v2_con$v2_asrm_asrm2,"v2_asrm_itm2")
##                0    1    2   3   4   <NA>     
## [1,] No. cases 713  184  37  13  7   589  1543
## [2,] Percent   46.2 11.9 2.4 0.8 0.5 38.2 100

3. Sleep (ordinal [0,1,2,3,4], v2_asrm_itm3)

v2_asrm_recode(v2_clin$v2_asrm_asrm3_schlaf,v2_con$v2_asrm_asrm3,"v2_asrm_itm3")
##                0   1   2   3   4   <NA>     
## [1,] No. cases 803 107 27  8   9   589  1543
## [2,] Percent   52  6.9 1.7 0.5 0.6 38.2 100

4. Speech (ordinal [0,1,2,3,4], v2_asrm_itm4)

v2_asrm_recode(v2_clin$v2_asrm_asrm4_reden,v2_con$v2_asrm_asrm4,"v2_asrm_itm4")
##                0    1    2   3   4   <NA>     
## [1,] No. cases 743  174  24  10  2   590  1543
## [2,] Percent   48.2 11.3 1.6 0.6 0.1 38.2 100

5. Activity Level (ordinal [0,1,2,3,4], v2_asrm_itm5)

v2_asrm_recode(v2_clin$v2_asrm_asrm5_aktiv,v2_con$v2_asrm_asrm5,"v2_asrm_itm5")
##                0    1    2   3   4   <NA>     
## [1,] No. cases 696  193  40  14  10  590  1543
## [2,] Percent   45.1 12.5 2.6 0.9 0.6 38.2 100

Create ASRM sum score (continuous [0-20],v2_asrm_sum)

v2_asrm_sum<-as.numeric.factor(v2_asrm_itm1)+
            as.numeric.factor(v2_asrm_itm2)+
            as.numeric.factor(v2_asrm_itm3)+
            as.numeric.factor(v2_asrm_itm4)+
            as.numeric.factor(v2_asrm_itm5)

summary(v2_asrm_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   1.000   1.616   3.000  16.000     593

Create dataset

v2_asrm<-data.frame(v2_asrm_itm1,v2_asrm_itm2,v2_asrm_itm3,v2_asrm_itm4,v2_asrm_itm5,v2_asrm_sum)

MSS

For explanation, please refer to the section in Visit 1

1. “I had more energy” (dichotomous, v2_mss_itm1)

v2_mss_recode(v2_clin$v2_mss_s1_mss1_energie,v2_con$v2_mss_s1_mss1,"v2_mss_itm1")
##                N    Y    <NA>     
## [1,] No. cases 751  190  602  1543
## [2,] Percent   48.7 12.3 39   100

2. “I had trouble sitting still” (dichotomous, v2_mss_itm2)

v2_mss_recode(v2_clin$v2_mss_s1_mss2_ruhig_sitzen,v2_con$v2_mss_s1_mss2,"v2_mss_itm2")
##                N    Y   <NA>     
## [1,] No. cases 800  138 605  1543
## [2,] Percent   51.8 8.9 39.2 100

3. “I drove faster” (dichotomous, v2_mss_itm3)

v2_mss_recode(v2_clin$v2_mss_s1_mss3_auto_fahren,v2_con$v2_mss_s1_mss3,"v2_mss_itm3")
##                N   Y   <NA>     
## [1,] No. cases 880 35  628  1543
## [2,] Percent   57  2.3 40.7 100

4. “I drank more alcoholic beverages” (dichotomous, v2_mss_itm4)

v2_mss_recode(v2_clin$v2_mss_s1_mss4_alkohol,v2_con$v2_mss_s1_mss4,"v2_mss_itm4")
##                N    Y   <NA>     
## [1,] No. cases 866  72  605  1543
## [2,] Percent   56.1 4.7 39.2 100

5. “I changed clothes several times a day” (dichotomous, v2_mss_itm5)

v2_mss_recode(v2_clin$v2_mss_s1_mss5_umziehen, v2_con$v2_mss_s1_mss5,"v2_mss_itm5")
##                N    Y  <NA>     
## [1,] No. cases 875  62 606  1543
## [2,] Percent   56.7 4  39.3 100

6. “I wore brighter clothes/make-up” (dichotomous, v2_mss_itm6)

v2_mss_recode(v2_clin$v2_mss_s1_mss6_bunter,v2_con$v2_mss_s1_mss6,"v2_mss_itm6")
##                N    Y   <NA>     
## [1,] No. cases 888  50  605  1543
## [2,] Percent   57.6 3.2 39.2 100

7. “I played music louder” (dichotomous, v2_mss_itm7)

v2_mss_recode(v2_clin$v2_mss_s1_mss7_musik_lauter,v2_con$v2_mss_s1_mss7,"v2_mss_itm7")
##                N    Y   <NA>     
## [1,] No. cases 815  127 601  1543
## [2,] Percent   52.8 8.2 39   100

8. “I ate faster than usual” (dichotomous, v2_mss_itm8)

v2_mss_recode(v2_clin$v2_mss_s1_mss8_hastiger_essen,v2_con$v2_mss_s1_mss8,"v2_mss_itm8")
##                N    Y   <NA>     
## [1,] No. cases 822  117 604  1543
## [2,] Percent   53.3 7.6 39.1 100

9. “I ate more than usual” (dichotomous, v2_mss_itm9)

v2_mss_recode(v2_clin$v2_mss_s1_mss9_mehr_essen,v2_con$v2_mss_s1_mss9,"v2_mss_itm9")
##                N    Y    <NA>     
## [1,] No. cases 728  211  604  1543
## [2,] Percent   47.2 13.7 39.1 100

10. “I slept fewer hours than usual” (dichotomous, v2_mss_itm10)

v2_mss_recode(v2_clin$v2_mss_s1_mss10_weniger_schlaf,v2_con$v2_mss_s1_mss10,"v2_mss_itm10")
##                N    Y  <NA>     
## [1,] No. cases 843  92 608  1543
## [2,] Percent   54.6 6  39.4 100

11. “I started things that I didn’t finish” (dichotomous, v2_mss_itm11)

v2_mss_recode(v2_clin$v2_mss_s1_mss11_unbeendet,v2_con$v2_mss_s1_mss11,"v2_mss_itm11")
##                N    Y    <NA>     
## [1,] No. cases 749  191  603  1543
## [2,] Percent   48.5 12.4 39.1 100

12. “I gave away my own possessions” (dichotomous, v2_mss_itm12)

v2_mss_recode(v2_clin$v2_mss_s1_mss12_weggeben,v2_con$v2_mss_s1_mss12,"v2_mss_itm12")
##                N    Y   <NA>     
## [1,] No. cases 850  89  604  1543
## [2,] Percent   55.1 5.8 39.1 100

13. “I bought gifts for people” (dichotomous, v2_mss_itm13)

v2_mss_recode(v2_clin$v2_mss_s1_mss13_geschenke,v2_con$v2_mss_s1_mss13,"v2_mss_itm13")
##                N    Y   <NA>     
## [1,] No. cases 854  84  605  1543
## [2,] Percent   55.3 5.4 39.2 100

14. “I spent money more freely” (dichotomous, v2_mss_itm14)

v2_mss_recode(v2_clin$v2_mss_s1_mss14_mehr_geld,v2_con$v2_mss_s1_mss14,"v2_mss_itm14")
##                N    Y    <NA>     
## [1,] No. cases 736  205  602  1543
## [2,] Percent   47.7 13.3 39   100

15. “I accumulated debts” (dichotomous, v2_mss_itm15)

v2_mss_recode(v2_clin$v2_mss_s1_mss15_schulden,v2_con$v2_mss_s1_mss15,"v2_mss_itm15")
##                N    Y   <NA>     
## [1,] No. cases 890  51  602  1543
## [2,] Percent   57.7 3.3 39   100

16. “I made unwise business decisions” (dichotomous, v2_mss_itm16)

v2_mss_recode(v2_clin$v2_mss_s1_mss16_unkluge_entsch,v2_con$v2_mss_s1_mss16,"v2_mss_itm16")
##                N    Y   <NA>     
## [1,] No. cases 899  37  607  1543
## [2,] Percent   58.3 2.4 39.3 100

17. “I partied more” (dichotomous, v2_mss_itm17)

v2_mss_recode(v2_clin$v2_mss_s1_mss17_parties,v2_con$v2_mss_s1_mss17,"v2_mss_itm17")
##                N    Y   <NA>     
## [1,] No. cases 881  57  605  1543
## [2,] Percent   57.1 3.7 39.2 100

18. “I enjoyed flirting” (dichotomous, v2_mss_itm18)

v2_mss_recode(v2_clin$v2_mss_s1_mss18_flirten,v2_con$v2_mss_s1_mss18,"v2_mss_itm18")
##                N   Y   <NA>     
## [1,] No. cases 864 75  604  1543
## [2,] Percent   56  4.9 39.1 100

19. “I masturbated more often” (dichotomous, v2_mss_itm19)

v2_mss_recode(v2_clin$v2_mss_s2_mss19_selbstbefried,v2_con$v2_mss_s2_mss19,"v2_mss_itm19")
##                N    Y   <NA>     
## [1,] No. cases 889  40  614  1543
## [2,] Percent   57.6 2.6 39.8 100

20. “I was more interested in sex than usual” (dichotomous, v2_mss_itm20)

v2_mss_recode(v2_clin$v2_mss_s2_mss20_sex_interess,v2_con$v2_mss_s2_mss20,"v2_mss_itm20")
##                N    Y  <NA>     
## [1,] No. cases 845  77 621  1543
## [2,] Percent   54.8 5  40.2 100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v2_mss_itm21)

v2_mss_recode(v2_clin$v2_mss_s2_mss21_sexpartner,v2_con$v2_mss_s2_mss21,"v2_mss_itm21")
##                N    Y   <NA>     
## [1,] No. cases 915  14  614  1543
## [2,] Percent   59.3 0.9 39.8 100

22. “I spent more time on the phone” (dichotomous, v2_mss_itm22)

v2_mss_recode(v2_clin$v2_mss_s2_mss22_mehr_telefon,v2_con$v2_mss_s2_mss22,"v2_mss_itm22")
##                N    Y   <NA>     
## [1,] No. cases 821  110 612  1543
## [2,] Percent   53.2 7.1 39.7 100

23. “I spoke louder than usual” (dichotomous, v2_mss_itm23)

v2_mss_recode(v2_clin$v2_mss_s2_mss23_sprache_lauter,v2_con$v2_mss_s2_mss23,"v2_mss_itm23")
##                N    Y  <NA>     
## [1,] No. cases 850  77 616  1543
## [2,] Percent   55.1 5  39.9 100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v2_mss_itm24)

v2_mss_recode(v2_clin$v2_mss_s2_mss24_spr_schneller,v2_con$v2_mss_s2_mss24,"v2_mss_itm24")
##                N    Y   <NA>     
## [1,] No. cases 876  55  612  1543
## [2,] Percent   56.8 3.6 39.7 100

25. “1 enjoyed punning or rhyming” (dichotomous, v2_mss_itm25)

v2_mss_recode(v2_clin$v2_mss_s2_mss25_witze,v2_con$v2_mss_s2_mss25,"v2_mss_itm25")
##                N    Y   <NA>     
## [1,] No. cases 856  75  612  1543
## [2,] Percent   55.5 4.9 39.7 100

26. “I butted into conversations” (dichotomous, v2_mss_itm26)

v2_mss_recode(v2_clin$v2_mss_s2_mss26_einmischen,v2_con$v2_mss_s2_mss26,"v2_mss_itm26")
##                N    Y   <NA>     
## [1,] No. cases 874  59  610  1543
## [2,] Percent   56.6 3.8 39.5 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v2_mss_itm27)

v2_mss_recode(v2_clin$v2_mss_s2_mss27_red_pausenlos,v2_con$v2_mss_s2_mss27,"v2_mss_itm27")
##                N    Y   <NA>     
## [1,] No. cases 906  25  612  1543
## [2,] Percent   58.7 1.6 39.7 100

28. “I enjoyed being the centre of attention” (dichotomous, v2_mss_itm28)

v2_mss_recode(v2_clin$v2_mss_s2_mss28_mittelpunkt,v2_con$v2_mss_s2_mss28,"v2_mss_itm28")
##                N    Y   <NA>     
## [1,] No. cases 884  48  611  1543
## [2,] Percent   57.3 3.1 39.6 100

29. “I liked to joke and laugh” (dichotomous, v2_mss_itm29)

v2_mss_recode(v2_clin$v2_mss_s2_mss29_herumalbern,v2_con$v2_mss_s2_mss29,"v2_mss_itm29")
##                N   Y   <NA>     
## [1,] No. cases 803 126 614  1543
## [2,] Percent   52  8.2 39.8 100

30. “People found me entertaining” (dichotomous, v2_mss_itm30)

v2_mss_recode(v2_clin$v2_mss_s2_mss30_unterhaltsamer,v2_con$v2_mss_s2_mss30,"v2_mss_itm30")
##                N    Y   <NA>     
## [1,] No. cases 857  71  615  1543
## [2,] Percent   55.5 4.6 39.9 100

31. “I felt as if I was on top of the world” (dichotomous, v2_mss_itm31)

v2_mss_recode(v2_clin$v2_mss_s2_mss31_obenauf,v2_con$v2_mss_s2_mss31,"v2_mss_itm31")
##                N    Y   <NA>     
## [1,] No. cases 855  75  613  1543
## [2,] Percent   55.4 4.9 39.7 100

32. “I was more cheerful than my usual self” (dichotomous, v2_mss_itm32)

v2_mss_recode(v2_clin$v2_mss_s2_mss32_froehlicher,v2_con$v2_mss_s2_mss32,"v2_mss_itm32")
##                N    Y    <NA>     
## [1,] No. cases 764  167  612  1543
## [2,] Percent   49.5 10.8 39.7 100

33. “Other people got on my nerves” (dichotomous, v2_mss_itm33)

v2_mss_recode(v2_clin$v2_mss_s2_mss33_ungeduldiger,v2_con$v2_mss_s2_mss33,"v2_mss_itm33")
##                N    Y    <NA>     
## [1,] No. cases 728  202  613  1543
## [2,] Percent   47.2 13.1 39.7 100

34. “I was getting into arguments” (dichotomous, v2_mss_itm34)

v2_mss_recode(v2_clin$v2_mss_s2_mss34_streiten,v2_con$v2_mss_s2_mss34,"v2_mss_itm34")
##                N    Y   <NA>     
## [1,] No. cases 863  65  615  1543
## [2,] Percent   55.9 4.2 39.9 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v2_mss_itm35)

v2_mss_recode(v2_clin$v2_mss_s2_mss35_ideen,v2_con$v2_mss_s2_mss35,"v2_mss_itm35")
##                N    Y    <NA>     
## [1,] No. cases 765  167  611  1543
## [2,] Percent   49.6 10.8 39.6 100

36. “My thoughts raced through my mind” (dichotomous, v2_mss_itm36)

v2_mss_recode(v2_clin$v2_mss_s2_mss36_gedanken,v2_con$v2_mss_s2_mss36,"v2_mss_itm36")
##                N    Y    <NA>     
## [1,] No. cases 697  234  612  1543
## [2,] Percent   45.2 15.2 39.7 100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v2_mss_itm37)

v2_mss_recode(v2_clin$v2_mss_s2_mss37_konzentration,v2_con$v2_mss_s2_mss37,"v2_mss_itm37")
##                N    Y   <NA>     
## [1,] No. cases 796  136 611  1543
## [2,] Percent   51.6 8.8 39.6 100

38. “I thought I was an especially important person” (dichotomous, v2_mss_itm38)

v2_mss_recode(v2_clin$v2_mss_s2_mss38_etw_besonderes,v2_con$v2_mss_s2_mss38,"v2_mss_itm38")
##                N    Y   <NA>     
## [1,] No. cases 870  57  616  1543
## [2,] Percent   56.4 3.7 39.9 100

39. “I thought I could change the world” (dichotomous, v2_mss_itm39)

v2_mss_recode(v2_clin$v2_mss_s2_mss39_welt_veraender,v2_con$v2_mss_s2_mss39,"v2_mss_itm39")
##                N   Y   <NA>     
## [1,] No. cases 880 52  611  1543
## [2,] Percent   57  3.4 39.6 100

40. “I thought I was right most of the time” (dichotomous, v2_mss_itm40)

v2_mss_recode(v2_clin$v2_mss_s2_mss40_recht_haben,v2_con$v2_mss_s2_mss40,"v2_mss_itm40")
##                N    Y   <NA>     
## [1,] No. cases 892  41  610  1543
## [2,] Percent   57.8 2.7 39.5 100

41. “I thought I was superior to others” (dichotomous, v2_mss_itm41)

v2_mss_recode(v2_clin$v2_mss_s3_mss41_ueberlegen,v2_con$v2_mss_s3_mss41,"v2_mss_itm41")
##                N    Y   <NA>     
## [1,] No. cases 905  29  609  1543
## [2,] Percent   58.7 1.9 39.5 100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v2_mss_itm42)

v2_mss_recode(v2_clin$v2_mss_s3_mss42_uebermut,v2_con$v2_mss_s3_mss42,"v2_mss_itm42")
##                N    Y   <NA>     
## [1,] No. cases 870  65  608  1543
## [2,] Percent   56.4 4.2 39.4 100

43. “I thought I knew what other people were thinking” (dichotomous, v2_mss_itm43)

v2_mss_recode(v2_clin$v2_mss_s3_mss43_ged_lesen_akt,v2_con$v2_mss_s3_mss43,"v2_mss_itm43")
##                N   Y   <NA>     
## [1,] No. cases 849 84  610  1543
## [2,] Percent   55  5.4 39.5 100

44. “I thought other people knew what I was thinking” (dichotomous, v2_mss_itm44)

v2_mss_recode(v2_clin$v2_mss_s3_mss44_ged_lesen_pas,v2_con$v2_mss_s3_mss44,"v2_mss_itm44")
##                N    Y   <NA>     
## [1,] No. cases 876  58  609  1543
## [2,] Percent   56.8 3.8 39.5 100

45. “I thought someone wanted to harm me” (dichotomous, v2_mss_itm45)

v2_mss_recode(v2_clin$v2_mss_s3_mss45_etw_antun,v2_con$v2_mss_s3_mss45,"v2_mss_itm45")
##                N    Y   <NA>     
## [1,] No. cases 886  49  608  1543
## [2,] Percent   57.4 3.2 39.4 100

46. “I heard voices when people weren’t there” (dichotomous, v2_mss_itm46)

v2_mss_recode(v2_clin$v2_mss_s3_mss46_stimmen,v2_con$v2_mss_s3_mss46,"v2_mss_itm46")
##                N    Y  <NA>     
## [1,] No. cases 871  61 611  1543
## [2,] Percent   56.4 4  39.6 100

47. “I had false beliefs concerning who I was” (dichotomous, v2_mss_itm47)

v2_mss_recode(v2_clin$v2_mss_s3_mss47_jmd_anders,v2_con$v2_mss_s3_mss47,"v2_mss_itm47")
##                N   Y   <NA>     
## [1,] No. cases 911 24  608  1543
## [2,] Percent   59  1.6 39.4 100

48. “I knew I was getting ill” (dichotomous, v2_mss_itm48)

v2_mss_recode(v2_clin$v2_mss_s3_mss48_krank_einsicht,v2_con$v2_mss_s3_mss48,"v2_mss_itm48")
##                N    Y   <NA>     
## [1,] No. cases 813  111 619  1543
## [2,] Percent   52.7 7.2 40.1 100

Create MSS sum score (continuous [0-48],v2_mss_sum)

v2_mss_sum<-ifelse(v2_mss_itm1=="Y",1,0)+
            ifelse(v2_mss_itm2=="Y",1,0)+
            ifelse(v2_mss_itm3=="Y",1,0)+
            ifelse(v2_mss_itm4=="Y",1,0)+
            ifelse(v2_mss_itm5=="Y",1,0)+
            ifelse(v2_mss_itm6=="Y",1,0)+
            ifelse(v2_mss_itm7=="Y",1,0)+
            ifelse(v2_mss_itm8=="Y",1,0)+
            ifelse(v2_mss_itm9=="Y",1,0)+
            ifelse(v2_mss_itm10=="Y",1,0)+
            ifelse(v2_mss_itm11=="Y",1,0)+
            ifelse(v2_mss_itm12=="Y",1,0)+
            ifelse(v2_mss_itm13=="Y",1,0)+
            ifelse(v2_mss_itm14=="Y",1,0)+
            ifelse(v2_mss_itm15=="Y",1,0)+
            ifelse(v2_mss_itm16=="Y",1,0)+
            ifelse(v2_mss_itm17=="Y",1,0)+
            ifelse(v2_mss_itm18=="Y",1,0)+
            ifelse(v2_mss_itm19=="Y",1,0)+
            ifelse(v2_mss_itm20=="Y",1,0)+
            ifelse(v2_mss_itm21=="Y",1,0)+
            ifelse(v2_mss_itm22=="Y",1,0)+
            ifelse(v2_mss_itm23=="Y",1,0)+
            ifelse(v2_mss_itm24=="Y",1,0)+
            ifelse(v2_mss_itm25=="Y",1,0)+
            ifelse(v2_mss_itm26=="Y",1,0)+
            ifelse(v2_mss_itm27=="Y",1,0)+
            ifelse(v2_mss_itm28=="Y",1,0)+
            ifelse(v2_mss_itm29=="Y",1,0)+
            ifelse(v2_mss_itm30=="Y",1,0)+
            ifelse(v2_mss_itm31=="Y",1,0)+
            ifelse(v2_mss_itm32=="Y",1,0)+
            ifelse(v2_mss_itm33=="Y",1,0)+
            ifelse(v2_mss_itm34=="Y",1,0)+
            ifelse(v2_mss_itm35=="Y",1,0)+
            ifelse(v2_mss_itm36=="Y",1,0)+
            ifelse(v2_mss_itm37=="Y",1,0)+
            ifelse(v2_mss_itm38=="Y",1,0)+
            ifelse(v2_mss_itm39=="Y",1,0)+
            ifelse(v2_mss_itm40=="Y",1,0)+
            ifelse(v2_mss_itm41=="Y",1,0)+
            ifelse(v2_mss_itm42=="Y",1,0)+
            ifelse(v2_mss_itm43=="Y",1,0)+
            ifelse(v2_mss_itm44=="Y",1,0)+
            ifelse(v2_mss_itm45=="Y",1,0)+
            ifelse(v2_mss_itm46=="Y",1,0)+
            ifelse(v2_mss_itm47=="Y",1,0)+
            ifelse(v2_mss_itm48=="Y",1,0)

summary(v2_mss_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.500   2.000   4.487   7.000  37.000     720

Create dataset

v2_mss<-data.frame(v2_mss_itm1,v2_mss_itm2,v2_mss_itm3,v2_mss_itm4,v2_mss_itm5,v2_mss_itm6,
                   v2_mss_itm7,v2_mss_itm8,v2_mss_itm9,v2_mss_itm10,v2_mss_itm11,
                   v2_mss_itm12,v2_mss_itm13,v2_mss_itm14,v2_mss_itm15,v2_mss_itm16,
                   v2_mss_itm17,v2_mss_itm18,v2_mss_itm19,v2_mss_itm20,v2_mss_itm21,
                   v2_mss_itm22,v2_mss_itm23,v2_mss_itm24,v2_mss_itm25,v2_mss_itm26,
                   v2_mss_itm27,v2_mss_itm28,v2_mss_itm29,v2_mss_itm30,v2_mss_itm31,
                   v2_mss_itm32,v2_mss_itm33,v2_mss_itm34,v2_mss_itm35,v2_mss_itm36,
                   v2_mss_itm37,v2_mss_itm38,v2_mss_itm39,v2_mss_itm40,v2_mss_itm41,
                   v2_mss_itm42,v2_mss_itm43,v2_mss_itm44,v2_mss_itm45,v2_mss_itm46,
                   v2_mss_itm47,v2_mss_itm48,v2_mss_sum)

LEQ

For explanation, please refer to the section in Visit 1

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v2_leq_A_1A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq1a_schw_krankh,v2_con$v2_leq_a_leq1a,"v2_leq_A_1A")
##                -999 bad  good <NA>     
## [1,] No. cases 628  235  40   640  1543
## [2,] Percent   40.7 15.2 2.6  41.5 100

1B Impact (ordinal [0,1,2,3], v2_leq_A_1B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq1e_schw_krankh,v2_con$v2_leq_a_leq1e,"v2_leq_A_1B")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 626  15 37  76  149 640  1543
## [2,] Percent   40.6 1  2.4 4.9 9.7 41.5 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v2_leq_A_2A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq2a_ernaehrung,v2_con$v2_leq_a_leq2a,"v2_leq_A_2A")
##                -999 bad good <NA>     
## [1,] No. cases 636  136 131  640  1543
## [2,] Percent   41.2 8.8 8.5  41.5 100

2B Impact (ordinal [0,1,2,3], v2_leq_A_2B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq2e_ernaehrung,v2_con$v2_leq_a_leq2e,"v2_leq_A_2B")
##                -999 0  1  2   3   <NA>     
## [1,] No. cases 631  16 62 112 82  640  1543
## [2,] Percent   40.9 1  4  7.3 5.3 41.5 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v2_leq_A_3A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq3a_schlaf,v2_con$v2_leq_a_leq3a,"v2_leq_A_3A")
##                -999 bad  good <NA>     
## [1,] No. cases 637  175  91   640  1543
## [2,] Percent   41.3 11.3 5.9  41.5 100

3B Impact (ordinal [0,1,2,3], v2_leq_A_3B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq3e_schlaf,v2_con$v2_leq_a_leq3e,"v2_leq_A_3B")
##                -999 0  1   2   3  <NA>     
## [1,] No. cases 634  15 59  103 92 640  1543
## [2,] Percent   41.1 1  3.8 6.7 6  41.5 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v2_leq_A_4A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq4a_freizeit,v2_con$v2_leq_a_leq4a,"v2_leq_A_4A")
##                -999 bad good <NA>     
## [1,] No. cases 572  125 206  640  1543
## [2,] Percent   37.1 8.1 13.4 41.5 100

4B Impact (ordinal [0,1,2,3], v2_leq_A_4B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq4e_freizeit,v2_con$v2_leq_a_leq4e,"v2_leq_A_4B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 568  17  70  137 111 640  1543
## [2,] Percent   36.8 1.1 4.5 8.9 7.2 41.5 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v2_leq_A_5A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq5a_zahnarzt,v2_con$v2_leq_a_leq5a,"v2_leq_A_5A")
##                -999 bad good <NA>     
## [1,] No. cases 767  52  84   640  1543
## [2,] Percent   49.7 3.4 5.4  41.5 100

5B Impact (ordinal [0,1,2,3], v2_leq_A_5B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq5e_zahnarzt,v2_con$v2_leq_a_leq5e,"v2_leq_A_5B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 764  30  42  33  34  640  1543
## [2,] Percent   49.5 1.9 2.7 2.1 2.2 41.5 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v2_leq_A_6A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq6a_schwanger,v2_con$v2_leq_a_leq6a,"v2_leq_A_6A")
##                -999 bad good <NA>     
## [1,] No. cases 894  2   7    640  1543
## [2,] Percent   57.9 0.1 0.5  41.5 100

6B Impact (ordinal [0,1,2,3], v2_leq_A_6B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq6e_schwanger,v2_con$v2_leq_a_leq6e,"v2_leq_A_6B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 894  2   2   5   640  1543
## [2,] Percent   57.9 0.1 0.1 0.3 41.5 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v2_leq_A_7A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq7a_fehlg_abtr,v2_con$v2_leq_a_leq7a,"v2_leq_A_7A")
##                -999 bad <NA>     
## [1,] No. cases 899  4   640  1543
## [2,] Percent   58.3 0.3 41.5 100

7B Impact (ordinal [0,1,2,3], v2_leq_A_7B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq7e_fehlg_abtr,v2_con$v2_leq_a_leq7e,"v2_leq_A_7B")
##                -999 0   3   <NA>     
## [1,] No. cases 898  2   3   640  1543
## [2,] Percent   58.2 0.1 0.2 41.5 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v2_leq_A_8A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq8a_wechseljahre,v2_con$v2_leq_a_leq8a,"v2_leq_A_8A")
##                -999 bad good <NA>     
## [1,] No. cases 874  24  5    640  1543
## [2,] Percent   56.6 1.6 0.3  41.5 100

8B Impact (ordinal [0,1,2,3], v2_leq_A_8B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq8e_wechseljahre,v2_con$v2_leq_a_leq8e,"v2_leq_A_8B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 872  2   7   16 6   640  1543
## [2,] Percent   56.5 0.1 0.5 1  0.4 41.5 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v2_leq_A_9A)

v2_leq_a_recode(v2_clin$v2_leq_a_leq9a_verhuetung,v2_con$v2_leq_a_leq9a,"v2_leq_A_9A")
##                -999 bad good <NA>     
## [1,] No. cases 886  14  3    640  1543
## [2,] Percent   57.4 0.9 0.2  41.5 100

9B Impact (ordinal [0,1,2,3], v2_leq_A_9B)

v2_leq_b_recode(v2_clin$v2_leq_a_leq9e_verhuetung,v2_con$v2_leq_a_leq9e,"v2_leq_A_9B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 885  6   4   1   7   640  1543
## [2,] Percent   57.4 0.4 0.3 0.1 0.5 41.5 100

Create dataset

v2_leq_A<-data.frame(v2_leq_A_1A,v2_leq_A_1B,v2_leq_A_2A,v2_leq_A_2B,v2_leq_A_3A,
                     v2_leq_A_3B,v2_leq_A_4A,v2_leq_A_4B,v2_leq_A_5A,v2_leq_A_5B,
                     v2_leq_A_6A,v2_leq_A_6B,v2_leq_A_7A,v2_leq_A_7B,v2_leq_A_8A,
                     v2_leq_A_8B,v2_leq_A_9A,v2_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v2_leq_B_10A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq10a_arbeitssuche,v2_con$v2_leq_b_leq10a,"v2_leq_B_10A")
##                -999 bad good <NA>     
## [1,] No. cases 755  119 29   640  1543
## [2,] Percent   48.9 7.7 1.9  41.5 100

10B Impact (ordinal [0,1,2,3], v2_leq_B_10B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq10e_arbeitssuche,v2_con$v2_leq_b_leq10e,"v2_leq_B_10B")
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 753  12  35  41  62 640  1543
## [2,] Percent   48.8 0.8 2.3 2.7 4  41.5 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v2_leq_B_11A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq11a_arbeit_aussen,v2_con$v2_leq_b_leq11a,"v2_leq_B_11A")
##                -999 bad good <NA>     
## [1,] No. cases 769  26  108  640  1543
## [2,] Percent   49.8 1.7 7    41.5 100

11B Impact (ordinal [0,1,2,3], v2_leq_B_11B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq11e_arbeit_aussen,v2_con$v2_leq_b_leq11e,"v2_leq_B_11B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 767  8   26  49  53  640  1543
## [2,] Percent   49.7 0.5 1.7 3.2 3.4 41.5 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v2_leq_B_12A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq12a_arbeitswechs,v2_con$v2_leq_b_leq12a,"v2_leq_B_12A")
##                -999 bad good <NA>     
## [1,] No. cases 785  21  97   640  1543
## [2,] Percent   50.9 1.4 6.3  41.5 100

12B Impact (ordinal [0,1,2,3], v2_leq_B_12B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq12e_arbeitswechs,v2_con$v2_leq_b_leq12e,"v2_leq_B_12B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 783  5   21  39  55  640  1543
## [2,] Percent   50.7 0.3 1.4 2.5 3.6 41.5 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v2_leq_B_13A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq13a_veraend_arb,v2_con$v2_leq_b_leq13a,"v2_leq_B_13A")
##                -999 bad good <NA>     
## [1,] No. cases 733  46  124  640  1543
## [2,] Percent   47.5 3   8    41.5 100

13B Impact (ordinal [0,1,2,3], v2_leq_B_13B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq13e_veraend_arb,v2_con$v2_leq_b_leq13e,"v2_leq_B_13B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 731  7   43  69  53  640  1543
## [2,] Percent   47.4 0.5 2.8 4.5 3.4 41.5 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v2_leq_B_14A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq14a_veraend_ba,v2_con$v2_leq_b_leq14a,"v2_leq_B_14A")
##                -999 bad good <NA>     
## [1,] No. cases 743  40  120  640  1543
## [2,] Percent   48.2 2.6 7.8  41.5 100

14B Impact (ordinal [0,1,2,3], v2_leq_B_14B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq14e_veraend_ba,v2_con$v2_leq_b_leq14e,"v2_leq_B_14B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 742  8   41  63  49  640  1543
## [2,] Percent   48.1 0.5 2.7 4.1 3.2 41.5 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v2_leq_B_15A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq15a_schw_arbeit,v2_con$v2_leq_b_leq15a,"v2_leq_B_15A")
##                -999 bad good <NA>     
## [1,] No. cases 797  88  18   640  1543
## [2,] Percent   51.7 5.7 1.2  41.5 100

15B Impact (ordinal [0,1,2,3], v2_leq_B_15B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq15e_schw_arbeit,v2_con$v2_leq_b_leq15e,"v2_leq_B_15B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 795  10  40  32  26  640  1543
## [2,] Percent   51.5 0.6 2.6 2.1 1.7 41.5 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v2_leq_B_16A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq16a_betr_reorg,v2_con$v2_leq_b_leq16a,"v2_leq_B_16A")
##                -999 bad good <NA>     
## [1,] No. cases 860  23  20   640  1543
## [2,] Percent   55.7 1.5 1.3  41.5 100

16B Impact (ordinal [0,1,2,3], v2_leq_B_16B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq16e_betr_reorg,v2_con$v2_leq_b_leq16e,"v2_leq_B_16B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 859  8   12  13  11  640  1543
## [2,] Percent   55.7 0.5 0.8 0.8 0.7 41.5 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v2_leq_B_17A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq17a_kuendigung,v2_con$v2_leq_b_leq17a,"v2_leq_B_17A")
##                -999 bad good <NA>     
## [1,] No. cases 850  31  22   640  1543
## [2,] Percent   55.1 2   1.4  41.5 100

17B Impact (ordinal [0,1,2,3], v2_leq_B_17B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq17e_kuendigung,v2_con$v2_leq_b_leq17e,"v2_leq_B_17B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 849  5   9   14  26  640  1543
## [2,] Percent   55   0.3 0.6 0.9 1.7 41.5 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v2_leq_B_18A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq18a_ende_beruf,v2_con$v2_leq_b_leq18a,"v2_leq_B_18A")
##                -999 bad good <NA>     
## [1,] No. cases 866  18  19   640  1543
## [2,] Percent   56.1 1.2 1.2  41.5 100

18B Impact (ordinal [0,1,2,3], v2_leq_B_18B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq18e_ende_beruf,v2_con$v2_leq_b_leq18e,"v2_leq_B_18B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 864  5   7   6   21  640  1543
## [2,] Percent   56   0.3 0.5 0.4 1.4 41.5 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v2_leq_B_19A)

v2_leq_a_recode(v2_clin$v2_leq_b_leq19a_fortbildung,v2_con$v2_leq_b_leq19a,"v2_leq_B_19A")
##                -999 bad good <NA>     
## [1,] No. cases 845  12  46   640  1543
## [2,] Percent   54.8 0.8 3    41.5 100

19B Impact (ordinal [0,1,2,3], v2_leq_B_19B)

v2_leq_b_recode(v2_clin$v2_leq_b_leq19e_fortbildung,v2_con$v2_leq_b_leq19e,"v2_leq_B_19B")
##                -999 0   1  2   3  <NA>     
## [1,] No. cases 842  4   15 26  16 640  1543
## [2,] Percent   54.6 0.3 1  1.7 1  41.5 100
v2_leq_B<-data.frame(v2_leq_B_10A,v2_leq_B_10B,v2_leq_B_11A,v2_leq_B_11B,v2_leq_B_12A,
                     v2_leq_B_12B,v2_leq_B_13A,v2_leq_B_13B,v2_leq_B_14A,v2_leq_B_14B,
                     v2_leq_B_15A,v2_leq_B_15B,v2_leq_B_16A,v2_leq_B_16B,v2_leq_B_17A,
                     v2_leq_B_17B,v2_leq_B_18A,v2_leq_B_18B,v2_leq_B_19A,v2_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v2_leq_C_20A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq20a_beginn_ende,v2_con$v2_leq_c_d_leq20a,"v2_leq_C_20A")
##                -999 bad good <NA>     
## [1,] No. cases 842  8   53   640  1543
## [2,] Percent   54.6 0.5 3.4  41.5 100

20B Impact (ordinal [0,1,2,3], v2_leq_C_20B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq20e_beginn_ende,v2_con$v2_leq_c_d_leq20e,"v2_leq_C_20B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 841  5   6   23  28  640  1543
## [2,] Percent   54.5 0.3 0.4 1.5 1.8 41.5 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v2_leq_C_21A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq21a_schulwechsel,v2_con$v2_leq_c_d_leq21a,"v2_leq_C_21A")
##                -999 bad good <NA>     
## [1,] No. cases 889  4   10   640  1543
## [2,] Percent   57.6 0.3 0.6  41.5 100

21B Impact (ordinal [0,1,2,3], v2_leq_C_21B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq21e_schulwechsel,v2_con$v2_leq_c_d_leq21e,"v2_leq_C_21B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 888  4   1   9   1   640  1543
## [2,] Percent   57.6 0.3 0.1 0.6 0.1 41.5 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v2_leq_C_22A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq22a_aend_karriere,v2_con$v2_leq_c_d_leq22a,"v2_leq_C_22A")
##                -999 bad good <NA>     
## [1,] No. cases 866  8   29   640  1543
## [2,] Percent   56.1 0.5 1.9  41.5 100

B Impact (ordinal [0,1,2,3], v2_leq_C_22B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq22e_aend_karriere,v2_con$v2_leq_c_d_leq22e,"v2_leq_C_22B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 865  3   6   17  12  640  1543
## [2,] Percent   56.1 0.2 0.4 1.1 0.8 41.5 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v2_leq_C_23A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq23a_schulprob,v2_con$v2_leq_c_d_leq23a,"v2_leq_C_23A")
##                -999 bad good <NA>     
## [1,] No. cases 876  25  2    640  1543
## [2,] Percent   56.8 1.6 0.1  41.5 100

23B Impact (ordinal [0,1,2,3], v2_leq_C_23B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq23e_schulprob,v2_con$v2_leq_c_d_leq23e,"v2_leq_C_23B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 875  1   10  10  7   640  1543
## [2,] Percent   56.7 0.1 0.6 0.6 0.5 41.5 100

Create dataset

v2_leq_C<-data.frame(v2_leq_C_20A,v2_leq_C_20B,v2_leq_C_21A,v2_leq_C_21B,v2_leq_C_22A,v2_leq_C_22B,v2_leq_C_23A,v2_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v2_leq_D_24A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq24a_schw_wsuche,v2_con$v2_leq_c_d_leq24a,"v2_leq_D_24A")
##                -999 bad good <NA>     
## [1,] No. cases 829  58  16   640  1543
## [2,] Percent   53.7 3.8 1    41.5 100

24B Impact (ordinal [0,1,2,3], v2_leq_D_24B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq24e_schw_wsuche,v2_con$v2_leq_c_d_leq24e,"v2_leq_D_24B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 828  4   20  15 36  640  1543
## [2,] Percent   53.7 0.3 1.3 1  2.3 41.5 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v2_leq_D_25A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq25a_umzug_nah,v2_con$v2_leq_c_d_leq25a,"v2_leq_D_25A")
##                -999 bad good <NA>     
## [1,] No. cases 841  15  47   640  1543
## [2,] Percent   54.5 1   3    41.5 100

B Impact (ordinal [0,1,2,3], v2_leq_D_25B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq25e_umzug_nah,v2_con$v2_leq_c_d_leq25e,"v2_leq_D_25B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 840  3   9   14  37  640  1543
## [2,] Percent   54.4 0.2 0.6 0.9 2.4 41.5 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v2_leq_D_26A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq26a_umzug_fern,v2_con$v2_leq_c_d_leq26a,"v2_leq_D_26A")
##                -999 bad good <NA>     
## [1,] No. cases 859  9   35   640  1543
## [2,] Percent   55.7 0.6 2.3  41.5 100

26B Impact (ordinal [0,1,2,3], v2_leq_D_26B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq26e_umzug_fern,v2_con$v2_leq_c_d_leq26e,"v2_leq_D_26B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 858  4   4   13  24  640  1543
## [2,] Percent   55.6 0.3 0.3 0.8 1.6 41.5 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v2_leq_D_27A)

v2_leq_a_recode(v2_clin$v2_leq_c_d_leq27a_veraend_lu,v2_con$v2_leq_c_d_leq27a,"v2_leq_D_27A")
##                -999 bad good <NA>     
## [1,] No. cases 757  46  100  640  1543
## [2,] Percent   49.1 3   6.5  41.5 100

27B Impact (ordinal [0,1,2,3], v2_leq_D_27B)

v2_leq_b_recode(v2_clin$v2_leq_c_d_leq27e_veraend_lu,v2_con$v2_leq_c_d_leq27e,"v2_leq_D_27B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 756  8   32  50  57  640  1543
## [2,] Percent   49   0.5 2.1 3.2 3.7 41.5 100

Create dataset

v2_leq_D<-data.frame(v2_leq_D_24A,v2_leq_D_24B,v2_leq_D_25A,v2_leq_D_25B,v2_leq_D_26A,
                     v2_leq_D_26B,v2_leq_D_27A,v2_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v2_leq_E_28A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq28a_neue_bez,v2_con$v2_leq_e_leq28a,"v2_leq_E_28A")
##                -999 bad good <NA>     
## [1,] No. cases 818  10  75   640  1543
## [2,] Percent   53   0.6 4.9  41.5 100

28B Impact (ordinal [0,1,2,3], v2_leq_E_28B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq28e_neue_bez,v2_con$v2_leq_e_leq28e,"v2_leq_E_28B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 817  4   8   30  44  640  1543
## [2,] Percent   52.9 0.3 0.5 1.9 2.9 41.5 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v2_leq_E_29A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq29a_verlobung,v2_con$v2_leq_e_leq29a,"v2_leq_E_29A")
##                -999 bad good <NA>     
## [1,] No. cases 889  4   10   640  1543
## [2,] Percent   57.6 0.3 0.6  41.5 100

29B Impact (ordinal [0,1,2,3], v2_leq_E_29B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq29e_verlobung,v2_con$v2_leq_e_leq29e,"v2_leq_E_29B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 888  4   4   4   3   640  1543
## [2,] Percent   57.6 0.3 0.3 0.3 0.2 41.5 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v2_leq_E_30A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq30a_prob_partner,v2_con$v2_leq_e_leq30a,"v2_leq_E_30A")
##                -999 bad good <NA>     
## [1,] No. cases 809  83  11   640  1543
## [2,] Percent   52.4 5.4 0.7  41.5 100

30B Impact (ordinal [0,1,2,3], v2_leq_E_30B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq30e_prob_partner,v2_con$v2_leq_e_leq30e,"v2_leq_E_30B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 808  4   29  33  29  640  1543
## [2,] Percent   52.4 0.3 1.9 2.1 1.9 41.5 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v2_leq_E_31A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq31a_trennung,v2_con$v2_leq_e_leq31a,"v2_leq_E_31A")
##                -999 bad good <NA>     
## [1,] No. cases 858  31  14   640  1543
## [2,] Percent   55.6 2   0.9  41.5 100

31B Impact (ordinal [0,1,2,3], v2_leq_E_31B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq31e_trennung,v2_con$v2_leq_e_leq31e,"v2_leq_E_31B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 858  2   5   13  25  640  1543
## [2,] Percent   55.6 0.1 0.3 0.8 1.6 41.5 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v2_leq_E_32A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq32a_schwanger_p,v2_con$v2_leq_e_leq32a,"v2_leq_E_32A")
##                -999 bad good <NA>     
## [1,] No. cases 893  2   8    640  1543
## [2,] Percent   57.9 0.1 0.5  41.5 100

32B Impact (ordinal [0,1,2,3], v2_leq_E_32B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq32e_schwanger_p,v2_con$v2_leq_e_leq32e,"v2_leq_E_32B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 893  3   2   2   3   640  1543
## [2,] Percent   57.9 0.2 0.1 0.1 0.2 41.5 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v2_leq_E_33A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq33a_fehlg_abtr_p,v2_con$v2_leq_e_leq33a,"v2_leq_E_33A")
##                -999 bad <NA>     
## [1,] No. cases 902  1   640  1543
## [2,] Percent   58.5 0.1 41.5 100

33B Impact (ordinal [0,1,2,3], v2_leq_E_33B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq33e_fehlg_abtr_p,v2_con$v2_leq_e_leq33e,"v2_leq_E_33B")
##                -999 0   <NA>     
## [1,] No. cases 902  1   640  1543
## [2,] Percent   58.5 0.1 41.5 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v2_leq_E_34A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq34a_heirat,v2_con$v2_leq_e_leq34a,"v2_leq_E_34A")
##                -999 bad good <NA>     
## [1,] No. cases 888  2   13   640  1543
## [2,] Percent   57.6 0.1 0.8  41.5 100

34B Impact (ordinal [0,1,2,3], v2_leq_E_34B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq34e_heirat,v2_con$v2_leq_e_leq34e,"v2_leq_E_34B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 887  2   5   1   8   640  1543
## [2,] Percent   57.5 0.1 0.3 0.1 0.5 41.5 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v2_leq_E_35A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq35a_veraend_naehe,v2_con$v2_leq_e_leq35a,"v2_leq_E_35A")
##                -999 bad good <NA>     
## [1,] No. cases 791  43  69   640  1543
## [2,] Percent   51.3 2.8 4.5  41.5 100

35B Impact (ordinal [0,1,2,3], v2_leq_E_35B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq35e_veraend_naehe,v2_con$v2_leq_e_leq35e,"v2_leq_E_35B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 790  3   24  44  42  640  1543
## [2,] Percent   51.2 0.2 1.6 2.9 2.7 41.5 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v2_leq_E_36A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq36a_untreue,v2_con$v2_leq_e_leq36a,"v2_leq_E_36A")
##                -999 bad good <NA>     
## [1,] No. cases 878  19  6    640  1543
## [2,] Percent   56.9 1.2 0.4  41.5 100

36B Impact (ordinal [0,1,2,3], v2_leq_E_36B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq36e_untreue,v2_con$v2_leq_e_leq36e,"v2_leq_E_36B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 877  3   7   5   11  640  1543
## [2,] Percent   56.8 0.2 0.5 0.3 0.7 41.5 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v2_leq_E_37A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq37a_konf_schwiege,v2_con$v2_leq_e_leq37a,"v2_leq_E_37A")
##                -999 bad good <NA>     
## [1,] No. cases 880  21  2    640  1543
## [2,] Percent   57   1.4 0.1  41.5 100

37B Impact (ordinal [0,1,2,3], v2_leq_E_37B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq37e_konf_schwiege,v2_con$v2_leq_e_leq37e,"v2_leq_E_37B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 879  4   9   6   5   640  1543
## [2,] Percent   57   0.3 0.6 0.4 0.3 41.5 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v2_leq_E_38A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq38a_trennung_str,v2_con$v2_leq_e_leq38a,"v2_leq_E_38A")
##                -999 bad good <NA>     
## [1,] No. cases 881  14  8    640  1543
## [2,] Percent   57.1 0.9 0.5  41.5 100

38B Impact (ordinal [0,1,2,3], v2_leq_E_38B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq38e_trennung_str,v2_con$v2_leq_e_leq38e,"v2_leq_E_38B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 880  4   5   3   11  640  1543
## [2,] Percent   57   0.3 0.3 0.2 0.7 41.5 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v2_leq_E_39A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq39a_trennung_ber,v2_con$v2_leq_e_leq39a,"v2_leq_E_39A")
##                -999 bad good <NA>     
## [1,] No. cases 895  7   1    640  1543
## [2,] Percent   58   0.5 0.1  41.5 100

39B Impact (ordinal [0,1,2,3], v2_leq_E_39B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq39e_trennung_ber,v2_con$v2_leq_e_leq39e,"v2_leq_E_39B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 894  2   1   2   4   640  1543
## [2,] Percent   57.9 0.1 0.1 0.1 0.3 41.5 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v2_leq_E_40A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq40a_versoehnung,v2_con$v2_leq_e_leq40a,"v2_leq_E_40A")
##                -999 bad good <NA>     
## [1,] No. cases 878  2   23   640  1543
## [2,] Percent   56.9 0.1 1.5  41.5 100

40B Impact (ordinal [0,1,2,3], v2_leq_E_40B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq40a_versoehnung,v2_con$v2_leq_e_leq40e,"v2_leq_E_40B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 878  2   21  1   1   640  1543
## [2,] Percent   56.9 0.1 1.4 0.1 0.1 41.5 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v2_leq_E_41A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq41a_scheidung,v2_con$v2_leq_e_leq41a,"v2_leq_E_41A")
##                -999 bad good <NA>     
## [1,] No. cases 894  4   5    640  1543
## [2,] Percent   57.9 0.3 0.3  41.5 100

41B Impact (ordinal [0,1,2,3], v2_leq_E_41B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq41e_scheidung,v2_con$v2_leq_e_leq41e,"v2_leq_E_41B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 892  4   2   5   640  1543
## [2,] Percent   57.8 0.3 0.1 0.3 41.5 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v2_leq_E_42A)

v2_leq_a_recode(v2_clin$v2_leq_e_leq42a_veraend_taet,v2_con$v2_leq_e_leq42a,"v2_leq_E_42A")
##                -999 bad good <NA>     
## [1,] No. cases 863  12  28   640  1543
## [2,] Percent   55.9 0.8 1.8  41.5 100

42B Impact (ordinal [0,1,2,3], v2_leq_E_42B)

v2_leq_b_recode(v2_clin$v2_leq_e_leq42e_veraend_taet,v2_con$v2_leq_e_leq42e,"v2_leq_E_42B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 862  5   14  8   14  640  1543
## [2,] Percent   55.9 0.3 0.9 0.5 0.9 41.5 100

Create dataset

v2_leq_E<-data.frame(v2_leq_E_28A,v2_leq_E_28B,v2_leq_E_29A,v2_leq_E_29B,v2_leq_E_30A,
                     v2_leq_E_30B,v2_leq_E_31A,v2_leq_E_31B,v2_leq_E_32A,v2_leq_E_32B,
                     v2_leq_E_33A,v2_leq_E_33B,v2_leq_E_34A,v2_leq_E_34B,v2_leq_E_35A,
                     v2_leq_E_35B,v2_leq_E_36A,v2_leq_E_36B,v2_leq_E_37A,v2_leq_E_37B,
                     v2_leq_E_38A,v2_leq_E_38B,v2_leq_E_39A,v2_leq_E_39B,v2_leq_E_40A,
                     v2_leq_E_40B,v2_leq_E_41A,v2_leq_E_41B,v2_leq_E_42A,v2_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v2_leq_F_43A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq43a_neu_fmitglied,v2_con$v2_leq_f_g_leq43a,"v2_leq_F_43A")
##                -999 bad good <NA>     
## [1,] No. cases 844  1   58   640  1543
## [2,] Percent   54.7 0.1 3.8  41.5 100

43B Impact (ordinal [0,1,2,3], v2_leq_F_43B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq43e_neu_fmitglied,v2_con$v2_leq_f_g_leq43e,"v2_leq_F_43B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 843  10  14  17  19  640  1543
## [2,] Percent   54.6 0.6 0.9 1.1 1.2 41.5 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v2_leq_F_44A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq44a_auszug_fm,v2_con$v2_leq_f_g_leq44a,"v2_leq_F_44A")
##                -999 bad good <NA>     
## [1,] No. cases 868  19  16   640  1543
## [2,] Percent   56.3 1.2 1    41.5 100

44B Impact (ordinal [0,1,2,3], v2_leq_F_44B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq44e_auszug_fm,v2_con$v2_leq_f_g_leq44e,"v2_leq_F_44B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 867  7   7   15 7   640  1543
## [2,] Percent   56.2 0.5 0.5 1  0.5 41.5 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v2_leq_F_45A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq45a_gz_verh_fm,v2_con$v2_leq_f_g_leq45a,"v2_leq_F_45A")
##                -999 bad good <NA>     
## [1,] No. cases 771  126 6    640  1543
## [2,] Percent   50   8.2 0.4  41.5 100

45B Impact (ordinal [0,1,2,3], v2_leq_F_45B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq45e_gz_verh_fm,v2_con$v2_leq_f_g_leq45e,"v2_leq_F_45B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 771  5   27  59  41  640  1543
## [2,] Percent   50   0.3 1.7 3.8 2.7 41.5 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v2_leq_F_46A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq46a_tod_partner,v2_con$v2_leq_f_g_leq46a,"v2_leq_F_46A")
##                -999 bad <NA>     
## [1,] No. cases 896  7   640  1543
## [2,] Percent   58.1 0.5 41.5 100

46B Impact (ordinal [0,1,2,3], v2_leq_F_46B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq46e_tod_partner,v2_con$v2_leq_f_g_leq46e,"v2_leq_F_46B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 895  1   1   1   5   640  1543
## [2,] Percent   58   0.1 0.1 0.1 0.3 41.5 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v2_leq_F_47A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq47a_tod_kind,v2_con$v2_leq_f_g_leq47a,"v2_leq_F_47A")
##                -999 bad good <NA>     
## [1,] No. cases 897  5   1    640  1543
## [2,] Percent   58.1 0.3 0.1  41.5 100

47B Impact (ordinal [0,1,2,3], v2_leq_F_47B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq47e_tod_kind,v2_con$v2_leq_f_g_leq47e,"v2_leq_F_47B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 896  1   1   1   4   640  1543
## [2,] Percent   58.1 0.1 0.1 0.1 0.3 41.5 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v2_leq_F_48A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq48a_tod_fm_ef,v2_con$v2_leq_f_g_leq48a,"v2_leq_F_48A")
##                -999 bad good <NA>     
## [1,] No. cases 834  63  6    640  1543
## [2,] Percent   54.1 4.1 0.4  41.5 100

48B Impact (ordinal [0,1,2,3], v2_leq_F_48B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq48e_tod_fm_ef,v2_con$v2_leq_f_g_leq48e,"v2_leq_F_48B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 833  5   24  20  21  640  1543
## [2,] Percent   54   0.3 1.6 1.3 1.4 41.5 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v2_leq_F_49A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq49a_geb_enkel,v2_con$v2_leq_f_g_leq49a,"v2_leq_F_49A")
##                -999 bad good <NA>     
## [1,] No. cases 879  3   21   640  1543
## [2,] Percent   57   0.2 1.4  41.5 100

49B Impact (ordinal [0,1,2,3], v2_leq_F_49B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq49e_geb_enkel,v2_con$v2_leq_f_g_leq49e,"v2_leq_F_49B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 878  4   5   7   9   640  1543
## [2,] Percent   56.9 0.3 0.3 0.5 0.6 41.5 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v2_leq_F_50A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq50a_fstand_eltern,v2_con$v2_leq_f_g_leq50a,"v2_leq_F_50A")
##                -999 bad good <NA>     
## [1,] No. cases 891  5   7    640  1543
## [2,] Percent   57.7 0.3 0.5  41.5 100

50B Impact (ordinal [0,1,2,3], v2_leq_F_50B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq50e_fstand_eltern,v2_con$v2_leq_f_g_leq50e,"v2_leq_F_50B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 890  1   6   2   4   640  1543
## [2,] Percent   57.7 0.1 0.4 0.1 0.3 41.5 100

Create dataset

v2_leq_F<-data.frame(v2_leq_F_43A,v2_leq_F_43B,v2_leq_F_44A,v2_leq_F_44B,v2_leq_F_45A,
                     v2_leq_F_45B,v2_leq_F_46A,v2_leq_F_46B,v2_leq_F_47A,v2_leq_F_47B,
                     v2_leq_F_48A,v2_leq_F_48B,v2_leq_F_49A,v2_leq_F_49B,v2_leq_F_50A,
                     v2_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v2_leq_G_51A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq51a_kindbetr,v2_con$v2_leq_f_g_leq51a,"v2_leq_G_51A")
##                -999 bad good <NA>     
## [1,] No. cases 880  8   15   640  1543
## [2,] Percent   57   0.5 1    41.5 100

51B Impact (ordinal [0,1,2,3], v2_leq_G_51B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq51e_kindbetr,v2_con$v2_leq_f_g_leq51e,"v2_leq_G_51B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 879  2   5   9   8   640  1543
## [2,] Percent   57   0.1 0.3 0.6 0.5 41.5 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v2_leq_G_52A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq52a_konf_eschaft,v2_con$v2_leq_f_g_leq52a,"v2_leq_G_52A")
##                -999 bad good <NA>     
## [1,] No. cases 880  16  7    640  1543
## [2,] Percent   57   1   0.5  41.5 100

52B Impact (ordinal [0,1,2,3], v2_leq_G_52B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq52e_konf_eschaft,v2_con$v2_leq_f_g_leq52e,"v2_leq_G_52B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 879  3   5   8   8   640  1543
## [2,] Percent   57   0.2 0.3 0.5 0.5 41.5 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v2_leq_G_53A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq53a_konf_geltern,v2_con$v2_leq_f_g_leq53a,"v2_leq_G_53A")
##                -999 bad good <NA>     
## [1,] No. cases 895  6   2    640  1543
## [2,] Percent   58   0.4 0.1  41.5 100

53B Impact (ordinal [0,1,2,3], v2_leq_G_53B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq53e_konf_geltern,v2_con$v2_leq_f_g_leq53e,"v2_leq_G_53B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 894  3   3   2   1   640  1543
## [2,] Percent   57.9 0.2 0.2 0.1 0.1 41.5 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v2_leq_G_54A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq54a_alleinerz,v2_con$v2_leq_f_g_leq54a,"v2_leq_G_54A")
##                -999 bad good <NA>     
## [1,] No. cases 896  4   3    640  1543
## [2,] Percent   58.1 0.3 0.2  41.5 100

54B Impact (ordinal [0,1,2,3], v2_leq_G_54B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq54e_alleinerz,v2_con$v2_leq_f_g_leq54e,"v2_leq_G_54B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 895  2   1   3   2   640  1543
## [2,] Percent   58   0.1 0.1 0.2 0.1 41.5 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v2_leq_G_55A)

v2_leq_a_recode(v2_clin$v2_leq_f_g_leq55a_sorgerecht,v2_con$v2_leq_f_g_leq55a,"v2_leq_G_55A")
##                -999 bad good <NA>     
## [1,] No. cases 889  9   5    640  1543
## [2,] Percent   57.6 0.6 0.3  41.5 100

55B Impact (ordinal [0,1,2,3], v2_leq_G_55B)

v2_leq_b_recode(v2_clin$v2_leq_f_g_leq55e_sorgerecht,v2_con$v2_leq_f_g_leq55e,"v2_leq_G_55B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 888  3   1   6   5   640  1543
## [2,] Percent   57.6 0.2 0.1 0.4 0.3 41.5 100

Create dataset

v2_leq_G<-data.frame(v2_leq_G_51A,v2_leq_G_51B,v2_leq_G_52A,v2_leq_G_52B,v2_leq_G_53A,
                     v2_leq_G_53B,v2_leq_G_54A,v2_leq_G_54B,v2_leq_G_55A,v2_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v2_leq_H_56A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq56a_pers_leistung,v2_con$v2_leq_h_leq56a,"v2_leq_H_56A")
##                -999 bad good <NA>     
## [1,] No. cases 732  7   164  640  1543
## [2,] Percent   47.4 0.5 10.6 41.5 100

56B Impact (ordinal [0,1,2,3], v2_leq_H_56B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq56e_pers_leistung,v2_con$v2_leq_h_leq56e,"v2_leq_H_56B")
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 730  7   37  68  61 640  1543
## [2,] Percent   47.3 0.5 2.4 4.4 4  41.5 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v2_leq_H_57A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq57a_wicht_entsch,v2_con$v2_leq_h_leq57a,"v2_leq_H_57A")
##                -999 bad good <NA>     
## [1,] No. cases 653  36  214  640  1543
## [2,] Percent   42.3 2.3 13.9 41.5 100

57B Impact (ordinal [0,1,2,3], v2_leq_H_57B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq57e_wicht_entsch,v2_con$v2_leq_h_leq57e,"v2_leq_H_57B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 650  11  37  87  118 640  1543
## [2,] Percent   42.1 0.7 2.4 5.6 7.6 41.5 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v2_leq_H_58A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq58a_pers_gewohn,v2_con$v2_leq_h_leq58a,"v2_leq_H_58A")
##                -999 bad good <NA>     
## [1,] No. cases 705  38  160  640  1543
## [2,] Percent   45.7 2.5 10.4 41.5 100

58B Impact (ordinal [0,1,2,3], v2_leq_H_58B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq58e_pers_gewohn,v2_con$v2_leq_h_leq58e,"v2_leq_H_58B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 704  8   42  81  68  640  1543
## [2,] Percent   45.6 0.5 2.7 5.2 4.4 41.5 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v2_leq_H_59A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq59a_relig_ueberz,v2_con$v2_leq_h_leq59a,"v2_leq_H_59A")
##                -999 bad good <NA>     
## [1,] No. cases 863  7   33   640  1543
## [2,] Percent   55.9 0.5 2.1  41.5 100

59B Impact (ordinal [0,1,2,3], v2_leq_H_59B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq59e_relig_ueberz,v2_con$v2_leq_h_leq59e,"v2_leq_H_59B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 861  11  15 6   10  640  1543
## [2,] Percent   55.8 0.7 1  0.4 0.6 41.5 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v2_leq_H_60A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq60a_pol_ansichten,v2_clin$v2_leq_h_leq60a,"v2_leq_H_60A")
## Warning in (is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con != 2) &
## is.na(leq_con_old_name) == : longer object length is not a multiple of
## shorter object length
## Warning in (is.na(v2_itm_leq_chk_con) | v2_itm_leq_chk_con != 2) &
## is.na(leq_con_old_name): longer object length is not a multiple of shorter
## object length
##                -999 bad good <NA>     
## [1,] No. cases 857  11  35   640  1543
## [2,] Percent   55.5 0.7 2.3  41.5 100

60B Impact (ordinal [0,1,2,3], v2_leq_H_60B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq60e_pol_ansichten,v2_con$v2_leq_h_leq60e,"v2_leq_H_60B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 858  10  21  6   8   640  1543
## [2,] Percent   55.6 0.6 1.4 0.4 0.5 41.5 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v2_leq_H_61A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq61a_pers_eigent,v2_con$v2_leq_h_leq61a,"v2_leq_H_61A")
##                -999 bad good <NA>     
## [1,] No. cases 836  60  7    640  1543
## [2,] Percent   54.2 3.9 0.5  41.5 100

61B Impact (ordinal [0,1,2,3], v2_leq_H_61B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq61e_pers_eigent,v2_con$v2_leq_h_leq61e,"v2_leq_H_61B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 835  6   33  19  10  640  1543
## [2,] Percent   54.1 0.4 2.1 1.2 0.6 41.5 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v2_leq_H_62A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq62a_erholungsurl,v2_con$v2_leq_h_leq62a,"v2_leq_H_62A")
##                -999 bad good <NA>     
## [1,] No. cases 660  15  228  640  1543
## [2,] Percent   42.8 1   14.8 41.5 100

62B Impact (ordinal [0,1,2,3], v2_leq_H_62B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq62e_erholungsurl,v2_con$v2_leq_h_leq62e,"v2_leq_H_62B")
##                -999 0   1  2  3   <NA>     
## [1,] No. cases 660  18  62 93 70  640  1543
## [2,] Percent   42.8 1.2 4  6  4.5 41.5 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v2_leq_H_63A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq63a_reise_andere,v2_con$v2_leq_h_leq63a,"v2_leq_H_63A")
##                -999 bad good <NA>     
## [1,] No. cases 780  11  112  640  1543
## [2,] Percent   50.6 0.7 7.3  41.5 100

63B Impact (ordinal [0,1,2,3], v2_leq_H_63B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq63e_reise_andere,v2_con$v2_leq_h_leq63e,"v2_leq_H_63B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 778  10  33  53  29  640  1543
## [2,] Percent   50.4 0.6 2.1 3.4 1.9 41.5 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v2_leq_H_64A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq64a_familientreff,v2_con$v2_leq_h_leq64a,"v2_leq_H_64A")
##                -999 bad good <NA>     
## [1,] No. cases 805  34  64   640  1543
## [2,] Percent   52.2 2.2 4.1  41.5 100

64B Impact (ordinal [0,1,2,3], v2_leq_H_64B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq64e_familientreff,v2_con$v2_leq_h_leq64e,"v2_leq_H_64B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 804  10  27  42  20  640  1543
## [2,] Percent   52.1 0.6 1.7 2.7 1.3 41.5 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v2_leq_H_65A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq65a_ges_unternehm,v2_con$v2_leq_h_leq65a,"v2_leq_H_65A")
##                -999 bad good <NA>     
## [1,] No. cases 774  29  100  640  1543
## [2,] Percent   50.2 1.9 6.5  41.5 100

65B Impact (ordinal [0,1,2,3], v2_leq_H_65B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq65e_ges_unternehm,v2_con$v2_leq_h_leq65e,"v2_leq_H_65B")
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 772  6   41  53  31 640  1543
## [2,] Percent   50   0.4 2.7 3.4 2  41.5 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v2_leq_H_66A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq66a_neue_freunds,v2_con$v2_leq_h_leq66a,"v2_leq_H_66A")
##                -999 bad good <NA>     
## [1,] No. cases 646  9   248  640  1543
## [2,] Percent   41.9 0.6 16.1 41.5 100

66B Impact (ordinal [0,1,2,3], v2_leq_H_66B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq66e_neue_freunds,v2_con$v2_leq_h_leq66e,"v2_leq_H_66B")
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 646  12  80  103 62 640  1543
## [2,] Percent   41.9 0.8 5.2 6.7 4  41.5 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v2_leq_H_67A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq67a_ende_freunds,v2_con$v2_leq_h_leq67a,"v2_leq_H_67A")
##                -999 bad good <NA>     
## [1,] No. cases 805  72  26   640  1543
## [2,] Percent   52.2 4.7 1.7  41.5 100

67B Impact (ordinal [0,1,2,3], v2_leq_H_67B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq67e_ende_freunds,v2_con$v2_leq_h_leq67e,"v2_leq_H_67B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 804  12  27  35  25  640  1543
## [2,] Percent   52.1 0.8 1.7 2.3 1.6 41.5 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v2_leq_H_68A)

v2_leq_a_recode(v2_clin$v2_leq_h_leq68a_haustier,v2_con$v2_leq_h_leq68a,"v2_leq_H_68A")
##                -999 bad good <NA>     
## [1,] No. cases 832  36  35   640  1543
## [2,] Percent   53.9 2.3 2.3  41.5 100

68B Impact (ordinal [0,1,2,3], v2_leq_H_68B)

v2_leq_b_recode(v2_clin$v2_leq_h_leq68e_haustier,v2_con$v2_leq_h_leq68e,"v2_leq_H_68B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 830  9   14  12  38  640  1543
## [2,] Percent   53.8 0.6 0.9 0.8 2.5 41.5 100

Create dataset

v2_leq_H<-data.frame(v2_leq_H_56A,v2_leq_H_56B,v2_leq_H_57A,v2_leq_H_57B,v2_leq_H_58A,
                     v2_leq_H_58B,v2_leq_H_59A,v2_leq_H_59B,v2_leq_H_60A,v2_leq_H_60B,
                     v2_leq_H_61A,v2_leq_H_61B,v2_leq_H_62A,v2_leq_H_62B,v2_leq_H_63A,
                     v2_leq_H_63B,v2_leq_H_64A,v2_leq_H_64B,v2_leq_H_65A,v2_leq_H_65B,
                     v2_leq_H_66A,v2_leq_H_66B,v2_leq_H_67A,v2_leq_H_67B,v2_leq_H_68A,
                     v2_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v2_leq_I_69A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq69a_finanz_sit,v2_con$v2_leq_i_j_k_leq69a,"v2_leq_I_69A")
##                -999 bad good <NA>     
## [1,] No. cases 657  121 125  640  1543
## [2,] Percent   42.6 7.8 8.1  41.5 100

69B Impact (ordinal [0,1,2,3], v2_leq_I_69B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq69e_finanz_sit,v2_con$v2_leq_i_j_k_leq69e,"v2_leq_I_69B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 655  7   67  86  88  640  1543
## [2,] Percent   42.4 0.5 4.3 5.6 5.7 41.5 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v2_leq_I_70A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq70a_finanz_verpfl,v2_con$v2_leq_i_j_k_leq70a,"v2_leq_I_70A")
##                -999 bad good <NA>     
## [1,] No. cases 827  23  53   640  1543
## [2,] Percent   53.6 1.5 3.4  41.5 100

70B Impact (ordinal [0,1,2,3], v2_leq_I_70B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq70e_finanz_verpfl,v2_con$v2_leq_i_j_k_leq70e,"v2_leq_I_70B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 825  9   32  27  10  640  1543
## [2,] Percent   53.5 0.6 2.1 1.7 0.6 41.5 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v2_leq_I_71A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq71a_hypothek,v2_con$v2_leq_i_j_k_leq71a,"v2_leq_I_71A")
##                -999 bad good <NA>     
## [1,] No. cases 882  11  10   640  1543
## [2,] Percent   57.2 0.7 0.6  41.5 100

71B Impact (ordinal [0,1,2,3], v2_leq_I_71B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq71e_hypothek,v2_con$v2_leq_i_j_k_leq71e,"v2_leq_I_71B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 881  3   7   7   5   640  1543
## [2,] Percent   57.1 0.2 0.5 0.5 0.3 41.5 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v2_leq_I_72A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq72a_hypoth_kuend,v2_con$v2_leq_i_j_k_leq72a,"v2_leq_I_72A")
##                -999 bad good <NA>     
## [1,] No. cases 892  2   9    640  1543
## [2,] Percent   57.8 0.1 0.6  41.5 100

72B Impact (ordinal [0,1,2,3], v2_leq_I_72B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq72e_hypoth_kuend,v2_con$v2_leq_i_j_k_leq72e,"v2_leq_I_72B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 891  3   6   1   2   640  1543
## [2,] Percent   57.7 0.2 0.4 0.1 0.1 41.5 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v2_leq_I_73A)

v2_leq_a_recode(v2_clin$v2_leq_i_j_k_leq73a_kreditwuerdk,v2_con$v2_leq_i_j_k_leq73a,"v2_leq_I_73A")
##                -999 bad <NA>     
## [1,] No. cases 874  29  640  1543
## [2,] Percent   56.6 1.9 41.5 100

73B Impact (ordinal [0,1,2,3], v2_leq_I_73B)

v2_leq_b_recode(v2_clin$v2_leq_i_j_k_leq73e_kreditwuerdk,v2_con$v2_leq_i_j_k_leq73e,"v2_leq_I_73B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 874  3   12  8   6   640  1543
## [2,] Percent   56.6 0.2 0.8 0.5 0.4 41.5 100

Create dataset

v2_leq_I<-data.frame(v2_leq_I_69A,v2_leq_I_69B,v2_leq_I_70A,v2_leq_I_70B,v2_leq_I_71A,
                     v2_leq_I_71B,v2_leq_I_72A,v2_leq_I_72B,v2_leq_I_73A,v2_leq_I_73B)

WHOQOL-BREF

For explanation, please refer to the section in Visit 1

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v2_whoqol_itm1)

v2_quol_recode(v2_clin$v2_whoqol_bref_who1_lebensqualitaet,v2_con$v2_whoqol_bref_who1,"v2_whoqol_itm1",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 25  64  261  411  179  603  1543
## [2,] Percent   1.6 4.1 16.9 26.6 11.6 39.1 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v2_whoqol_itm2)”

v2_quol_recode(v2_clin$v2_whoqol_bref_who2_gesundheit,v2_con$v2_whoqol_bref_who2,"v2_whoqol_itm2",0)
##                1   2    3    4   5   NA's     
## [1,] No. cases 43  176  208  386 127 603  1543
## [2,] Percent   2.8 11.4 13.5 25  8.2 39.1 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v2_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v2_quol_recode(v2_clin$v2_whoqol_bref_who3_schmerzen,v2_con$v2_whoqol_bref_who3,"v2_whoqol_itm3",1)
##                1   2   3   4    5    NA's     
## [1,] No. cases 10  53  88  195  583  614  1543
## [2,] Percent   0.6 3.4 5.7 12.6 37.8 39.8 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v2_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v2_quol_recode(v2_clin$v2_whoqol_bref_who4_med_behand,v2_con$v2_whoqol_bref_who4,"v2_whoqol_itm4",1)
##                1   2    3   4    5    NA's     
## [1,] No. cases 85  183  136 176  350  613  1543
## [2,] Percent   5.5 11.9 8.8 11.4 22.7 39.7 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v2_whoqol_itm5)

v2_quol_recode(v2_clin$v2_whoqol_bref_who5_lebensgenuss,v2_con$v2_whoqol_bref_who5,"v2_whoqol_itm5",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 45  115 241  397  130 615  1543
## [2,] Percent   2.9 7.5 15.6 25.7 8.4 39.9 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v2_whoqol_itm6)

v2_quol_recode(v2_clin$v2_whoqol_bref_who6_lebenssinn,v2_con$v2_whoqol_bref_who6,"v2_whoqol_itm6",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 56  114 166  348  234  625  1543
## [2,] Percent   3.6 7.4 10.8 22.6 15.2 40.5 100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v2_whoqol_itm7)

v2_quol_recode(v2_clin$v2_whoqol_bref_who7_konzentration,v2_con$v2_whoqol_bref_who7,"v2_whoqol_itm7",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 18  128 363  362  58  614  1543
## [2,] Percent   1.2 8.3 23.5 23.5 3.8 39.8 100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v2_whoqol_itm8)

v2_quol_recode(v2_clin$v2_whoqol_bref_who8_sicherheit,v2_con$v2_whoqol_bref_who8,"v2_whoqol_itm8",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 22  70  237  410  189  615  1543
## [2,] Percent   1.4 4.5 15.4 26.6 12.2 39.9 100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v2_whoqol_itm9)

v2_quol_recode(v2_clin$v2_whoqol_bref_who9_umweltbed,v2_con$v2_whoqol_bref_who9,"v2_whoqol_itm9",0)
##                1  2   3    4    5    NA's     
## [1,] No. cases 15 33  203  443  229  620  1543
## [2,] Percent   1  2.1 13.2 28.7 14.8 40.2 100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v2_whoqol_itm10)

v2_quol_recode(v2_clin$v2_whoqol_bref_who10_energie,v2_con$v2_whoqol_bref_who10,"v2_whoqol_itm10",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 17  96  212  400  206  612  1543
## [2,] Percent   1.1 6.2 13.7 25.9 13.4 39.7 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v2_whoqol_itm11)

v2_quol_recode(v2_clin$v2_whoqol_bref_who11_aussehen,v2_con$v2_whoqol_bref_who11,"v2_whoqol_itm11",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 19  69  214  400  227  614  1543
## [2,] Percent   1.2 4.5 13.9 25.9 14.7 39.8 100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v2_whoqol_itm12)

v2_quol_recode(v2_clin$v2_whoqol_bref_who12_genug_geld,v2_con$v2_whoqol_bref_who12,"v2_whoqol_itm12",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 52  115 224  314  225  613  1543
## [2,] Percent   3.4 7.5 14.5 20.3 14.6 39.7 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v2_whoqol_itm13)

v2_quol_recode(v2_clin$v2_whoqol_bref_who13_infozugang,v2_con$v2_whoqol_bref_who13,"v2_whoqol_itm13",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 9   20  107 336  458  613  1543
## [2,] Percent   0.6 1.3 6.9 21.8 29.7 39.7 100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v2_whoqol_itm14)

v2_quol_recode(v2_clin$v2_whoqol_bref_who14_freizeitaktiv,v2_con$v2_whoqol_bref_who14,"v2_whoqol_itm14",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 12  54  189  352  321  615  1543
## [2,] Percent   0.8 3.5 12.2 22.8 20.8 39.9 100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v2_whoqol_itm15)”

v2_quol_recode(v2_clin$v2_whoqol_bref_who15_fortbewegung,v2_con$v2_whoqol_bref_who15,"v2_whoqol_itm15",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 4   37  141 327  420  614  1543
## [2,] Percent   0.3 2.4 9.1 21.2 27.2 39.8 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v2_whoqol_itm16)

v2_quol_recode(v2_clin$v2_whoqol_bref_who16_schlaf,v2_con$v2_whoqol_bref_who16,"v2_whoqol_itm16",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 45  147 164  409  176  602  1543
## [2,] Percent   2.9 9.5 10.6 26.5 11.4 39   100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v2_whoqol_itm17)

v2_quol_recode(v2_clin$v2_whoqol_bref_who17_alltag,v2_con$v2_whoqol_bref_who17,"v2_whoqol_itm17",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 29  110 181  408  211  604  1543
## [2,] Percent   1.9 7.1 11.7 26.4 13.7 39.1 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v2_whoqol_itm18)

v2_quol_recode(v2_clin$v2_whoqol_bref_who18_arbeitsfhgk,v2_con$v2_whoqol_bref_who18,"v2_whoqol_itm18",0)
##                1   2    3    4    5    NA's     
## [1,] No. cases 67  192  171  318  182  613  1543
## [2,] Percent   4.3 12.4 11.1 20.6 11.8 39.7 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v2_whoqol_itm19)

v2_quol_recode(v2_clin$v2_whoqol_bref_who19_selbstzufried,v2_con$v2_whoqol_bref_who19,"v2_whoqol_itm19",0)
##                1   2   3    4   5   NA's     
## [1,] No. cases 45  128 214  417 137 602  1543
## [2,] Percent   2.9 8.3 13.9 27  8.9 39   100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v2_whoqol_itm20)

v2_quol_recode(v2_clin$v2_whoqol_bref_who20_pers_bezieh,v2_con$v2_whoqol_bref_who20,"v2_whoqol_itm20",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 32  101 212  422  168  608  1543
## [2,] Percent   2.1 6.5 13.7 27.3 10.9 39.4 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v2_whoqol_itm21)

v2_quol_recode(v2_clin$v2_whoqol_bref_who21_sexualleben,v2_con$v2_whoqol_bref_who21,"v2_whoqol_itm21",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 91  170 262 284  115 621  1543
## [2,] Percent   5.9 11  17  18.4 7.5 40.2 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v2_whoqol_itm22)

v2_quol_recode(v2_clin$v2_whoqol_bref_who22_freunde,v2_con$v2_whoqol_bref_who22,"v2_whoqol_itm22",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 34  60  204  423  213  609  1543
## [2,] Percent   2.2 3.9 13.2 27.4 13.8 39.5 100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v2_whoqol_itm23)

v2_quol_recode(v2_clin$v2_whoqol_bref_who23_wohnbeding,v2_con$v2_whoqol_bref_who23,"v2_whoqol_itm23",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 43  59  144 420  275  602  1543
## [2,] Percent   2.8 3.8 9.3 27.2 17.8 39   100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v2_whoqol_itm24)

v2_quol_recode(v2_clin$v2_whoqol_bref_who24_gesundhdiens,v2_con$v2_whoqol_bref_who24,"v2_whoqol_itm24",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 14  19  114 438  352  606  1543
## [2,] Percent   0.9 1.2 7.4 28.4 22.8 39.3 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v2_whoqol_itm25)

v2_quol_recode(v2_clin$v2_whoqol_bref_who25_transport,v2_con$v2_whoqol_bref_who25,"v2_whoqol_itm25",0)
##                1  2   3   4    5    NA's     
## [1,] No. cases 16 40  108 407  366  606  1543
## [2,] Percent   1  2.6 7   26.4 23.7 39.3 100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v2_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v2_quol_recode(v2_clin$v2_whoqol_bref_who26_neg_gefuehle,v2_con$v2_whoqol_bref_who26,"v2_whoqol_itm26",1)
##                1   2   3    4    5    NA's     
## [1,] No. cases 25  139 222  352  192  613  1543
## [2,] Percent   1.6 9   14.4 22.8 12.4 39.7 100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v2_whoqol_dom_glob)

v2_whoqol_dom_glob_df<-data.frame(as.numeric(v2_whoqol_itm1),as.numeric(v2_whoqol_itm2))

v2_who_glob_no_nas<-rowSums(is.na(v2_whoqol_dom_glob_df))

v2_whoqol_dom_glob<-ifelse((v2_who_glob_no_nas==0) | (v2_who_glob_no_nas==1), 
                            rowMeans(v2_whoqol_dom_glob_df,na.rm=T)*4,NA)

v2_whoqol_dom_glob<-round(v2_whoqol_dom_glob,2)

summary(v2_whoqol_dom_glob)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     4.0    12.0    14.0    14.2    16.0    20.0     602

Physical Health (continuous [4-20],v2_whoqol_dom_phys)

v2_whoqol_dom_phys_df<-data.frame(as.numeric(v2_whoqol_itm3),as.numeric(v2_whoqol_itm10),as.numeric(v2_whoqol_itm16),as.numeric(v2_whoqol_itm15),as.numeric(v2_whoqol_itm17),as.numeric(v2_whoqol_itm4),as.numeric(v2_whoqol_itm18))

v2_who_phys_no_nas<-rowSums(is.na(v2_whoqol_dom_phys_df))

v2_whoqol_dom_phys<-ifelse((v2_who_phys_no_nas==0) | (v2_who_phys_no_nas==1), 
                            rowMeans(v2_whoqol_dom_phys_df,na.rm=T)*4,NA)

v2_whoqol_dom_phys<-round(v2_whoqol_dom_phys,2)

summary(v2_whoqol_dom_phys)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.71   13.14   15.43   15.16   17.71   20.00     616

Psychological (continuous [4-20],v2_whoqol_dom_psy)

v2_whoqol_dom_psy_df<-data.frame(as.numeric(v2_whoqol_itm5),as.numeric(v2_whoqol_itm7),as.numeric(v2_whoqol_itm19),as.numeric(v2_whoqol_itm11),as.numeric(v2_whoqol_itm26),as.numeric(v2_whoqol_itm6))

v2_who_psy_no_nas<-rowSums(is.na(v2_whoqol_dom_psy_df))

v2_whoqol_dom_psy<-ifelse((v2_who_psy_no_nas==0) | (v2_who_psy_no_nas==1), 
                            rowMeans(v2_whoqol_dom_psy_df,na.rm=T)*4,NA)

v2_whoqol_dom_psy<-round(v2_whoqol_dom_psy,2)

summary(v2_whoqol_dom_psy)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.33   12.00   14.67   14.25   16.67   20.00     616

Social relationships (continuous [4-20],v2_whoqol_dom_soc)

v2_whoqol_dom_soc_df<-data.frame(as.numeric(v2_whoqol_itm20),as.numeric(v2_whoqol_itm22),as.numeric(v2_whoqol_itm21))

v2_who_soc_no_nas<-rowSums(is.na(v2_whoqol_dom_soc_df))

v2_whoqol_dom_soc<-ifelse((v2_who_soc_no_nas==0) | (v2_who_soc_no_nas==1), 
                            rowMeans(v2_whoqol_dom_soc_df,na.rm=T)*4,NA)

v2_whoqol_dom_soc<-round(v2_whoqol_dom_soc,2)

summary(v2_whoqol_dom_soc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.11   16.00   20.00     603

Environment (continuous [4-20],v2_whoqol_dom_env)

v2_whoqol_dom_env_df<-data.frame(as.numeric(v2_whoqol_itm8),as.numeric(v2_whoqol_itm23),as.numeric(v2_whoqol_itm12),as.numeric(v2_whoqol_itm24),as.numeric(v2_whoqol_itm13),as.numeric(v2_whoqol_itm14),as.numeric(v2_whoqol_itm9),as.numeric(v2_whoqol_itm25))

v2_who_env_no_nas<-rowSums(is.na(v2_whoqol_dom_env_df))

v2_whoqol_dom_env<-ifelse((v2_who_env_no_nas==0) | (v2_who_env_no_nas==1), 
                            rowMeans(v2_whoqol_dom_env_df,na.rm=T)*4,NA)

v2_whoqol_dom_env<-round(v2_whoqol_dom_env,2)

summary(v2_whoqol_dom_env)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    6.00   14.50   16.00   15.85   18.00   20.00     614

Create dataset

v2_whoqol<-data.frame(v2_whoqol_itm1,v2_whoqol_itm2,v2_whoqol_itm3,v2_whoqol_itm4,
                      v2_whoqol_itm5,v2_whoqol_itm6,v2_whoqol_itm7,v2_whoqol_itm8,
                      v2_whoqol_itm9,v2_whoqol_itm10,v2_whoqol_itm11,v2_whoqol_itm12,
                      v2_whoqol_itm13,v2_whoqol_itm14,v2_whoqol_itm15,v2_whoqol_itm16,
                      v2_whoqol_itm17,v2_whoqol_itm18,v2_whoqol_itm19,v2_whoqol_itm20,
                      v2_whoqol_itm21,v2_whoqol_itm22,v2_whoqol_itm23,v2_whoqol_itm24,
                      v2_whoqol_itm25,v2_whoqol_itm26,v2_whoqol_dom_glob,
                      v2_whoqol_dom_phys,v2_whoqol_dom_psy,v2_whoqol_dom_soc,
                      v2_whoqol_dom_env)

Visit 2: Create dataframe

v2_df<-data.frame(v2_id,
                  v2_rec,
                  v2_clin_ill_ep,
                  v2_con_problems,
                  v2_dem,
                  v2_ev_prc_fst_ep,
                  v2_suic,
                  v2_leprcp,
                  v2_med,
                  v2_subst,
                  v2_symp_panss,
                  v2_symp_ids_c,
                  v2_symp_ymrs,
                  v2_ill_sev,
                  v2_nrpsy,
                  v2_sf12,
                  v2_med_adh,
                  v2_bdi2,
                  v2_asrm,
                  v2_mss,
                  v2_leq,
                  v2_whoqol)

Visit 3: Data preparation

Read in data of clinical participants

## [1] 1344

Read in data of control participants

## [1] 329

Modify column names

Only include subjects for which data for the first visit is present

v3_clin<-subset(v3_clin, as.character(v3_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v3_clin)[1]
## [1] 1223
v3_con<-subset(v3_con, as.character(v3_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v3_con)[1] 
## [1] 320

Participant identity column (categorical [id], v3_id)

v3_id<-as.factor(c(as.character(v3_clin$mnppsd),as.character(v3_con$mnppsd)))

Create separation column

v3_sep<-rep(as.factor("XXXXX"),(dim(v3_clin)[1]+dim(v3_con)[1]))

Visit 3: Recruitment data

Date of interview (categorical [year-month-day], v3_interv_date)

v3_interv_date<-c(as.Date(as.character(v3_clin$v3_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v3_con$v3_rekru_visit_rekr_datum), "%Y%m%d"))

Age at third interview (continuous [years], v3_age)

v3_age_years_clin<-as.numeric(substr(v3_clin$v3_ausschluss1_rekr_datum,1,4))-
  as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))

v3_age_years_con<-as.numeric(substr(v3_con$v3_rekru_visit_rekr_datu,1,4))-
  as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v3_age_years<-c(v3_age_years_clin,v3_age_years_con)

v3_age<-ifelse(c(as.numeric(substr(v3_clin$v3_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v3_con$v3_rekru_visit_rekr_datu,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v3_age_years-1,v3_age_years)
summary(v3_age) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    -1.0    32.0    46.0    45.3    54.0   926.0     773

Create dataset

v3_rec<-data.frame(v3_age,v3_interv_date)

Visit 3: Illness episodes between study visits

Please see Visit 2 for explanation.

Illness episodes since last study visit (only in clinical participants)

“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v3_clin_ill_ep_snc_lst)

v3_clin_ill_ep_snc_lst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_ill_ep_snc_lst<-ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==1,"Y",
                          ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==2,"N",
                            ifelse(c(v3_clin$v3_aktu_situat_aenderung_akt_sit,rep(-999,dim(v3_con)[1]))==3,"C",v3_clin_ill_ep_snc_lst)))

v3_clin_ill_ep_snc_lst<-factor(v3_clin_ill_ep_snc_lst)                         
descT(v3_clin_ill_ep_snc_lst)
##                -999 C  N    Y   <NA>     
## [1,] No. cases 320  77 334  148 664  1543
## [2,] Percent   20.7 5  21.6 9.6 43   100

“If yes, how many illness episodes? (continuous [no. illness episodes], v3_clin_no_ep)”

v3_clin_no_ep<-ifelse(v3_clin_ill_ep_snc_lst=="Y",c(v3_clin$v3_aktu_situat_anzahl_episoden,rep(-999,dim(v3_con)[1])),-999)
descT(v3_clin_no_ep)
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 731  110 28  3   1   670  1543
## [2,] Percent   47.4 7.1 1.8 0.2 0.1 43.4 100

In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).

First illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_man)

v3_clin_fst_ill_ep_man<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_man<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_fst_ill_ep_man)
##                -999 Y   <NA>     
## [1,] No. cases 857  30  656  1543
## [2,] Percent   55.5 1.9 42.5 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_dep)

v3_clin_fst_ill_ep_dep<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_dep<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_fst_ill_ep_dep)
##                -999 Y   <NA>     
## [1,] No. cases 808  79  656  1543
## [2,] Percent   52.4 5.1 42.5 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v3_clin_fst_ill_ep_mx<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_mx<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                         -999)

descT(v3_clin_fst_ill_ep_mx)
##                -999 Y   <NA>     
## [1,] No. cases 877  10  656  1543
## [2,] Percent   56.8 0.6 42.5 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v3_clin_fst_ill_ep_psy)

v3_clin_fst_ill_ep_psy<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_psy<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_fst_ill_ep_psy)
##                -999 Y   <NA>     
## [1,] No. cases 845  42  656  1543
## [2,] Percent   54.8 2.7 42.5 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v3_clin_fst_ill_ep_dur)

v3_clin_fst_ill_ep_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_dur<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",
                               ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==2,"two to four weeks", 
                                      ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
                                             ifelse(v3_clin_ill_ep_snc_lst=="N",-999,v3_clin_fst_ill_ep_dur))))

v3_clin_fst_ill_ep_dur<-ordered(v3_clin_fst_ill_ep_dur, 
                               levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_fst_ill_ep_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 654  27                  38               
## [2,] Percent   42.4 1.7                 2.5              
##      more than four weeks <NA>     
## [1,] 79                   745  1543
## [2,] 5.1                  48.3 100

“During this episode, were you hospitalized?” (dichotomous, v3_clin_fst_ill_ep_hsp)

v3_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="N",-999,
                          ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                            c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v3_con)[1]))==2,"N",
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                                c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y", v3_clin_fst_ill_ep_hsp)))

descT(v3_clin_fst_ill_ep_hsp)
##                -999 N   Y   <NA>     
## [1,] No. cases 654  75  72  742  1543
## [2,] Percent   42.4 4.9 4.7 48.1 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v3_clin_fst_ill_ep_hsp_dur)

v3_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_hsp_dur<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",  
                              ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
                                ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",    
                                            -999)))

v3_clin_fst_ill_ep_hsp_dur<-ordered(v3_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v3_clin_fst_ill_ep_hsp_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 806  12                  18               
## [2,] Percent   52.2 0.8                 1.2              
##      more than four weeks <NA>     
## [1,] 36                   671  1543
## [2,] 2.3                  43.5 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):

Reason for hospitalization: symptom worsensing (checkbox [Y], v3_clin_fst_ill_ep_symp_wrs)

v3_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_symp_wrs<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v3_con)[1]))==1,"Y",-999)
  
descT(v3_clin_fst_ill_ep_symp_wrs)
##                -999 Y   <NA>     
## [1,] No. cases 827  60  656  1543
## [2,] Percent   53.6 3.9 42.5 100

Reason for hospitalization: self-endangerment (checkbox [Y], v3_clin_fst_ill_ep_slf_end)

v3_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_slf_end<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y", 
                              -999)

descT(v3_clin_fst_ill_ep_slf_end)
##                -999 Y   <NA>     
## [1,] No. cases 878  9   656  1543
## [2,] Percent   56.9 0.6 42.5 100

Reason for hospitalization: suicidality (checkbox [Y], v3_clin_fst_ill_ep_suic)

v3_clin_fst_ill_ep_suic<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_suic<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y", 
                           -999)

descT(v3_clin_fst_ill_ep_suic)
##                -999 Y   <NA>     
## [1,] No. cases 877  10  656  1543
## [2,] Percent   56.8 0.6 42.5 100

Reason for hospitalization: endangerment of others (checkbox [Y], v3_clin_fst_ill_ep_oth_end)

v3_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_oth_end<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_fst_ill_ep_oth_end)
##                -999 Y   <NA>     
## [1,] No. cases 885  2   656  1543
## [2,] Percent   57.4 0.1 42.5 100

Reason for hospitalization: medication change (checkbox [Y], v3_clin_fst_ill_ep_med_chg)

v3_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_fst_ill_ep_med_chg<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_fst_ill_ep_med_chg)
##                -999 Y   <NA>     
## [1,] No. cases 878  9   656  1543
## [2,] Percent   56.9 0.6 42.5 100

Reason for hospitalization: other (checkbox [Y], v3_clin_fst_ill_ep_othr)

v3_clin_fst_ill_ep_othr<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_fst_ill_ep_othr<-ifelse(v3_clin_fst_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
 
descT(v3_clin_fst_ill_ep_othr)
##                -999 Y   <NA>     
## [1,] No. cases 878  9   656  1543
## [2,] Percent   56.9 0.6 42.5 100

Second illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_man)

v3_clin_sec_ill_ep_man<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_man<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",-999)

descT(v3_clin_sec_ill_ep_man)
##                -999 Y   <NA>     
## [1,] No. cases 750  5   788  1543
## [2,] Percent   48.6 0.3 51.1 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_dep) #frstill

v3_clin_sec_ill_ep_dep<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_dep<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",
                          -999)

descT(v3_clin_sec_ill_ep_dep)
##                -999 Y   <NA>     
## [1,] No. cases 737  18  788  1543
## [2,] Percent   47.8 1.2 51.1 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v3_clin_sec_ill_ep_mx<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_mx<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y", 
                         -999)

descT(v3_clin_sec_ill_ep_mx)
##                -999 <NA>     
## [1,] No. cases 755  788  1543
## [2,] Percent   48.9 51.1 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v3_clin_sec_ill_ep_psy)

v3_clin_sec_ill_ep_psy<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_psy<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y", 
                          -999)

descT(v3_clin_sec_ill_ep_psy)
##                -999 Y   <NA>     
## [1,] No. cases 753  2   788  1543
## [2,] Percent   48.8 0.1 51.1 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v3_clin_sec_ill_ep_dur)

v3_clin_sec_ill_ep_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_dur<-ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==1,"less than two weeks", 
                           ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",    
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",  
                                ifelse(v3_clin_ill_ep_snc_lst=="N",-999,v3_clin_sec_ill_ep_dur))))
 
v3_clin_sec_ill_ep_dur<-ordered(v3_clin_sec_ill_ep_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_sec_ill_ep_dur)
##                less than two weeks two to four weeks more than four weeks
## [1,] No. cases 6                   7                 9                   
## [2,] Percent   0.4                 0.5               0.6                 
##      <NA>     
## [1,] 1521 1543
## [2,] 98.6 100

“During this episode, were you hospitalized?” (dichotomous, v3_clin_sec_ill_ep_hsp)

v3_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="N",-999,
                          ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                            c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v3_con)[1]))==2,"N",
                              ifelse(v3_clin_ill_ep_snc_lst=="Y" &
                                c(v3_clin$v3_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y", v3_clin_sec_ill_ep_hsp)))

descT(v3_clin_sec_ill_ep_hsp)
##                -999 N   Y   <NA>     
## [1,] No. cases 654  10  10  869  1543
## [2,] Percent   42.4 0.6 0.6 56.3 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v3_clin_sec_ill_ep_hsp_dur)

v3_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_hsp_dur<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==1,"less than two weeks",  
                              ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
                                ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",    
                                            -999)))

v3_clin_sec_ill_ep_hsp_dur<-ordered(v3_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v3_clin_sec_ill_ep_hsp_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 741  3                   5                
## [2,] Percent   48   0.2                 0.3              
##      more than four weeks <NA>     
## [1,] 1                    793  1543
## [2,] 0.1                  51.4 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v3_clin_sec_ill_ep_symp_wrs)

v3_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_symp_wrs<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v3_con)[1]))==1,"Y",-999)
  
descT(v3_clin_sec_ill_ep_symp_wrs)
##                -999 Y   <NA>     
## [1,] No. cases 749  6   788  1543
## [2,] Percent   48.5 0.4 51.1 100

Reason for hospitalization: self-endangerment (checkbox [Y], v3_clin_sec_ill_ep_slf_end)

v3_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_slf_end<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y", 
                              -999)

descT(v3_clin_sec_ill_ep_slf_end)
##                -999 Y   <NA>     
## [1,] No. cases 754  1   788  1543
## [2,] Percent   48.9 0.1 51.1 100

Reason for hospitalization: suicidality (checkbox [Y], v3_clin_sec_ill_ep_suic)

v3_clin_sec_ill_ep_suic<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_suic<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y", 
                           -999)

descT(v3_clin_sec_ill_ep_suic)
##                -999 Y   <NA>     
## [1,] No. cases 752  3   788  1543
## [2,] Percent   48.7 0.2 51.1 100

Reason for hospitalization: endangerment of others (checkbox [Y], v3_clin_sec_ill_ep_oth_end)

v3_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_oth_end<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_sec_ill_ep_oth_end)
##                -999 <NA>     
## [1,] No. cases 755  788  1543
## [2,] Percent   48.9 51.1 100

Reason for hospitalization: medication change (checkbox [Y], v3_clin_sec_ill_ep_med_chg)

v3_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_sec_ill_ep_med_chg<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)

descT(v3_clin_sec_ill_ep_med_chg)
##                -999 Y   <NA>     
## [1,] No. cases 752  3   788  1543
## [2,] Percent   48.7 0.2 51.1 100

Reason for hospitalization: other (checkbox [Y], v3_clin_sec_ill_ep_othr)

v3_clin_sec_ill_ep_othr<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_sec_ill_ep_othr<-ifelse(v3_clin_sec_ill_ep_hsp=="Y" & v3_clin_ill_ep_snc_lst=="Y" & c(v3_clin$v3_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v3_con)[1]))==1, "Y",-999)
 
descT(v3_clin_sec_ill_ep_othr)
##                -999 <NA>     
## [1,] No. cases 755  788  1543
## [2,] Percent   48.9 51.1 100

Additional psychiatric hospitalization as in- or daypatient? (dichotomous, v3_clin_add_oth_hsp)

v3_clin_add_oth_hsp<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_add_oth_hsp<-ifelse(v3_clin_ill_ep_snc_lst=="Y" &
  c(v3_clin$v3_aktu_situat_aenderung_aufent,rep(-999,dim(v3_con)[1]))==1,"Y","N")

descT(v3_clin_add_oth_hsp)
##                N    Y   <NA>     
## [1,] No. cases 861  14  668  1543
## [2,] Percent   55.8 0.9 43.3 100

If yes, how many other hospitalizations? (continous [no. of hospitalizations], v3_clin_oth_hsp_nmb)

v3_clin_oth_hsp_nmb<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_oth_hsp_nmb<-ifelse(v3_clin_add_oth_hsp=="Y",
          c(v3_clin$v3_aktu_situat_aenderung_anzahl,rep(-999,dim(v3_con)[1])),-999)

descT(v3_clin_oth_hsp_nmb)
##                -999 1   2   <NA>     
## [1,] No. cases 861  7   2   673  1543
## [2,] Percent   55.8 0.5 0.1 43.6 100

If yes, duration of other hospitalizations? (ordinal, v3_clin_oth_hsp_dur)

v3_clin_oth_hsp_dur<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_clin_oth_hsp_dur<-
  ifelse(v3_clin_add_oth_hsp=="Y" & 
           c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==1,"less than two weeks", 
   ifelse(v3_clin_add_oth_hsp=="Y" & 
            c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==2,"two to four weeks",
    ifelse(v3_clin_add_oth_hsp=="Y" & 
             c(v3_clin$v3_aktu_situat_aenderung_dauer,rep(-999,dim(v3_con)[1]))==3,"more than four weeks",
     ifelse(v3_clin_add_oth_hsp=="N",-999,v3_clin_add_oth_hsp))))

v3_clin_oth_hsp_dur<-ordered(v3_clin_oth_hsp_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v3_clin_oth_hsp_dur)
##                less than two weeks two to four weeks more than four weeks
## [1,] No. cases 3                   6                 3                   
## [2,] Percent   0.2                 0.4               0.2                 
##      <NA>     
## [1,] 1531 1543
## [2,] 99.2 100

If yes, reason for other hospitalization(s) medication change? (checkbox [Y], v3_clin_othr_psy_med)

v3_clin_othr_psy_med<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_clin_othr_psy_med<-ifelse(v3_clin_add_oth_hsp=="Y" & v3_clin_add_oth_hsp=="Y" & 
      c(v3_clin$v3_aktu_situat_aenderung_medikament,rep(-999,dim(v3_con)[1]))==1,"Y",
                        ifelse(v3_clin_add_oth_hsp=="N",-999,v3_clin_othr_psy_med))
  
descT(v3_clin_othr_psy_med)
##                -999 Y   <NA>     
## [1,] No. cases 861  4   678  1543
## [2,] Percent   55.8 0.3 43.9 100

Current psychiatric treatment of both clinical and control participants (ordinal [1,2,3,4], v3_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v3_clin_cur_psy_trm<-rep(NA,dim(v3_clin)[1])
v3_con_cur_psy_trm<-rep(NA,dim(v3_con)[1])

v3_clin_cur_psy_trm<-ifelse(v3_clin$v3_aktu_situat_psybehandlung==0,"1",
                        ifelse(v3_clin$v3_aktu_situat_psybehandlung==3,"2", 
                          ifelse(v3_clin$v3_aktu_situat_psybehandlung==2,"3",
                            ifelse(v3_clin$v3_aktu_situat_psybehandlung==1,"4",v3_clin_cur_psy_trm)))) 

v3_con_cur_psy_trm<-ifelse(v3_con$v3_bildung_beruf_psybehandlung==0,"1",
                      ifelse(v3_con$v3_bildung_beruf_psybehandlung==3,"2",
                        ifelse(v3_con$v3_bildung_beruf_psybehandlung==2,"3",
                          ifelse(v3_con$v3_bildung_beruf_psybehandlung==1,"4",v3_con_cur_psy_trm))))

v3_cur_psy_trm<-factor(c(v3_clin_cur_psy_trm,v3_con_cur_psy_trm),ordered=T)
descT(v3_cur_psy_trm)
##                1    2    3   4   <NA>     
## [1,] No. cases 235  471  4   33  800  1543
## [2,] Percent   15.2 30.5 0.3 2.1 51.8 100

Create dataset

v3_clin_ill_ep<-data.frame(v3_clin_ill_ep_snc_lst,
                           v3_clin_no_ep,
                           v3_clin_fst_ill_ep_man,
                           v3_clin_fst_ill_ep_dep,
                           v3_clin_fst_ill_ep_mx,
                           v3_clin_fst_ill_ep_psy,
                           v3_clin_fst_ill_ep_dur,
                           v3_clin_fst_ill_ep_hsp,
                           v3_clin_fst_ill_ep_hsp_dur,
                           v3_clin_fst_ill_ep_symp_wrs,
                           v3_clin_fst_ill_ep_slf_end,
                           v3_clin_fst_ill_ep_suic,
                           v3_clin_fst_ill_ep_oth_end,
                           v3_clin_fst_ill_ep_med_chg,
                           v3_clin_fst_ill_ep_othr,
                           v3_clin_sec_ill_ep_man,
                           v3_clin_sec_ill_ep_dep,
                           v3_clin_sec_ill_ep_mx,
                           v3_clin_sec_ill_ep_psy,
                           v3_clin_sec_ill_ep_dur,
                           v3_clin_sec_ill_ep_hsp,
                           v3_clin_sec_ill_ep_hsp_dur,
                           v3_clin_sec_ill_ep_symp_wrs,
                           v3_clin_sec_ill_ep_slf_end,
                           v3_clin_sec_ill_ep_suic,
                           v3_clin_sec_ill_ep_oth_end,
                           v3_clin_sec_ill_ep_med_chg,
                           v3_clin_sec_ill_ep_othr,
                           v3_clin_add_oth_hsp,
                           v3_clin_oth_hsp_nmb,
                           v3_clin_oth_hsp_dur,
                           v3_clin_othr_psy_med,
                           v3_cur_psy_trm)

Visit 3: Demographic information

Did your marital status change since the last study visit? (dichotomous, v3_cng_mar_stat)

v3_clin_cng_mar_stat<-rep(NA,dim(v3_clin)[1]) 
v3_clin_cng_mar_stat<-ifelse(v3_clin$v3_aktu_situat_fam_stand==1, "Y", 
                        ifelse(v3_clin$v3_aktu_situat_fam_stand==2, "N", v3_clin_cng_mar_stat))

v3_con_cng_mar_stat<-rep(NA,dim(v3_con)[1]) 
v3_con_cng_mar_stat<-ifelse(v3_con$v3_famil_wohn_fam_stand==1, "Y", 
                        ifelse(v3_con$v3_famil_wohn_fam_stand==2, "N", v3_con_cng_mar_stat))

v3_cng_mar_stat<-factor(c(v3_clin_cng_mar_stat,v3_con_cng_mar_stat))

Marital status (categorical [married, married but living separately, single, divorced, widowed], v3_marital_stat)

v3_clin_marital_stat<-rep(NA,dim(v3_clin)[1])
v3_clin_marital_stat<-ifelse(v3_clin$v3_aktu_situat_fam_familienstand==1,"Married", 
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==2,"Married_living_sep",
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==3,"Single",
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==4,"Divorced",
                 ifelse(v3_clin$v3_aktu_situat_fam_familienstand==5,"Widowed",v3_clin_marital_stat)))))

v3_con_marital_stat<-rep(NA,dim(v3_con)[1])
v3_con_marital_stat<-ifelse(v3_con$v3_famil_wohn_fam_famstand==1,"Married", 
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==2,"Married_living_sep",
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==3,"Single",
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==4,"Divorced",
                 ifelse(v3_con$v3_famil_wohn_fam_famstand==5,"Widowed",v3_con_marital_stat)))))

v3_marital_stat<-factor(c(v3_clin_marital_stat,v3_con_marital_stat))
desc(v3_marital_stat)
##                Divorced Married Married_living_sep Single Widowed NA's
## [1,] No. cases 104      196     28                 418    10      787 
## [2,] Percent   6.7      12.7    1.8                27.1   0.6     51  
##          
## [1,] 1543
## [2,] 100

Relationship status

“Do you currently have a partner?” (dichotomous, v3_partner)

v3_clin_partner<-rep(NA,dim(v3_clin)[1])
v3_clin_partner<-ifelse(v3_clin$v3_aktu_situat_fam_fest_partner==1,"Y",
            ifelse(v3_clin$v3_aktu_situat_fam_fest_partner==2,"N",v3_clin_partner))

v3_con_partner<-rep(NA,dim(v3_con)[1])
v3_con_partner<-ifelse(v3_con$v3_famil_wohn_fam_partner==1,"Y",
            ifelse(v3_con$v3_famil_wohn_fam_partner==2,"N",v3_con_partner))


v3_partner<-factor(c(v3_clin_partner,v3_con_partner))
descT(v3_partner)
##                N    Y    <NA>     
## [1,] No. cases 360  383  800  1543
## [2,] Percent   23.3 24.8 51.8 100

Children

Biological (continuous [number], v3_no_bio_chld)

v3_no_bio_chld<-c(v3_clin$v3_aktu_situat_fam_kind_gesamt,v3_con$v3_famil_wohn_fam_lkind)
descT(v3_no_bio_chld)
##                0    1   2  3   4   5   <NA>     
## [1,] No. cases 468  132 93 52  8   2   788  1543
## [2,] Percent   30.3 8.6 6  3.4 0.5 0.1 51.1 100

Non-biological

Adoptive children (continuous [number], v3_no_adpt_chld)

v3_no_adpt_chld<-c(v3_clin$v3_aktu_situat_fam_adopt_gesamt,v3_con$v3_famil_wohn_fam_adkind)
descT(v3_no_adpt_chld)  
##                0    1   2   <NA>     
## [1,] No. cases 744  2   1   796  1543
## [2,] Percent   48.2 0.1 0.1 51.6 100

Step children (continuous [number], v3_stp_chld)

v3_stp_chld<-c(v3_clin$v3_aktu_situat_fam_stift_gesamt,v3_con$v3_famil_wohn_fam_skind)
descT(v3_stp_chld)      
##                0    1   2  3   4   <NA>     
## [1,] No. cases 672  39  16 4   2   810  1543
## [2,] Percent   43.6 2.5 1  0.3 0.1 52.5 100

Change in housing situation since last study visit? (dichotomous, v3_chg_hsng)

v3_clin_chg_hsng<-rep(NA,dim(v3_clin)[1])
v3_clin_chg_hsng<-ifelse(v3_clin$v3_wohnsituation_wohn_aenderung==1,"Y",
                  ifelse(v3_clin$v3_wohnsituation_wohn_aenderung==2,"N",v3_clin_chg_hsng))  

v3_con_chg_hsng<-rep(NA,dim(v3_con)[1])
v3_con_chg_hsng<-ifelse(v3_con$v3_famil_wohn_wohn_stand==1,"Y",
                 ifelse(v3_con$v3_famil_wohn_wohn_stand==2,"N",v3_con_chg_hsng))

v3_chg_hsng<-factor(c(v3_clin_chg_hsng,v3_con_chg_hsng))
descT(v3_chg_hsng)
##                N    Y   <NA>     
## [1,] No. cases 691  75  777  1543
## [2,] Percent   44.8 4.9 50.4 100

Living alone (dichotomous, v3_liv_aln)

v3_clin_liv_aln<-rep(NA,dim(v3_clin)[1])
v3_clin_liv_aln<-ifelse(v3_clin$v3_wohnsituation_wohn_allein==1,"Y",    
                 ifelse(v3_clin$v3_wohnsituation_wohn_allein==0,"N",v3_clin_liv_aln))   

v3_con_liv_aln<-rep(NA,dim(v3_con)[1])
v3_con_liv_aln<-ifelse(v3_con$v3_famil_wohn_wohn_allein==1,"Y", 
                 ifelse(v3_con$v3_famil_wohn_wohn_allein==0,"N",v3_con_liv_aln))
                 
v3_liv_aln<-factor(c(v3_clin_liv_aln,v3_con_liv_aln))
descT(v3_liv_aln)
##                N    Y    <NA>     
## [1,] No. cases 484  291  768  1543
## [2,] Percent   31.4 18.9 49.8 100

Employment

Did your employment situation change since the last study visit?

v3_clin_chg_empl_stat<-rep(NA,dim(v3_clin)[1]) 
v3_clin_chg_empl_stat<-ifelse(v3_clin$v3_wohnsituation_erwerb_aenderung==1, "Y", 
                  ifelse(v3_clin$v3_wohnsituation_erwerb_aenderung==2, "N",v3_clin_chg_empl_stat))

v3_con_chg_empl_stat<-rep(NA,dim(v3_con)[1]) 
v3_con_chg_empl_stat<-ifelse(v3_con$v3_bildung_beruf_bild_stand==1, "Y", 
                  ifelse(v3_con$v3_bildung_beruf_bild_stand==2, "N",v3_con_chg_empl_stat))

v3_chg_empl_stat<-factor(c(v3_clin_chg_empl_stat,v3_con_chg_empl_stat))
descT(v3_chg_empl_stat)
##                N    Y   <NA>     
## [1,] No. cases 634  109 800  1543
## [2,] Percent   41.1 7.1 51.8 100

Currently paid employment (dichotomous, v3_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v3_clin_curr_paid_empl<-rep(NA,dim(v3_clin)[1])
v3_clin_curr_paid_empl<-ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==1,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==2,"Y",   
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==3,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==4,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==5,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==6,-999,  
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==7,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==8,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==9,"Y",
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==10,"Y",  
                        ifelse(v3_clin$v3_wohnsituation_erwerbstaetig==11,"N",v3_clin_curr_paid_empl)))))))))))

v3_con_curr_paid_empl<-rep(NA,dim(v3_con)[1])
v3_con_curr_paid_empl<-ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==1,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==2,"Y",    
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==3,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==4,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==5,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==6,-999,   
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==7,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==8,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==9,"Y",
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==10,"Y",   
                        ifelse(v3_con$v3_bildung_beruf_erwerb_taetig==11,"N",v3_con_curr_paid_empl)))))))))))

v3_curr_paid_empl<-factor(c(v3_clin_curr_paid_empl,v3_con_curr_paid_empl))
descT(v3_curr_paid_empl)
##                -999 N    Y    <NA>     
## [1,] No. cases 14   363  380  786  1543
## [2,] Percent   0.9  23.5 24.6 50.9 100

Disability pension due to psychological/psychiatric illness (dichotomous, v3_disabl_pens)

NB: Not available (-999) in control participants

v3_clin_disabl_pens<-rep(NA,dim(v3_clin)[1])
v3_clin_disabl_pens<-ifelse(v3_clin$v3_wohnsituation_rente_psych==1,"Y",        
                     ifelse(v3_clin$v3_wohnsituation_rente_psych==2,"N",v3_clin_disabl_pens))       

v3_con_disabl_pens<-rep(-999,dim(v3_con)[1])

v3_disabl_pens<-factor(c(v3_clin_disabl_pens,v3_con_disabl_pens))
descT(v3_disabl_pens)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  202  236  785  1543
## [2,] Percent   20.7 13.1 15.3 50.9 100

Employed in workshop for handicapped persons (dichotomous, v3_spec_emp)

v3_clin_spec_emp<-rep(NA,dim(v3_clin)[1])
v3_clin_spec_emp<-ifelse(v3_clin$v3_wohnsituation_erwerb_werk==1,"Y",           
                  ifelse(v3_clin$v3_wohnsituation_erwerb_werk==2,"N",v3_clin_spec_emp))         

v3_con_spec_emp<-rep(NA,dim(v3_con)[1])
v3_con_spec_emp<-ifelse(v3_con$v3_bildung_beruf_erwerb_wfbm==1,"Y",         
                 ifelse(v3_con$v3_bildung_beruf_erwerb_wfbm==2,"N",v3_con_spec_emp))            


v3_spec_emp<-factor(c(v3_clin_spec_emp,v3_con_spec_emp))
descT(v3_spec_emp)
##                N    Y   <NA>     
## [1,] No. cases 269  55  1219 1543
## [2,] Percent   17.4 3.6 79   100

Weeks of work absence due to psychological distress in past six months (continuous [weeks], v3_wrk_abs_pst_6_mths)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.

v3_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v3_clin)[1])
v3_clin_wrk_abs_pst_6_mths<-ifelse((v3_clin$v3_wohnsituation_erwerb_unbekannt==1 | v3_clin$v3_wohnsituation_erwerb_rente==1 |  
                                 v3_clin$v3_wohnsituation_erwerb_fehlen>26),-999, v3_clin$v3_wohnsituation_erwerb_fehlen)

v3_con_wrk_abs_pst_6_mths<-rep(NA,dim(v3_con)[1])
v3_con_wrk_abs_pst_6_mths<-ifelse((v3_con$v3_bildung_beruf_erwerb_ausfallu==1 | v3_con$v3_bildung_beruf_erwerb_rente==1 |  
                                 v3_con$v3_bildung_beruf_erwerb_ausfallm>26),-999, v3_con$v3_bildung_beruf_erwerb_ausfallm)

v3_wrk_abs_pst_6_mths<-c(v3_clin_wrk_abs_pst_6_mths,v3_con_wrk_abs_pst_6_mths)
descT(v3_wrk_abs_pst_6_mths)
##                -999 0    1   2   3   4   6   7   8   10  11  12  13  14 
## [1,] No. cases 285  199  9   12  8   4   3   2   7   2   1   2   1   1  
## [2,] Percent   18.5 12.9 0.6 0.8 0.5 0.3 0.2 0.1 0.5 0.1 0.1 0.1 0.1 0.1
##      15  16  17  20  24  26  <NA>     
## [1,] 1   1   1   1   10  2   991  1543
## [2,] 0.1 0.1 0.1 0.1 0.6 0.1 64.2 100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v3_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v3_clin_cur_work_restr<-rep(NA,dim(v3_clin)[1])
v3_clin_cur_work_restr<-ifelse(v3_clin$v3_wohnsituation_erwerb_psysymptom==1,"Y",   
                    ifelse(v3_clin$v3_wohnsituation_erwerb_psysymptom==2,"N",v3_clin_cur_work_restr))    

v3_con_cur_work_restr<-rep(NA,dim(v3_con)[1])
v3_con_cur_work_restr<-ifelse(v3_con$v3_bildung_beruf_erwerb_psyeinsch==1,"Y",  
                    ifelse(v3_con$v3_bildung_beruf_erwerb_psyeinsch==2,"N",v3_con_cur_work_restr))   

v3_cur_work_restr<-factor(c(v3_clin_cur_work_restr,v3_con_cur_work_restr))
descT(v3_cur_work_restr)
##                N    Y    <NA>     
## [1,] No. cases 455  236  852  1543
## [2,] Percent   29.5 15.3 55.2 100

Self-reported Weight (continuous [kilograms], v3_weight)

v3_weight<-c(v3_clin$v3_wohnsituation_erwerb_gewicht,v3_con$v3_bildung_beruf_erwerb_gewicht)
summary(v3_weight)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   41.00   68.00   81.00   83.89   97.00  175.00     788

BMI (continuous [BMI], v3_bmi)

We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v3_bmi<-v3_weight/(v1_height/100)^2
summary(v3_bmi)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.63   22.91   26.57   27.68   31.22   50.78     790

Create dataset

v3_dem<-data.frame(v3_cng_mar_stat,v3_marital_stat,v3_partner,v3_no_bio_chld,v3_no_adpt_chld,v3_stp_chld,v3_chg_hsng,v3_liv_aln,
                    v3_chg_empl_stat,v3_curr_paid_empl,v3_disabl_pens,v3_spec_emp,v3_wrk_abs_pst_6_mths,v3_cur_work_restr,
                    v3_weight,v3_bmi)

Visit 3: Life events precipitating illness episode between study visits

Please see Visit 2 for explanation.

**Life events: Occurred before illness episode? (dichotomous, v3_evnt_prcp_b4_*)**

for(i in 1:length(grep("v3_ergaenz_leq_leq_zeit_31055_",names(v3_clin)))){
  b4_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_zeit_31055_",names(v3_clin))[i]],
                   paste("v3_evnt_prcp_b4_",i,sep=""))
}

**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v3_evnt_prcp_f_*)**

for(i in 1:length(grep("v3_ergaenz_leq_leq_ausloeser_31055_",names(v3_clin)))){
  prcp_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_ausloeser_31055_",names(v3_clin))[i]],
                   paste("v3_evnt_prcp_f_",i,sep=""))
}

**Life events: LEQ item number (categorical [LEQ item number], v3_evnt_prcp_it_*)**

for(i in 1:length(grep("v3_ergaenz_leq_leq_item_31055_",names(v3_clin)))){
  leq_event_recode_v3(v3_clin[,grep("v3_ergaenz_leq_leq_item_31055_",names(v3_clin))[i]],
                   paste("v3_evnt_prcp_it_",i,sep=""))
}

Create dataset

v3_leprcp<-data.frame(v3_evnt_prcp_it_1,v3_evnt_prcp_b4_1,v3_evnt_prcp_f_1,
                      v3_evnt_prcp_it_2,v3_evnt_prcp_b4_2,v3_evnt_prcp_f_2,
                      v3_evnt_prcp_it_3,v3_evnt_prcp_b4_3,v3_evnt_prcp_f_3,
                      v3_evnt_prcp_it_4,v3_evnt_prcp_b4_4,v3_evnt_prcp_f_4,
                      v3_evnt_prcp_it_5,v3_evnt_prcp_b4_5,v3_evnt_prcp_f_5,
                      v3_evnt_prcp_it_6,v3_evnt_prcp_b4_6,v3_evnt_prcp_f_6,
                      v3_evnt_prcp_it_7,v3_evnt_prcp_b4_7,v3_evnt_prcp_f_7,
                      v3_evnt_prcp_it_8,v3_evnt_prcp_b4_8,v3_evnt_prcp_f_8,
                      v3_evnt_prcp_it_9,v3_evnt_prcp_b4_9,v3_evnt_prcp_f_9,
                      v3_evnt_prcp_it_10,v3_evnt_prcp_b4_10,v3_evnt_prcp_f_10,
                      v3_evnt_prcp_it_11,v3_evnt_prcp_b4_11,v3_evnt_prcp_f_11,
                      v3_evnt_prcp_it_12,v3_evnt_prcp_b4_12,v3_evnt_prcp_f_12,
                      v3_evnt_prcp_it_13,v3_evnt_prcp_b4_13,v3_evnt_prcp_f_13,
                      v3_evnt_prcp_it_14,v3_evnt_prcp_b4_14,v3_evnt_prcp_f_14,
                      v3_evnt_prcp_it_15,v3_evnt_prcp_b4_15,v3_evnt_prcp_f_15,
                      v3_evnt_prcp_it_16,v3_evnt_prcp_b4_16,v3_evnt_prcp_f_16,
                      v3_evnt_prcp_it_17,v3_evnt_prcp_b4_17,v3_evnt_prcp_f_17,
                      v3_evnt_prcp_it_18,v3_evnt_prcp_b4_18,v3_evnt_prcp_f_18,
                      v3_evnt_prcp_it_19,v3_evnt_prcp_b4_19,v3_evnt_prcp_f_19,
                      v3_evnt_prcp_it_20,v3_evnt_prcp_b4_20,v3_evnt_prcp_f_20,
                      v3_evnt_prcp_it_21,v3_evnt_prcp_b4_21,v3_evnt_prcp_f_21,
                      v3_evnt_prcp_it_22,v3_evnt_prcp_b4_22,v3_evnt_prcp_f_22,
                      v3_evnt_prcp_it_23,v3_evnt_prcp_b4_23,v3_evnt_prcp_f_23,
                      v3_evnt_prcp_it_24,v3_evnt_prcp_b4_24,v3_evnt_prcp_f_24,
                      v3_evnt_prcp_it_25,v3_evnt_prcp_b4_25,v3_evnt_prcp_f_25,
                      v3_evnt_prcp_it_26,v3_evnt_prcp_b4_26,v3_evnt_prcp_f_26,
                      v3_evnt_prcp_it_27,v3_evnt_prcp_b4_27,v3_evnt_prcp_f_27,
                      v3_evnt_prcp_it_28,v3_evnt_prcp_b4_28,v3_evnt_prcp_f_28,
                      v3_evnt_prcp_it_29,v3_evnt_prcp_b4_29,v3_evnt_prcp_f_29,
                      v3_evnt_prcp_it_30,v3_evnt_prcp_b4_30,v3_evnt_prcp_f_30,
                      v3_evnt_prcp_it_31,v3_evnt_prcp_b4_31,v3_evnt_prcp_f_31)

Visit 3: Suicide attempts and suicidal ideation since last study visit

Please note that all of the following questions refer to suicide attempts and suicidal ideation occurring since the last study visit.

Suicidal ideation

Suicidal ideation since last study visit (dichotomous, v3_suic_ide_snc_lst_vst)

Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.

v3_suic_ide_snc_lst_vst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_suic_ide_snc_lst_vst<-ifelse(c(v3_clin$v3_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v3_con)[1]))==1, "N", 
                            ifelse(c(v3_clin$v3_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v3_con)[1]))==3, "Y",                                                 v3_suic_ide_snc_lst_vst))

v3_suic_ide_snc_lst_vst<-factor(v3_suic_ide_snc_lst_vst)
descT(v3_suic_ide_snc_lst_vst)
##                -999 N    Y   <NA>     
## [1,] No. cases 320  420  137 666  1543
## [2,] Percent   20.7 27.2 8.9 43.2 100

Suicidal ideation detailed (ordinal [1,2,3,4], v3_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v3_scid_suic_ide<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_scid_suic_ide<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==3, "3",
                                       ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v3_con)[1]))==4, "4",-999))))

v3_scid_suic_ide<-factor(v3_scid_suic_ide,ordered=T)                                   
descT(v3_scid_suic_ide)
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 740  75  20  22  20  666  1543
## [2,] Percent   48   4.9 1.3 1.4 1.3 43.2 100

Thoughts about methods (ordinal [1,2,3], v3_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.

v3_scid_suic_thght_mth<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_scid_suic_thght_mth<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v3_con)[1]))==3, "3",-999)))

v3_scid_suic_thght_mth<-factor(v3_scid_suic_thght_mth,ordered=T)                                   
descT(v3_scid_suic_thght_mth)
##                -999 1   2   3   <NA>     
## [1,] No. cases 740  59  44  27  673  1543
## [2,] Percent   48   3.8 2.9 1.7 43.6 100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v3_scid_suic_note_thgts)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v3_scid_suic_note_thgts<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_scid_suic_note_thgts<-ifelse(v3_suic_ide_snc_lst_vst=="Y"&c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==3, "3",
                                       ifelse(v3_suic_ide_snc_lst_vst=="Y" & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==4, "4",-999))))

v3_scid_suic_note_thgts<-factor(v3_scid_suic_note_thgts,ordered=T)                                   
descT(v3_scid_suic_note_thgts)
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 740  121 4   2   4   672  1543
## [2,] Percent   48   7.8 0.3 0.1 0.3 43.6 100

Suicide attemps

Suicide attempt since last study visit (ordinal [1,2,3], v3_suic_attmpt_snc_lst_vst)

This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.

v3_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_suic_attmpt_snc_lst_vst<-ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==1, "1",
                         ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==2, "2",
                                ifelse(c(v3_clin$v3_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v3_con)[1]))==3, "3",-999)))

v3_suic_attmpt_snc_lst_vst<-factor(v3_suic_attmpt_snc_lst_vst,ordered=T)                                   
descT(v3_suic_attmpt_snc_lst_vst)
##                -999 1    2   3   <NA>     
## [1,] No. cases 320  536  2   8   677  1543
## [2,] Percent   20.7 34.7 0.1 0.5 43.9 100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v3_no_suic_attmpt)

This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.

v3_no_suic_attmpt<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))
v3_no_suic_attmpt<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999, ifelse(v3_suic_attmpt_snc_lst_vst>1, c(v3_clin$v3_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v3_con)[1])),v3_no_suic_attmpt))

v3_no_suic_attmpt<-factor(v3_no_suic_attmpt,ordered=T)
descT(v3_no_suic_attmpt)
##                -999 1   2   <NA>     
## [1,] No. cases 856  9   1   677  1543
## [2,] Percent   55.5 0.6 0.1 43.9 100

Preparation of suicide attempt (ordinal [1,2,3,4], v3_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.

v3_prep_suic_attp_ord<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_prep_suic_attp_ord<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999, 
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==1, "1",
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==2, "2",             
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==3, "3",
                              ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v3_con)[1]))==4, "4",
                              v3_prep_suic_attp_ord))))) 

v3_prep_suic_attp_ord<-factor(v3_prep_suic_attp_ord,ordered=T)
descT(v3_prep_suic_attp_ord)
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 856  5   2   1   2   677  1543
## [2,] Percent   55.5 0.3 0.1 0.1 0.1 43.9 100

Suicidal attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v3_suic_note_attmpt)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v3_suic_note_attmpt<-c(rep(NA,dim(v3_clin)[1]),rep(-999,dim(v3_con)[1]))

v3_suic_note_attmpt<-ifelse(v3_suic_attmpt_snc_lst_vst==1, -999, 
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==1, "1",
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==2, "2",
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==3, "3",
                            ifelse(v3_suic_attmpt_snc_lst_vst>1 & c(v3_clin$v3_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v3_con)[1]))==4, "4",
                            v3_suic_note_attmpt))))) 

v3_suic_note_attmpt<-factor(v3_suic_note_attmpt,ordered=T)
descT(v3_suic_note_attmpt)
##                -999 1   3   4   <NA>     
## [1,] No. cases 856  5   1   1   680  1543
## [2,] Percent   55.5 0.3 0.1 0.1 44.1 100

Create dataset

v3_suic<-data.frame(v3_suic_ide_snc_lst_vst,v3_scid_suic_ide,v3_scid_suic_thght_mth,v3_scid_suic_note_thgts,
                    v3_suic_attmpt_snc_lst_vst,v3_no_suic_attmpt,v3_prep_suic_attp_ord,
                    v3_suic_note_attmpt)

Visit 3: Medication

PsyCourse 3.1 contains now medication data. The code below creates the following variables for each person:

Number of antidepressants prescribed (continuous [number], v3_Antidepressants) Number of antipsychotics prescribed (continuous [number], v3_Antipsychotics) Number of mood stabilizers prescribed (continuous [number], v3_Mood_stabilizers) Number of tranquilizers prescribed (continuous [number], v3_Tranquilizers) Number of other psychiatric medications (continuous [number], v3_Other_psychiatric)

Clinical participants

#get the following variables from v3_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v3_clin_medication_variables_1<-as.data.frame(v3_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v3_clin))])
dim(v3_clin_medication_variables_1) #[1] 1223   61
## [1] 1223   61
#recode the variables that are coded as characters/logicals in the "v3_clin_medication_variables_1" as factors
v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_15<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_15)

v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_15<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_15)

v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_16<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_16)

v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_16<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_16)

v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_17<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_medi_199998_17)

v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_17<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_med_kategorie_199998_17)

v3_clin_medication_variables_1$v3_medikabehand3_depot_medi_200170_3<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_depot_medi_200170_3)

v3_clin_medication_variables_1$v3_medikabehand3_depot_kategorie_200170_3<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_depot_kategorie_200170_3)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_8<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_8)
v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_8<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_8)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_9<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_9)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_9)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_10<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_medi_199584_10)

v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v3_clin_medication_variables_1$v3_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v3_clin_medications_duplicated_1<-as.data.frame(t(apply(v3_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v3_clin_medications_duplicated_1) #1223   30
## [1] 1223   30
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_clin, not as character
v3_clin_medication_variables_1[,!c(TRUE, FALSE)][v3_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v3_clin_medication_variables_1) #1223   61
## [1] 1223   61
#bind columns id and medication names, but not categories together 
v3_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v3_clin_medication_variables_1[,1], v3_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v3_clin_medication_name_1) #1223   31
## [1] 1223   31
#get the medication categories from the "_medication_variables_1" dataframe
v3_clin_medication_categories_1<-as.data.frame(v3_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v3_clin_medication_categories_1) #1223   31
## [1] 1223   31
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_clin, not as character 
#Important: v3_clin_medication_name_1=="NA" replaced with is.na(v3_clin_medication_name_1)
v3_clin_medication_categories_1[is.na(v3_clin_medication_name_1)] <- NA
#write.csv(v3_clin_medication_categories_1, file="v3_clin_medication_group_1.csv") 

#Make a count table of medications
v3_clin_med_table<-data.frame("mnppsd"=v3_clin$mnppsd)
v3_clin_med_table$v3_Antidepressants<-rowSums(v3_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v3_clin_med_table$v3_Antipsychotics<-rowSums(v3_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v3_clin_med_table$v3_Mood_stabilizers<-rowSums(v3_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v3_clin_med_table$v3_Tranquilizers<-rowSums(v3_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v3_clin_med_table$v3_Other_psychiatric<-rowSums(v3_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v3_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v3_con_medication_variables_1<-as.data.frame(v3_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v3_con))])
dim(v3_con_medication_variables_1) #[1] 320 29 
## [1] 320  29
#recode the variables that are coded as characters/logicals in the "v3_con_medication_variables_1" as factors
v3_con_medication_variables_1$v3_medikabehand3_med_medi_200705_8<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_med_medi_200705_8)

v3_con_medication_variables_1$v3_medikabehand3_med_kategorie_200705_8<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_med_kategorie_200705_8)

v3_con_medication_variables_1$v3_medikabehand3_bedarf_medi_201187_4<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_bedarf_medi_201187_4)

v3_con_medication_variables_1$v3_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v3_con_medication_variables_1$v3_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v3_con_medications_duplicated_1<-as.data.frame(t(apply(v3_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v3_con_medications_duplicated_1) #320  14
## [1] 320  14
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_con, not as character 
v3_con_medication_variables_1[,!c(TRUE, FALSE)][v3_con_medications_duplicated_1=="TRUE"] <- NA
dim(v3_con_medication_variables_1) #320   29
## [1] 320  29
#bind columns id and medication names, but not categories together 
v3_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v3_con_medication_variables_1[,1], v3_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v3_con_medication_name_1) #320  15
## [1] 320  15
#get the medication categories from the "_medication_variables_1" dataframe
v3_con_medication_categories_1<-as.data.frame(v3_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v3_con_medication_categories_1) #320  15
## [1] 320  15
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v3_con, not as character 
#Important: v3_con_medication_name_1=="NA" replaced with is.na(v3_con_medication_name_1)
v3_con_medication_categories_1[is.na(v3_con_medication_name_1)] <- NA
#write.csv(v3_con_medication_categories_1, file="v3_con_medication_group_1.csv")

#Make a count table of medications
v3_con_med_table<-data.frame("mnppsd"=v3_con$mnppsd)
v3_con_med_table$v3_Antidepressants<-rowSums(v3_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v3_con_med_table$v3_Antipsychotics<-rowSums(v3_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v3_con_med_table$v3_Mood_stabilizers<-rowSums(v3_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v3_con_med_table$v3_Tranquilizers<-rowSums(v3_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v3_con_med_table$v3_Other_psychiatric<-rowSums(v3_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v3_clin and v3_con together by rows

v3_drugs<-rbind(v3_clin_med_table,v3_con_med_table)
dim(v3_drugs) #1543    6
## [1] 1543    6
#check if the id column of v3_drugs and v1_id match
table(droplevels(v3_drugs[,1])==v1_id)
## 
## TRUE 
## 1543

Adverse events under current medication (dichotomous, v3_adv)

v3_clin_adv<-ifelse(v3_clin$v3_medikabehand_medi2_nebenwirk==1,"Y","N")
v3_con_adv<-rep("-999",dim(v3_con)[1])
v3_adv<-factor(c(v3_clin_adv,v3_con_adv))
descT(v3_adv)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  162  275  786  1543
## [2,] Percent   20.7 10.5 17.8 50.9 100

Psychiatric medication change during the past six months (dichotomous, v3_medchange)

v3_clin_medchange<-rep(NA,dim(v3_clin)[1])
v3_clin_medchange<-ifelse(v3_clin$v3_medikabehand_medi3_mediaenderung==1,"Y","N")
v3_con_medchange<-rep("-999",dim(v3_con)[1])

v3_medchange<-as.factor(c(v3_clin_medchange,v3_con_medchange))
descT(v3_medchange)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  191  250  782  1543
## [2,] Percent   20.7 12.4 16.2 50.7 100

Lithium

Please see the section in Visit 1 for explanation.

“Did you ever take lithium?” (dichotomous, v3_lith)

v3_clin_lith<-rep(NA,dim(v3_clin)[1])
v3_clin_lith<-ifelse(v3_clin$v3_medikabehand_med_zusatz_lithium==1,"Y","N")
v3_con_lith<-rep("-999",dim(v3_con)[1])

v3_lith<-as.factor(c(v3_clin_lith,v3_con_lith))
v3_lith<-as.factor(v3_lith)

descT(v3_lith)
##                -999 N   Y   <NA>     
## [1,] No. cases 320  151 106 966  1543
## [2,] Percent   20.7 9.8 6.9 62.6 100

“If yes, for how long?” (dichotomous, v3_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.

v3_clin_lith_prd<-rep(NA,dim(v3_clin)[1])
v3_con_lith_prd<-rep(-999,dim(v3_con)[1])

v3_clin_lith_prd<-ifelse(v3_clin_lith=="N", -999, ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v3_clin$v3_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v3_lith_prd<-factor(c(v3_clin_lith_prd,v3_con_lith_prd))
descT(v3_lith_prd)
##                -999 1   2   3   <NA>     
## [1,] No. cases 471  32  24  50  966  1543
## [2,] Percent   30.5 2.1 1.6 3.2 62.6 100

Create dataset

v3_med<-data.frame(v3_drugs[,2:6],v3_adv,v3_medchange,v3_lith,v3_lith_prd)

Visit 3: Substance abuse

Tobacco

For more explanation, see Visit 1

“Did you start or stop smoking during the past six months?” (categorical [NS,NN,YSP,YST], v3_smk_strt_stp)

This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.

v3_clin_smk_strt_stp<-rep(NA,dim(v3_clin)[1])
v3_clin_smk_strt_stp<-ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==1,"NS",
                        ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==2,"NN",
                          ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==3,"YSP",
                            ifelse(v3_clin$v3_tabalk1_ta1_jemals_rauch==4,"YST",v3_clin_smk_strt_stp))))
       
#ATTENTION: answering alternative: e-cigarette only in controls
v3_con_smk_strt_stp<-rep(NA,dim(v3_clin)[1])
v3_con_smk_strt_stp<-ifelse(v3_con$v3_tabalk_folge_tabak1==1 | v3_con$v3_tabalk_folge_tabak1==2,"NS",
                        ifelse(v3_con$v3_tabalk_folge_tabak1==3,"NN",
                          ifelse(v3_con$v3_tabalk_folge_tabak1==4,"YSP",
                            ifelse(v3_con$v3_tabalk_folge_tabak1==5,"YST",v3_con_smk_strt_stp))))
                        
v3_smk_strt_stp<-c(v3_clin_smk_strt_stp,v3_con_smk_strt_stp)
descT(v3_smk_strt_stp)
##                NN   NS   YSP YST <NA>     
## [1,] No. cases 269  459  29  9   777  1543
## [2,] Percent   17.4 29.7 1.9 0.6 50.4 100

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v3_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.

v3_no_cig<-c(rep(NA,dim(v3_clin)[1]),rep(NA,dim(v3_con)[1]))

v3_no_cig<-ifelse((v3_smk_strt_stp=="NN" | v3_smk_strt_stp=="YSP"), -999, 
            ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") & 
                     c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==1,                                
                     c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*365,
            ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") & 
                     c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==2,                                
                     c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*52,
            ifelse((v3_smk_strt_stp=="NS" | v3_smk_strt_stp=="YST") & 
                     c(v3_clin$v3_tabalk1_ta3_zig_pro_zeit,v3_con$v3_tabalk_folge_tabak2_zeit)==3,                                
                     c(v3_clin$v3_tabalk1_ta3_anz_zig,v3_con$v3_tabalk_folge_tabak2_anz)*12,
                      v3_no_cig))))

summary(v3_no_cig[v3_no_cig>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0    3650    5475    6322    7300   73000     949

Alcohol

“How often did you consume alcoholic beverages during the past six months?” (ordinal [1,2,3,4,5,6,7], v3_alc_pst6_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v3_alc_pst6_mths<-c(v3_clin$v3_tabalk1_ta9_alkkonsum,v3_con$v3_tabalk_folge_alkohol4)
v3_alc_pst6_mths<-factor(v3_alc_pst6_mths, ordered=T)

descT(v3_alc_pst6_mths)
##                1    2    3   4    5   6   7   <NA>     
## [1,] No. cases 177  157  76  173  109 37  38  776  1543
## [2,] Percent   11.5 10.2 4.9 11.2 7.1 2.4 2.5 50.3 100

“On how many occasions during the past six months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?”" (ordinal [1,2,3,4,5,6,7,8,9], v3_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v3_alc_5orm<-ifelse(v3_alc_pst6_mths<4,-999,
                    ifelse(is.na(c(v3_clin$v3_tabalk1_ta10_alk_haeufigk_m1,v3_con$v3_tabalk_folge_alkohol5))==T,   
                            c(v3_clin$v3_tabalk1_ta11_alk_haeufigk_f1,v3_con$v3_tabalk_folge_alkohol6),
                            c(v3_clin$v3_tabalk1_ta10_alk_haeufigk_m1,v3_con$v3_tabalk_folge_alkohol5)))

v3_alc_5orm<-factor(v3_alc_5orm, ordered=T)

descT(v3_alc_5orm)
##                -999 1   2   3   4   5   6   7   8   9   <NA>     
## [1,] No. cases 410  148 67  43  26  12  33  14  1   8   781  1543
## [2,] Percent   26.6 9.6 4.3 2.8 1.7 0.8 2.1 0.9 0.1 0.5 50.6 100

Illicit drugs

For more information see in visit 1 and 2.

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v3_pst6_ill_drg)

v3_pst6_ill_drg<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_pst6_ill_drg<-ifelse(c(v3_clin$v3_drogen1_dg1_konsum,v3_con$v3_drogen_folge_drogenkonsum)==2, "Y", "N")

descT(v3_pst6_ill_drg)
##                N    Y  <NA>     
## [1,] No. cases 703  62 778  1543
## [2,] Percent   45.6 4  50.4 100

Create dataset

v3_subst<-data.frame(v3_smk_strt_stp,
                     v3_no_cig,
                     v3_alc_pst6_mths,
                     v3_alc_5orm,
                     v3_pst6_ill_drg)

Visit 3: Symptom rating scales (interviewer rates patient)

PANSS

For more information on the scale, please see Visit 1

Positive subscale

P1 Delusions (ordinal [1,2,3,4,5,6,7], v3_panss_p1)

v3_panss_p1<-c(v3_clin$v3_panss_p_p1_wahnideen,v3_con$v3_panss_p_p1_wahnideen)
v3_panss_p1<-factor(v3_panss_p1, ordered=T)

descT(v3_panss_p1)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 564  35  59  24  14  6   1   840  1543
## [2,] Percent   36.6 2.3 3.8 1.6 0.9 0.4 0.1 54.4 100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v3_panss_p2)

v3_panss_p2<-c(v3_clin$v3_panss_p_p2_form_denkst,v3_con$v3_panss_p_p2_form_denkst)
v3_panss_p2<-factor(v3_panss_p2, ordered=T)

descT(v3_panss_p2)
##                1    2   3   4   5   7   <NA>     
## [1,] No. cases 511  65  81  37  9   1   839  1543
## [2,] Percent   33.1 4.2 5.2 2.4 0.6 0.1 54.4 100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v3_panss_p3)

v3_panss_p3<-c(v3_clin$v3_panss_p_p3_halluz,v3_con$v3_panss_p_p3_halluz)
v3_panss_p3<-factor(v3_panss_p3, ordered=T)

descT(v3_panss_p3)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 626  22  26  17  10  3   839  1543
## [2,] Percent   40.6 1.4 1.7 1.1 0.6 0.2 54.4 100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v3_panss_p4)

v3_panss_p4<-c(v3_clin$v3_panss_p_p4_erregung,v3_con$v3_panss_p_p4_erregung)
v3_panss_p4<-factor(v3_panss_p4, ordered=T)

descT(v3_panss_p4)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 527  52  99  23  3   839  1543
## [2,] Percent   34.2 3.4 6.4 1.5 0.2 54.4 100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v3_panss_p5)

v3_panss_p5<-c(v3_clin$v3_panss_p_p5_groessenideen,v3_con$v3_panss_p_p5_groessenideen)
v3_panss_p5<-factor(v3_panss_p5, ordered=T)

descT(v3_panss_p5)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 646  21  24  10  3   839  1543
## [2,] Percent   41.9 1.4 1.6 0.6 0.2 54.4 100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v3_panss_p6)

v3_panss_p6<-c(v3_clin$v3_panss_p_p6_misstr_verfolg,v3_con$v3_panss_p_p6_misstr_verfolg)
v3_panss_p6<-factor(v3_panss_p6, ordered=T)

descT(v3_panss_p6)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 557  44  64  23  13  3   839  1543
## [2,] Percent   36.1 2.9 4.1 1.5 0.8 0.2 54.4 100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v3_panss_p7)

v3_panss_p7<-c(v3_clin$v3_panss_p_p7_feindseligkeit,v3_con$v3_panss_p_p7_feindseligkeit)
v3_panss_p7<-factor(v3_panss_p7, ordered=T)

descT(v3_panss_p7)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 642  32  25  3   1   1   839  1543
## [2,] Percent   41.6 2.1 1.6 0.2 0.1 0.1 54.4 100

PANSS Positive sum score (continuous [7-49], v3_panss_sum_pos)

v3_panss_sum_pos<-as.numeric.factor(v3_panss_p1)+
                  as.numeric.factor(v3_panss_p2)+
                  as.numeric.factor(v3_panss_p3)+
                  as.numeric.factor(v3_panss_p4)+
                  as.numeric.factor(v3_panss_p5)+
                  as.numeric.factor(v3_panss_p6)+
                  as.numeric.factor(v3_panss_p7)

summary(v3_panss_sum_pos)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   7.000   7.000   7.000   9.457  11.000  30.000     840

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v3_panss_n1)

v3_panss_n1<-c(v3_clin$v3_panss_n_n1_affektverflachung,v3_con$v3_panss_n_n1_affektverflachung)
v3_panss_n1<-factor(v3_panss_n1, ordered=T)

descT(v3_panss_n1)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 442  74  97  51  32  2   845  1543
## [2,] Percent   28.6 4.8 6.3 3.3 2.1 0.1 54.8 100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v3_panss_n2)

v3_panss_n2<-c(v3_clin$v3_panss_n_n2_emot_rueckzug,v3_con$v3_panss_n_n2_emot_rueckzug)
v3_panss_n2<-factor(v3_panss_n2, ordered=T)

descT(v3_panss_n2)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 507  67  71  44  12  1   841  1543
## [2,] Percent   32.9 4.3 4.6 2.9 0.8 0.1 54.5 100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v3_panss_n3)

v3_panss_n3<-c(v3_clin$v3_panss_n_n3_mang_aff_rapp,v3_con$v3_panss_n_n3_mang_aff_rapp)
v3_panss_n3<-factor(v3_panss_n3, ordered=T)

descT(v3_panss_n3)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 534  66  73  24  5   2   839  1543
## [2,] Percent   34.6 4.3 4.7 1.6 0.3 0.1 54.4 100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v3_panss_n4)

v3_panss_n4<-c(v3_clin$v3_panss_n_n4_soz_pass_apath,v3_con$v3_panss_n_n4_soz_pass_apath)
v3_panss_n4<-factor(v3_panss_n4, ordered=T)

descT(v3_panss_n4)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 510  58  97  25  10  4   839  1543
## [2,] Percent   33.1 3.8 6.3 1.6 0.6 0.3 54.4 100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v3_panss_n5)

v3_panss_n5<-c(v3_clin$v3_panss_n_n5_abstr_denken,v3_con$v3_panss_n_n5_abstr_denken)
v3_panss_n5<-factor(v3_panss_n5, ordered=T)

descT(v3_panss_n5)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 442  79  118 48  9   4   843  1543
## [2,] Percent   28.6 5.1 7.6 3.1 0.6 0.3 54.6 100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v3_panss_n6)

v3_panss_n6<-c(v3_clin$v3_panss_n_n6_spon_fl_sprache,v3_con$v3_panss_n_n6_spon_fl_sprache)
v3_panss_n6<-factor(v3_panss_n6, ordered=T)

descT(v3_panss_n6)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 572  40  60  23  6   2   840  1543
## [2,] Percent   37.1 2.6 3.9 1.5 0.4 0.1 54.4 100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v3_panss_n7)

v3_panss_n7<-c(v3_clin$v3_panss_n_n7_stereotyp_ged,v3_con$v3_panss_n_n7_stereotyp_ged)
v3_panss_n7<-factor(v3_panss_n7, ordered=T)

descT(v3_panss_n7)
##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 581  47 57  12  3   1   842  1543
## [2,] Percent   37.7 3  3.7 0.8 0.2 0.1 54.6 100

PANSS Negative sum score (continuous [7-49], v3_panss_sum_neg)

v3_panss_sum_neg<-as.numeric.factor(v3_panss_n1)+
                  as.numeric.factor(v3_panss_n2)+
                  as.numeric.factor(v3_panss_n3)+
                  as.numeric.factor(v3_panss_n4)+
                  as.numeric.factor(v3_panss_n5)+
                  as.numeric.factor(v3_panss_n6)+
                  as.numeric.factor(v3_panss_n7)

summary(v3_panss_sum_neg)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00    7.00    9.00   10.79   13.00   34.00     854

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v3_panss_g1)

v3_panss_g1<-c(v3_clin$v3_panss_g_g1_sorge_gesundh,v3_con$v3_panss_g_g1_sorge_gesundh)
v3_panss_g1<-factor(v3_panss_g1, ordered=T)

descT(v3_panss_g1)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 514  80  73  25  5   2   844  1543
## [2,] Percent   33.3 5.2 4.7 1.6 0.3 0.1 54.7 100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v3_panss_g2)

v3_panss_g2<-c(v3_clin$v3_panss_g_g2_angst,v3_con$v3_panss_g_g2_angst)
v3_panss_g2<-factor(v3_panss_g2, ordered=T)

descT(v3_panss_g2)
##                1    2   3   4   5  6   7   <NA>     
## [1,] No. cases 482  49  122 30  16 1   1   842  1543
## [2,] Percent   31.2 3.2 7.9 1.9 1  0.1 0.1 54.6 100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v3_panss_g3)

v3_panss_g3<-c(v3_clin$v3_panss_g_g3_schuldgefuehle,v3_con$v3_panss_g_g3_schuldgefuehle)
v3_panss_g3<-factor(v3_panss_g3, ordered=T)

descT(v3_panss_g3)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 565  34  63  28  12  841  1543
## [2,] Percent   36.6 2.2 4.1 1.8 0.8 54.5 100

G4 Tension (ordinal [1,2,3,4,5,6,7], v3_panss_g4)

v3_panss_g4<-c(v3_clin$v3_panss_g_g4_anspannung,v3_con$v3_panss_g_g4_anspannung)
v3_panss_g4<-factor(v3_panss_g4, ordered=T)

descT(v3_panss_g4)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 486  73  94  41  6   2   841  1543
## [2,] Percent   31.5 4.7 6.1 2.7 0.4 0.1 54.5 100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v3_panss_g5)

v3_panss_g5<-c(v3_clin$v3_panss_g_g5_manier_koerperh,v3_con$v3_panss_g_g5_manier_koerperh)
v3_panss_g5<-factor(v3_panss_g5, ordered=T)

descT(v3_panss_g5)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 638  36  19  4   3   2   841  1543
## [2,] Percent   41.3 2.3 1.2 0.3 0.2 0.1 54.5 100

G6 Depression (ordinal [1,2,3,4,5,6,7], v3_panss_g6)

v3_panss_g6<-c(v3_clin$v3_panss_g_g6_depression,v3_con$v3_panss_g_g6_depression)
v3_panss_g6<-factor(v3_panss_g6, ordered=T)

descT(v3_panss_g6)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 440  53  115 65  26  3   1   840  1543
## [2,] Percent   28.5 3.4 7.5 4.2 1.7 0.2 0.1 54.4 100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v3_panss_g7)

v3_panss_g7<-c(v3_clin$v3_panss_g_g7_mot_verlangs,v3_con$v3_panss_g_g7_mot_verlangs)
v3_panss_g7<-factor(v3_panss_g7, ordered=T)

descT(v3_panss_g7)
##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 506  61 98  34  4   1   839  1543
## [2,] Percent   32.8 4  6.4 2.2 0.3 0.1 54.4 100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v3_panss_g8)

v3_panss_g8<-c(v3_clin$v3_panss_g_g8_unkoop_verh,v3_con$v3_panss_g_g8_unkoop_verh)
v3_panss_g8<-factor(v3_panss_g8, ordered=T)

descT(v3_panss_g8)
##                1    2   3   4   6   <NA>     
## [1,] No. cases 656  18  24  2   2   841  1543
## [2,] Percent   42.5 1.2 1.6 0.1 0.1 54.5 100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v3_panss_g9)

v3_panss_g9<-c(v3_clin$v3_panss_g_g9_ungew_denkinh,v3_con$v3_panss_g_g9_ungew_denkinh)
v3_panss_g9<-factor(v3_panss_g9, ordered=T)

descT(v3_panss_g9)
##                1    2  3  4   5   6   7   <NA>     
## [1,] No. cases 568  47 62 18  6   2   1   839  1543
## [2,] Percent   36.8 3  4  1.2 0.4 0.1 0.1 54.4 100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v3_panss_g10)

v3_panss_g10<-c(v3_clin$v3_panss_g_g10_desorient,v3_con$v3_panss_g_g10_desorient)
v3_panss_g10<-factor(v3_panss_g10, ordered=T)

descT(v3_panss_g10)
##                1    2   3   4   <NA>     
## [1,] No. cases 654  28  19  1   841  1543
## [2,] Percent   42.4 1.8 1.2 0.1 54.5 100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v3_panss_g11)

v3_panss_g11<-c(v3_clin$v3_panss_g_g11_mang_aufmerks,v3_con$v3_panss_g_g11_mang_aufmerks)
v3_panss_g11<-factor(v3_panss_g11, ordered=T)

descT(v3_panss_g11)
##                1    2   3   4  5   6   <NA>     
## [1,] No. cases 446  71  135 46 3   1   841  1543
## [2,] Percent   28.9 4.6 8.7 3  0.2 0.1 54.5 100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v3_panss_g12)

v3_panss_g12<-c(v3_clin$v3_panss_g_g12_mang_urt_einsi,v3_con$v3_panss_g_g12_mang_urt_einsi)

v3_panss_g12<-factor(v3_panss_g12, ordered=T)
descT(v3_panss_g12)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 596  40  39  20  4   3   1   840  1543
## [2,] Percent   38.6 2.6 2.5 1.3 0.3 0.2 0.1 54.4 100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v3_panss_g13)

v3_panss_g13<-c(v3_clin$v3_panss_g_g13_willensschwae,v3_con$v3_panss_g_g13_willensschwae)
v3_panss_g13<-factor(v3_panss_g13, ordered=T)

descT(v3_panss_g13)
##                1    2   3   4   <NA>     
## [1,] No. cases 622  22  37  22  840  1543
## [2,] Percent   40.3 1.4 2.4 1.4 54.4 100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v3_panss_g14)

v3_panss_g14<-c(v3_clin$v3_panss_g_g14_mang_impulsk,v3_con$v3_panss_g_g14_mang_impulsk)
v3_panss_g14<-factor(v3_panss_g14, ordered=T)

descT(v3_panss_g14)
##                1    2   3  4   <NA>     
## [1,] No. cases 606  28  62 7   840  1543
## [2,] Percent   39.3 1.8 4  0.5 54.4 100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v3_panss_g15)

v3_panss_g15<-c(v3_clin$v3_panss_g_g15_selbstbezog,v3_con$v3_panss_g_g15_selbstbezog)
v3_panss_g15<-factor(v3_panss_g15, ordered=T)

descT(v3_panss_g15)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 625  42  25  9   3   839  1543
## [2,] Percent   40.5 2.7 1.6 0.6 0.2 54.4 100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v3_panss_g16)

v3_panss_g16<-c(v3_clin$v3_panss_g_g16_aktsoz_vermeid,v3_con$v3_panss_g_g16_aktsoz_vermeid)
v3_panss_g16<-factor(v3_panss_g16, ordered=T)

descT(v3_panss_g16)
##                1    2   3  4   5   <NA>     
## [1,] No. cases 576  40  61 17  10  839  1543
## [2,] Percent   37.3 2.6 4  1.1 0.6 54.4 100

PANSS General Psychopathology sum score (continuous [16-112], v3_panss_sum_gen)

v3_panss_sum_gen<-as.numeric.factor(v3_panss_g1)+
                  as.numeric.factor(v3_panss_g2)+
                  as.numeric.factor(v3_panss_g3)+
                  as.numeric.factor(v3_panss_g4)+
                  as.numeric.factor(v3_panss_g5)+
                  as.numeric.factor(v3_panss_g6)+
                  as.numeric.factor(v3_panss_g7)+
                  as.numeric.factor(v3_panss_g8)+
                  as.numeric.factor(v3_panss_g9)+
                  as.numeric.factor(v3_panss_g10)+
                  as.numeric.factor(v3_panss_g11)+
                  as.numeric.factor(v3_panss_g12)+
                  as.numeric.factor(v3_panss_g13)+
                  as.numeric.factor(v3_panss_g14)+
                  as.numeric.factor(v3_panss_g15)+
                  as.numeric.factor(v3_panss_g16)

summary(v3_panss_sum_gen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   16.00   16.00   20.00   22.34   26.00   56.00     860

Create PANSS Total score (continuous [30-210], v3_panss_sum_tot)

v3_panss_sum_tot<-v3_panss_sum_pos+v3_panss_sum_neg+v3_panss_sum_gen
summary(v3_panss_sum_tot)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   31.00   38.00   42.65   51.00  112.00     874

Create dataset

v3_symp_panss<-data.frame(v3_panss_p1,v3_panss_p2,v3_panss_p3,v3_panss_p4,v3_panss_p5,v3_panss_p6,v3_panss_p7,
                          v3_panss_n1,v3_panss_n2,v3_panss_n3,v3_panss_n4,v3_panss_n5,v3_panss_n6,v3_panss_n7,
                          v3_panss_g1,v3_panss_g2,v3_panss_g3,v3_panss_g4,v3_panss_g5,v3_panss_g6,v3_panss_g7,
                          v3_panss_g8,v3_panss_g9,v3_panss_g10,v3_panss_g11,v3_panss_g12,v3_panss_g13,v3_panss_g14,
                          v3_panss_g15,v3_panss_g16,v3_panss_sum_pos,v3_panss_sum_neg,v3_panss_sum_gen,
                          v3_panss_sum_tot)

IDS-C30

For more information on the scale, please see Visit 1

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v3_idsc_itm1)

v3_idsc_itm1<-c(v3_clin$v3_ids_c_s1_ids1_einschlafschw,v3_con$v3_ids_c_s1_ids1_einschlafschw)
v3_idsc_itm1<-factor(v3_idsc_itm1, ordered=T)

descT(v3_idsc_itm1)
##                0    1   2  3   <NA>     
## [1,] No. cases 507  83  62 45  846  1543
## [2,] Percent   32.9 5.4 4  2.9 54.8 100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v3_idsc_itm2)

v3_idsc_itm2<-c(v3_clin$v3_ids_c_s1_ids2_naechtl_aufw,v3_con$v3_ids_c_s1_ids2_naechtl_aufw)
v3_idsc_itm2<-factor(v3_idsc_itm2, ordered=T)

descT(v3_idsc_itm2)
##                0    1   2   3   <NA>     
## [1,] No. cases 440  120 83  55  845  1543
## [2,] Percent   28.5 7.8 5.4 3.6 54.8 100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v3_idsc_itm3)

v3_idsc_itm3<-c(v3_clin$v3_ids_c_s1_ids3_frueh_aufw,v3_con$v3_ids_c_s1_ids3_frueh_aufw)
v3_idsc_itm3<-factor(v3_idsc_itm3, ordered=T)

descT(v3_idsc_itm3)
##                0    1   2   3   <NA>     
## [1,] No. cases 584  53  30  30  846  1543
## [2,] Percent   37.8 3.4 1.9 1.9 54.8 100

Item 4 Hypersomnia (ordinal [0,1,2,3], v3_idsc_itm4)

v3_idsc_itm4<-c(v3_clin$v3_ids_c_s1_ids4_hypersomnie,v3_con$v3_ids_c_s1_ids4_hypersomnie)
v3_idsc_itm4<-factor(v3_idsc_itm4, ordered=T)

descT(v3_idsc_itm4)
##                0    1    2   3   <NA>     
## [1,] No. cases 464  161  56  17  845  1543
## [2,] Percent   30.1 10.4 3.6 1.1 54.8 100

Item 5 Mood (sad) (ordinal [0,1,2,3], v3_idsc_itm5)

v3_idsc_itm5<-c(v3_clin$v3_ids_c_s1_ids5_stimmung_trgk,v3_con$v3_ids_c_s1_ids5_stimmung_trgk)
v3_idsc_itm5<-factor(v3_idsc_itm5, ordered=T)

descT(v3_idsc_itm5)
##                0    1   2   3   <NA>     
## [1,] No. cases 465  152 54  26  846  1543
## [2,] Percent   30.1 9.9 3.5 1.7 54.8 100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v3_idsc_itm6)

v3_idsc_itm6<-c(v3_clin$v3_ids_c_s1_ids6_stimmung_grzt,v3_con$v3_ids_c_s1_ids6_stimmung_grzt)
v3_idsc_itm6<-factor(v3_idsc_itm6, ordered=T)

descT(v3_idsc_itm6)
##                0    1    2   3   <NA>     
## [1,] No. cases 466  179  39  13  846  1543
## [2,] Percent   30.2 11.6 2.5 0.8 54.8 100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v3_idsc_itm7)

v3_idsc_itm7<-c(v3_clin$v3_ids_c_s1_ids7_stimmung_agst,v3_con$v3_ids_c_s1_ids7_stimmung_agst)
v3_idsc_itm7<-factor(v3_idsc_itm7, ordered=T)

descT(v3_idsc_itm7)
##                0   1   2   3   <NA>     
## [1,] No. cases 494 128 51  24  846  1543
## [2,] Percent   32  8.3 3.3 1.6 54.8 100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v3_idsc_itm8)

v3_idsc_itm8<-c(v3_clin$v3_ids_c_s1_ids8_reakt_stimmung,v3_con$v3_ids_c_s1_ids8_reakt_stimmung)
v3_idsc_itm8<-factor(v3_idsc_itm8, ordered=T)

descT(v3_idsc_itm8)
##                0    1   2   3   <NA>     
## [1,] No. cases 579  79  20  18  847  1543
## [2,] Percent   37.5 5.1 1.3 1.2 54.9 100

Item 9 Mood Variation (ordinal [0,1,2,3], v3_idsc_itm9)

v3_idsc_itm9<-c(v3_clin$v3_ids_c_s1_ids9_stimmungsschw,v3_con$v3_ids_c_s1_ids9_stimmungsschw)
v3_idsc_itm9<-factor(v3_idsc_itm9, ordered=T)

descT(v3_idsc_itm9)
##                0    1   2   3   <NA>     
## [1,] No. cases 549  64  24  59  847  1543
## [2,] Percent   35.6 4.1 1.6 3.8 54.9 100

Item 9A (categorical [M, A, N], v3_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v3_idsc_itm9a_pre<-c(v3_clin$v3_ids_c_s1_ids9a_stimmungsschw,v3_con$v3_ids_c_s1_ids9a_stimmungsschw)

v3_idsc_itm9a<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==1, "M", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==2, "A", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9a_pre==3, "N", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9a))
v3_idsc_itm9a<-factor(v3_idsc_itm9a, ordered=F)

descT(v3_idsc_itm9a)
##                -999 A  M   N   <NA>     
## [1,] No. cases 549  16 75  28  875  1543
## [2,] Percent   35.6 1  4.9 1.8 56.7 100

Item 9B (dichotomous, v3_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v3_idsc_itm9b_pre<-c(v3_clin$v3_ids_c_s1_ids9b_stimmungsschw,v3_con$v3_ids_c_s1_ids9b_stimmungsschw)

v3_idsc_itm9b<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm9b<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9b_pre==0, "N", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9b))
v3_idsc_itm9b<-ifelse(v3_idsc_itm9!=0 & v3_idsc_itm9b_pre==1, "Y", ifelse(v3_idsc_itm9==0, -999, v3_idsc_itm9b))
v3_idsc_itm9b<-factor(v3_idsc_itm9b, ordered=F)

descT(v3_idsc_itm9b)
##                -999 N   Y   <NA>     
## [1,] No. cases 549  35  65  894  1543
## [2,] Percent   35.6 2.3 4.2 57.9 100

Item 10 Quality of mood (ordinal [0,1,2,3], v3_idsc_itm10)

v3_idsc_itm10<-c(v3_clin$v3_ids_c_s1_ids10_quali_stimmung,v3_con$v3_ids_c_s1_ids10_quali_stimmung)
v3_idsc_itm10<-factor(v3_idsc_itm10, ordered=T)

descT(v3_idsc_itm10)
##                0    1   2  3   <NA>     
## [1,] No. cases 614  44  15 23  847  1543
## [2,] Percent   39.8 2.9 1  1.5 54.9 100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v3_idsc_itm11)

v3_idsc_app_verm<-c(v3_clin$v3_ids_c_s2_ids11_appetit_verm,v3_con$v3_ids_c_s2_ids11_appetit_verm)
v3_idsc_app_gest<-c(v3_clin$v3_ids_c_s2_ids12_appetit_steig,v3_con$v3_ids_c_s2_ids12_appetit_steig)

v3_idsc_itm11<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm11<-ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==T,          
                         v3_idsc_app_verm, 
                      ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &    
                     (v3_idsc_app_verm>v3_idsc_app_gest), v3_idsc_app_verm,                                            ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &                                                         (v3_idsc_app_gest>=v3_idsc_app_verm),-999,v3_idsc_itm11)))))

#Important: do not code as factor, see after calculation of sum score!
descT(v3_idsc_itm11)
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 213  401 61 19  5   844  1543
## [2,] Percent   13.8 26  4  1.2 0.3 54.7 100

Item 12 (ordinal [0,1,2,3], v3_idsc_itm12)

v3_idsc_itm12<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm12<-ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v3_idsc_app_verm)==T & is.na(v3_idsc_app_gest)==F,    
                         v3_idsc_app_gest,                
                  ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==T,          
                         -999, 
                      ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &    
                     (v3_idsc_app_verm>v3_idsc_app_gest), -999,                                            
                     ifelse(is.na(v3_idsc_app_verm)==F & is.na(v3_idsc_app_gest)==F &                                                         (v3_idsc_app_gest>=v3_idsc_app_verm),
                            v3_idsc_app_gest,v3_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v3_idsc_itm12)
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 486  110 67  21  15 844  1543
## [2,] Percent   31.5 7.1 4.3 1.4 1  54.7 100

Item 13 (ordinal [0,1,2,3], v3_idsc_itm13)

v3_idsc_gew_abn<-c(v3_clin$v3_ids_c_s2_ids13_gewichtsabn,v3_con$v3_ids_c_s2_ids13_gewichtsabn)
v3_idsc_gew_zun<-c(v3_clin$v3_ids_c_s2_ids14_gewichtszun,v3_con$v3_ids_c_s2_ids14_gewichtszun)

v3_idsc_itm13<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm13<-ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==T,          
                         v3_idsc_gew_abn, 
                      ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &    
                     (v3_idsc_gew_abn>v3_idsc_gew_zun), v3_idsc_gew_abn,                                            ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F & (v3_idsc_gew_zun >= v3_idsc_gew_abn),-999,v3_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v3_idsc_itm13)
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 232  369  41  39  18  844  1543
## [2,] Percent   15   23.9 2.7 2.5 1.2 54.7 100

Item 14 (ordinal [0,1,2,3], v3_idsc_itm14)

v3_idsc_itm14<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_idsc_itm14<-ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v3_idsc_gew_abn)==T & is.na(v3_idsc_gew_zun)==F,    
                         v3_idsc_gew_zun,                
                  ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==T,          
                         -999, 
                      ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &    
                     (v3_idsc_gew_abn>v3_idsc_gew_zun), -999,                                            
                     ifelse(is.na(v3_idsc_gew_abn)==F & is.na(v3_idsc_gew_zun)==F &                                                         (v3_idsc_gew_zun>=v3_idsc_gew_abn),
                            v3_idsc_gew_zun,v3_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v3_idsc_itm14)
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 467  138 52  23  19  844  1543
## [2,] Percent   30.3 8.9 3.4 1.5 1.2 54.7 100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v3_idsc_itm15)

v3_idsc_itm15<-c(v3_clin$v3_ids_c_s2_ids15_konz_entscheid,v3_con$v3_ids_c_s2_ids15_konz_entscheid)
v3_idsc_itm15<-factor(v3_idsc_itm15, ordered=T)

descT(v3_idsc_itm15)
##                0    1   2   3   <NA>     
## [1,] No. cases 410  185 91  13  844  1543
## [2,] Percent   26.6 12  5.9 0.8 54.7 100

Item 16 Outlook (self) (ordinal [0,1,2,3], v3_idsc_itm16)

v3_idsc_itm16<-c(v3_clin$v3_ids_c_s2_ids16_selbstbild,v3_con$v3_ids_c_s2_ids16_selbstbild)
v3_idsc_itm16<-factor(v3_idsc_itm16, ordered=T)

descT(v3_idsc_itm16)
##                0    1   2   3   <NA>     
## [1,] No. cases 536  109 23  33  842  1543
## [2,] Percent   34.7 7.1 1.5 2.1 54.6 100

Item 17 Outlook (future) (ordinal [0,1,2,3], v3_idsc_itm17)

v3_idsc_itm17<-c(v3_clin$v3_ids_c_s2_ids17_zukunftssicht,v3_con$v3_ids_c_s2_ids17_zukunftssicht)
v3_idsc_itm17<-factor(v3_idsc_itm17, ordered=T)

descT(v3_idsc_itm17)
##                0    1    2   3   <NA>     
## [1,] No. cases 477  168  43  10  845  1543
## [2,] Percent   30.9 10.9 2.8 0.6 54.8 100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v3_idsc_itm18)

v3_idsc_itm18<-c(v3_clin$v3_ids_c_s2_ids18_selbstmordged,v3_con$v3_ids_c_s2_ids18_selbstmordged)
v3_idsc_itm18<-factor(v3_idsc_itm18, ordered=T)

descT(v3_idsc_itm18)
##                0    1   2   <NA>     
## [1,] No. cases 635  35  29  844  1543
## [2,] Percent   41.2 2.3 1.9 54.7 100

Item 19 Involvement (ordinal [0,1,2,3], v3_idsc_itm19)

v3_idsc_itm19<-c(v3_clin$v3_ids_c_s2_ids19_interess_aktiv,v3_con$v3_ids_c_s2_ids19_interess_aktiv)
v3_idsc_itm19<-factor(v3_idsc_itm19, ordered=T)

descT(v3_idsc_itm19)
##                0    1   2   3   <NA>     
## [1,] No. cases 583  89  14  12  845  1543
## [2,] Percent   37.8 5.8 0.9 0.8 54.8 100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v3_idsc_itm20)

v3_idsc_itm20<-c(v3_clin$v3_ids_c_s2_ids20_energ_ermued,v3_con$v3_ids_c_s2_ids20_energ_ermued)
v3_idsc_itm20<-factor(v3_idsc_itm20, ordered=T)

descT(v3_idsc_itm20)
##                0    1   2   3   <NA>     
## [1,] No. cases 473  147 73  8   842  1543
## [2,] Percent   30.7 9.5 4.7 0.5 54.6 100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v3_idsc_itm21)

v3_idsc_itm21<-c(v3_clin$v3_ids_c_s3_ids21_vergn_genuss,v3_con$v3_ids_c_s3_ids21_vergn_genuss)
v3_idsc_itm21<-factor(v3_idsc_itm21, ordered=T)

descT(v3_idsc_itm21)
##                0    1   2  3   <NA>     
## [1,] No. cases 599  78  15 8   843  1543
## [2,] Percent   38.8 5.1 1  0.5 54.6 100

Item 22 Sexual interest (ordinal [0,1,2,3], v3_idsc_itm22)

v3_idsc_itm22<-c(v3_clin$v3_ids_c_s3_ids22_sex_interesse,v3_con$v3_ids_c_s3_ids22_sex_interesse)
v3_idsc_itm22<-factor(v3_idsc_itm22, ordered=T)

descT(v3_idsc_itm22)
##                0    1   2   3   <NA>     
## [1,] No. cases 521  53  68  53  848  1543
## [2,] Percent   33.8 3.4 4.4 3.4 55   100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v3_idsc_itm23)

v3_idsc_itm23<-c(v3_clin$v3_ids_c_s3_ids23_psymo_hemm,v3_con$v3_ids_c_s3_ids23_psymo_hemm)
v3_idsc_itm23<-factor(v3_idsc_itm23, ordered=T)

descT(v3_idsc_itm23)
##                0    1   2   3   <NA>     
## [1,] No. cases 567  115 17  2   842  1543
## [2,] Percent   36.7 7.5 1.1 0.1 54.6 100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v3_idsc_itm24)

v3_idsc_itm24<-c(v3_clin$v3_ids_c_s3_ids24_psymo_agitht,v3_con$v3_ids_c_s3_ids24_psymo_agitht)
v3_idsc_itm24<-factor(v3_idsc_itm24, ordered=T)

descT(v3_idsc_itm24)
##                0    1   2   3   <NA>     
## [1,] No. cases 562  94  34  4   849  1543
## [2,] Percent   36.4 6.1 2.2 0.3 55   100

Item 25 Somatic complaints (ordinal [0,1,2,3], v3_idsc_itm25)

v3_idsc_itm25<-c(v3_clin$v3_ids_c_s3_ids25_som_beschw,v3_con$v3_ids_c_s3_ids25_som_beschw)
v3_idsc_itm25<-factor(v3_idsc_itm25, ordered=T)

descT(v3_idsc_itm25)
##                0    1   2   3   <NA>     
## [1,] No. cases 483  169 35  14  842  1543
## [2,] Percent   31.3 11  2.3 0.9 54.6 100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v3_idsc_itm26)

v3_idsc_itm26<-c(v3_clin$v3_ids_c_s3_ids26_veg_erreg,v3_con$v3_ids_c_s3_ids26_veg_erreg)
v3_idsc_itm26<-factor(v3_idsc_itm26, ordered=T)

descT(v3_idsc_itm26)
##                0    1   2   3   <NA>     
## [1,] No. cases 510  144 34  11  844  1543
## [2,] Percent   33.1 9.3 2.2 0.7 54.7 100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v3_idsc_itm27)

v3_idsc_itm27<-c(v3_clin$v3_ids_c_s3_ids27_panik_phob,v3_con$v3_ids_c_s3_ids27_panik_phob)
v3_idsc_itm27<-factor(v3_idsc_itm27, ordered=T)

descT(v3_idsc_itm27)
##                0    1   2   3   <NA>     
## [1,] No. cases 623  42  22  11  845  1543
## [2,] Percent   40.4 2.7 1.4 0.7 54.8 100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v3_idsc_itm28)

v3_idsc_itm28<-c(v3_clin$v3_ids_c_s3_ids28_verdauung,v3_con$v3_ids_c_s3_ids28_verdauung)
v3_idsc_itm28<-factor(v3_idsc_itm28, ordered=T)

descT(v3_idsc_itm28)
##                0    1   2   3   <NA>     
## [1,] No. cases 589  71  28  12  843  1543
## [2,] Percent   38.2 4.6 1.8 0.8 54.6 100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v3_idsc_itm29)

v3_idsc_itm29<-c(v3_clin$v3_ids_c_s3_ids29_pers_bezieh,v3_con$v3_ids_c_s3_ids29_pers_bezieh)
v3_idsc_itm29<-factor(v3_idsc_itm29, ordered=T)

descT(v3_idsc_itm29)
##                0   1   2   3   <NA>     
## [1,] No. cases 571 87  27  13  845  1543
## [2,] Percent   37  5.6 1.7 0.8 54.8 100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v3_idsc_itm30)

v3_idsc_itm30<-c(v3_clin$v3_ids_c_s3_ids30_schwgf_k_energ,v3_con$v3_ids_c_s3_ids30_schwgf_k_energ)
v3_idsc_itm30<-factor(v3_idsc_itm30, ordered=T)

descT(v3_idsc_itm30)
##                0    1  2   3   <NA>     
## [1,] No. cases 570  92 28  10  843  1543
## [2,] Percent   36.9 6  1.8 0.6 54.6 100

Create IDS-C30 total score (continuous [0-84], v3_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v3_idsc_sum<-as.numeric.factor(v3_idsc_itm1)+
             as.numeric.factor(v3_idsc_itm2)+
             as.numeric.factor(v3_idsc_itm3)+
             as.numeric.factor(v3_idsc_itm4)+
             as.numeric.factor(v3_idsc_itm5)+
             as.numeric.factor(v3_idsc_itm6)+
             as.numeric.factor(v3_idsc_itm7)+
             as.numeric.factor(v3_idsc_itm8)+
             as.numeric.factor(v3_idsc_itm9)+
             as.numeric.factor(v3_idsc_itm10)+
  
 ifelse(is.na(v3_idsc_itm11)==T & is.na(v3_idsc_itm12)==T, NA, 
        ifelse((v3_idsc_itm11==-999 & v3_idsc_itm12!=-999), v3_idsc_itm12,                
              ifelse((v3_idsc_itm11!=-999 & v3_idsc_itm12==-999),v3_idsc_itm11, NA)))+
  
   ifelse(is.na(v3_idsc_itm13)==T & is.na(v3_idsc_itm14)==T, NA, 
        ifelse((v3_idsc_itm13==-999 & v3_idsc_itm14!=-999), v3_idsc_itm14,                
              ifelse((v3_idsc_itm13!=-999 & v3_idsc_itm14==-999),v3_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v3_idsc_itm15)+
             as.numeric.factor(v3_idsc_itm16)+
             as.numeric.factor(v3_idsc_itm17)+
             as.numeric.factor(v3_idsc_itm18)+
             as.numeric.factor(v3_idsc_itm19)+
             as.numeric.factor(v3_idsc_itm20)+
             as.numeric.factor(v3_idsc_itm21)+
             as.numeric.factor(v3_idsc_itm22)+
             as.numeric.factor(v3_idsc_itm23)+
             as.numeric.factor(v3_idsc_itm24)+
             as.numeric.factor(v3_idsc_itm25)+
             as.numeric.factor(v3_idsc_itm26)+
             as.numeric.factor(v3_idsc_itm27)+
             as.numeric.factor(v3_idsc_itm28)+
             as.numeric.factor(v3_idsc_itm29)+
             as.numeric.factor(v3_idsc_itm30)

summary(v3_idsc_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   3.000   7.000   9.873  14.000  70.000     900

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v3_idsc_itm11<-factor(v3_idsc_itm11,ordered=T)
v3_idsc_itm12<-factor(v3_idsc_itm12,ordered=T)
v3_idsc_itm13<-factor(v3_idsc_itm13,ordered=T)
v3_idsc_itm14<-factor(v3_idsc_itm14,ordered=T)

Create dataset

v3_symp_ids_c<-data.frame(v3_idsc_itm1,v3_idsc_itm2,v3_idsc_itm3,v3_idsc_itm4,v3_idsc_itm5,v3_idsc_itm6,v3_idsc_itm7,
                          v3_idsc_itm8,v3_idsc_itm9,v3_idsc_itm9a,v3_idsc_itm9b,v3_idsc_itm10,v3_idsc_itm11,v3_idsc_itm12,
                          v3_idsc_itm13,v3_idsc_itm14,v3_idsc_itm15,v3_idsc_itm16,v3_idsc_itm17,v3_idsc_itm18,v3_idsc_itm19,
                          v3_idsc_itm20,v3_idsc_itm21,v3_idsc_itm22,v3_idsc_itm23,v3_idsc_itm24,v3_idsc_itm25,v3_idsc_itm26,
                          v3_idsc_itm27,v3_idsc_itm28,v3_idsc_itm29,v3_idsc_itm30,v3_idsc_sum)

YMRS

For more information on the scale, please see Visit 1

Item 1 Elevated mood (ordinal [0,1,2,3,4], v3_ymrs_itm1)

v3_ymrs_itm1<-c(v3_clin$v3_ymrs_ymrs1_gehob_stimm,v3_con$v3_ymrs_ymrs1_gehob_stimm)
v3_ymrs_itm1<-factor(v3_ymrs_itm1, ordered=T)

descT(v3_ymrs_itm1)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 583  83  32  1   1   843  1543
## [2,] Percent   37.8 5.4 2.1 0.1 0.1 54.6 100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v3_ymrs_itm2)

v3_ymrs_itm2<-c(v3_clin$v3_ymrs_ymrs2_gest_aktiv,v3_con$v3_ymrs_ymrs2_gest_aktiv)
v3_ymrs_itm2<-factor(v3_ymrs_itm2, ordered=T)

descT(v3_ymrs_itm2)
##                0   1   2   3   <NA>     
## [1,] No. cases 602 66  25  6   844  1543
## [2,] Percent   39  4.3 1.6 0.4 54.7 100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v3_ymrs_itm3)

v3_ymrs_itm3<-c(v3_clin$v3_ymrs_ymrs3_sex_interesse,v3_con$v3_ymrs_ymrs3_sex_interesse)
v3_ymrs_itm3<-factor(v3_ymrs_itm3, ordered=T)

descT(v3_ymrs_itm3)
##                0   1  2   3   <NA>     
## [1,] No. cases 663 16 18  2   844  1543
## [2,] Percent   43  1  1.2 0.1 54.7 100

Item 4 Sleep (ordinal [0,1,2,3,4], v3_ymrs_itm4)

v3_ymrs_itm4<-c(v3_clin$v3_ymrs_ymrs4_schlaf,v3_con$v3_ymrs_ymrs4_schlaf)
v3_ymrs_itm4<-factor(v3_ymrs_itm4, ordered=T)

descT(v3_ymrs_itm4)
##                0    1  2   3   <NA>     
## [1,] No. cases 642  31 18  9   843  1543
## [2,] Percent   41.6 2  1.2 0.6 54.6 100

Item 5 Irritability (ordinal [0,2,4,6,8], v3_ymrs_itm5)

v3_ymrs_itm5<-c(v3_clin$v3_ymrs_ymrs5_reizbarkeit,v3_con$v3_ymrs_ymrs5_reizbarkeit)
v3_ymrs_itm5<-factor(v3_ymrs_itm5, ordered=T)

descT(v3_ymrs_itm5)
##                0    2   4   6   <NA>     
## [1,] No. cases 581  106 13  1   842  1543
## [2,] Percent   37.7 6.9 0.8 0.1 54.6 100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v3_ymrs_itm6)

v3_ymrs_itm6<-c(v3_clin$v3_ymrs_ymrs6_sprechweise,v3_con$v3_ymrs_ymrs6_sprechweise)
v3_ymrs_itm6<-factor(v3_ymrs_itm6, ordered=T)

descT(v3_ymrs_itm6)
##                0    2   4   6   <NA>     
## [1,] No. cases 594  49  48  8   844  1543
## [2,] Percent   38.5 3.2 3.1 0.5 54.7 100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v3_ymrs_itm7)

v3_ymrs_itm7<-c(v3_clin$v3_ymrs_ymrs7_sprachstoer,v3_con$v3_ymrs_ymrs7_sprachstoer)
v3_ymrs_itm7<-factor(v3_ymrs_itm7, ordered=T)

descT(v3_ymrs_itm7)
##                0    1   2   3   <NA>     
## [1,] No. cases 615  63  17  4   844  1543
## [2,] Percent   39.9 4.1 1.1 0.3 54.7 100

Item 8 Content (ordinal [0,2,4,6,8], v3_ymrs_itm8)

v3_ymrs_itm8<-c(v3_clin$v3_ymrs_ymrs8_inhalte,v3_con$v3_ymrs_ymrs8_inhalte)
v3_ymrs_itm8<-factor(v3_ymrs_itm8, ordered=T)

descT(v3_ymrs_itm8)
##                0    2  4   6   8   <NA>     
## [1,] No. cases 658  16 3   9   12  845  1543
## [2,] Percent   42.6 1  0.2 0.6 0.8 54.8 100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v3_ymrs_itm9)

v3_ymrs_itm9<-c(v3_clin$v3_ymrs_ymrs9_exp_aggr_verh,v3_con$v3_ymrs_ymrs9_exp_aggr_verh)
v3_ymrs_itm9<-factor(v3_ymrs_itm9, ordered=T)

descT(v3_ymrs_itm9)
##                0    2   4   <NA>     
## [1,] No. cases 676  22  2   843  1543
## [2,] Percent   43.8 1.4 0.1 54.6 100

Item 10 Appearance (ordinal [0,1,2,3,4], v3_ymrs_itm10)

v3_ymrs_itm10<-c(v3_clin$v3_ymrs_ymrs10_erscheinung,v3_con$v3_ymrs_ymrs10_erscheinung)
v3_ymrs_itm10<-factor(v3_ymrs_itm10, ordered=T)

descT(v3_ymrs_itm10)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 634  54  9   1   1   844  1543
## [2,] Percent   41.1 3.5 0.6 0.1 0.1 54.7 100

Item 11 Insight (ordinal [0,1,2,3,4], v3_ymrs_itm11)

v3_ymrs_itm11<-c(v3_clin$v3_ymrs_ymrs11_krkh_einsicht,v3_con$v3_ymrs_ymrs11_krkh_einsicht)
v3_ymrs_itm11<-factor(v3_ymrs_itm11, ordered=T)

descT(v3_ymrs_itm11)
##                0    1   2  3   <NA>     
## [1,] No. cases 672  8   15 4   844  1543
## [2,] Percent   43.6 0.5 1  0.3 54.7 100

Create YMRS total score (continuous [0-60], v3_ymrs_sum)

v3_ymrs_sum<-(as.numeric.factor(v3_ymrs_itm1)+
        as.numeric.factor(v3_ymrs_itm2)+
        as.numeric.factor(v3_ymrs_itm3)+
        as.numeric.factor(v3_ymrs_itm4)+
        as.numeric.factor(v3_ymrs_itm5)+
        as.numeric.factor(v3_ymrs_itm6)+
        as.numeric.factor(v3_ymrs_itm7)+
        as.numeric.factor(v3_ymrs_itm8)+
        as.numeric.factor(v3_ymrs_itm9)+
        as.numeric.factor(v3_ymrs_itm10)+
        as.numeric.factor(v3_ymrs_itm11))

summary(v3_ymrs_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   2.174   2.000  30.000     858

Create dataset

v3_symp_ymrs<-data.frame(v3_ymrs_itm1,
                         v3_ymrs_itm2,
                         v3_ymrs_itm3,
                         v3_ymrs_itm4,
                         v3_ymrs_itm5,
                         v3_ymrs_itm6,
                         v3_ymrs_itm7,
                         v3_ymrs_itm8,
                         v3_ymrs_itm9,
                         v3_ymrs_itm10,
                         v3_ymrs_itm11,
                         v3_ymrs_sum)

CGI

Please see Visit 1 for more details and explicit rating instructions.

Illness severity (ordinal [1,2,3,4,5,6,7], v3_cgi_s)

v3_cgi_s<-c(v3_clin$v3_cgi1_cgi1_schweregrad,rep(-999,dim(v3_con)[1]))

v3_cgi_s[v3_cgi_s==0]<- -999
v3_cgi_s<-factor(v3_cgi_s, ordered=T)

descT(v3_cgi_s)
##                -999 1  2   3    4    5   6   7   <NA>     
## [1,] No. cases 320  15 44  186  176  103 26  1   672  1543
## [2,] Percent   20.7 1  2.9 12.1 11.4 6.7 1.7 0.1 43.6 100

Change since last study visit (ordinal [1,2,3,4,5,6,7], v3_cgi_c)

Here, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “very much improved”-1 to “very much worse”-7.

v3_cgi_c<-c(v3_clin$v3_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v3_con)[1]))

v3_cgi_c[v3_cgi_c==0]<- -999
v3_cgi_c<-factor(v3_cgi_c, ordered=T)

descT(v3_cgi_c)
##                -999 1   2   3   4   5  6   7   <NA>     
## [1,] No. cases 333  8   74  108 216 93 14  1   696  1543
## [2,] Percent   21.6 0.5 4.8 7   14  6  0.9 0.1 45.1 100

GAF (continuous [1-100], v3_gaf)

Please see Visit 1 for more details and explicit rating instructions.

v3_gaf<-c(v3_clin$v3_gaf_gaf_code,v3_con$v3_gaf_gaf_code)
v3_gaf[v3_gaf==0]<- -999

summary(v3_gaf[v3_gaf>0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   25.00   55.00   68.00   67.31   80.00   99.00     832
boxplot(v3_gaf[v3_gaf>0 & v1_stat=="CLINICAL"], v3_gaf[v3_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v3_ill_sev<-data.frame(v3_cgi_s,v3_cgi_c,v3_gaf)

Visit 3: Neuropsychology (cognitive tests)

There are no differences compared to the test battery assessed in Visit 2.

General comments on the testing (character, v3_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v3_nrpsy_lng)

v3_nrpsy_lng<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_nrpsy_lng<-ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==0, "mother tongue", 
                     ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==1, "good", 
                            ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==2, "sufficient", 
                              ifelse(c(v3_clin$v3_npu1_np_sprach,v3_con$v3_npu_folge_np_sprach)==3, "not sufficient",v3_nrpsy_lng))))
                            
v3_nrpsy_lng<-factor(v3_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v3_nrpsy_lng)
##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 703           35   4          0              801  1543
## [2,] Percent   45.6          2.3  0.3        0              51.9 100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v3_nrpsy_mtv)

v3_nrpsy_mtv_pre<-c(v3_clin$v3_npu1_np_mot,v3_con$v3_npu_folge_np_mot)

v3_nrpsy_mtv<-ifelse(v3_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v3_nrpsy_mtv_pre==1, "average", 
                      ifelse(v3_nrpsy_mtv_pre==2, "good", NA)))

v3_nrpsy_mtv<-factor(v3_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))

descT(v3_nrpsy_mtv)
##                poor average good <NA>     
## [1,] No. cases 12   59      656  816  1543
## [2,] Percent   0.8  3.8     42.5 52.9 100

VLMT

For a description of the test and the variables, see Visit 2.

Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.

VLMT_introcheck (categorical [0, 1, 9], v3_nrpsy_vlmt_check)

v3_nrpsy_vlmt_check<-c(v3_clin$v3_vlmt_vlmt_introcheck1,v3_con$v3_npu_folge_np_vlmt)
descT(v3_nrpsy_vlmt_check)
##                0   1    9   <NA>     
## [1,] No. cases 64  671  26  782  1543
## [2,] Percent   4.1 43.5 1.7 50.7 100

Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v3_nrpsy_vlmt_corr)

v3_nrpsy_vlmt_corr<-c(v3_clin$v3_vlmt_vlmt3_sw_a5d,v3_con$v3_npu_folge_np_vlmt_gl)
summary(v3_nrpsy_vlmt_corr)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   41.00   51.00   50.32   60.00   75.00     838

Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v3_nrpsy_vlmt_lss_d)

v3_nrpsy_vlmt_lss_d<-c(v3_clin$v3_vlmt_vlmt5_aw_ilsd6,v3_con$v3_npu_folge_np_vlmt_vni)
summary(v3_nrpsy_vlmt_lss_d)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -5.000   0.000   2.000   1.819   3.000  14.000     848

Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v3_nrpsy_vlmt_lss_t)

v3_nrpsy_vlmt_lss_t<-c(v3_clin$v3_vlmt_vlmt6_aw_vwd7,v3_con$v3_npu_folge_np_vlmt_vnzv)
summary(v3_nrpsy_vlmt_lss_t)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   -5.00    0.00    2.00    2.13    4.00   15.00     857

Recognition performance (corrected for falsely recognized words) (continuous [number of words], v3_nrpsy_vlmt_rec)

v3_nrpsy_vlmt_rec<-c(v3_clin$v3_vlmt_vlmt8_kwl,v3_con$v3_npu_folge_np_vlmt_kw)
summary(v3_nrpsy_vlmt_rec)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   -19.0    10.0    13.0    11.5    15.0    15.0     863

TMT

For a description of the test, see Visit 1.

TMT Part A, time (continuous [seconds], v3_nrpsy_tmt_A_rt)

v3_nrpsy_tmt_A_rt<-c(v3_clin$v3_npu1_tmt_001,v3_con$v3_npu_folge_np_tmt_001)
summary(v3_nrpsy_tmt_A_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    9.00   21.00   28.00   31.55   38.00  179.00     800

TMT Part A, errors (continuous [number of errors], v3_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).

v3_nrpsy_tmt_A_err<-c(v3_clin$v3_npu1_tmt_af_001,v3_con$v3_npu_folge_np_tmtfehler_001)
summary(v3_nrpsy_tmt_A_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.0839  0.0000  6.0000     804

TMT Part B, time (continuous [seconds], v3_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v3_nrpsy_tmt_B_rt<-c(v3_clin$v3_npu1_tmt_002,v3_con$v3_npu_folge_tmt_002) 
v3_nrpsy_tmt_B_rt[v3_nrpsy_tmt_B_rt>300]<-300

summary(v3_nrpsy_tmt_B_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   23.00   47.00   65.00   74.67   88.00  300.00     818

TMT Part B, errors (continuous [number of errors], v3_nrpsy_tmt_B_err)

## [1] 70
## [1] 903
v3_nrpsy_tmt_B_err<-c(v3_clin$v3_npu1_tmt_af_002,v3_con$v3_npu_folge_tmt_af_002)
summary(v3_nrpsy_tmt_B_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.5201  1.0000 18.0000     822

Verbal digit span

For a description of the test, see Visit 1.

Forward (continuous [number of items], v3_nrpsy_dgt_sp_frw)

v3_nrpsy_dgt_sp_frw<-c(v3_clin$v3_npu1_zns_001,v3_con$v3_npu_folge_np_wie_001)
summary(v3_nrpsy_dgt_sp_frw)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   3.000   8.000  10.000   9.713  11.000  16.000     812

Backward (continuous [number of items], v3_nrpsy_dgt_sp_bck)

v3_nrpsy_dgt_sp_bck<-c(v3_clin$v3_npu1_zns_002,v3_con$v3_npu_folge_np_wie_002)
summary(v3_nrpsy_dgt_sp_bck)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00    5.00    6.00    6.58    8.00   14.00     813

DST (continuous [number of correct symbols], v3_nrpsy_dg_sym)

For a description of the test, see Visit 1.

v3_introcheck3<-c(v3_clin$v3_npu1_np_introcheck3,v3_con$v3_npu_folge_np_hawier)
v3_nrpsy_dg_sym_pre<-c(v3_clin$v3_npu1_zst_001,v3_con$v3_npu_folge_np_hawier_001)

v3_nrpsy_dg_sym<-ifelse(v3_introcheck3==1, v3_nrpsy_dg_sym_pre, 
                           ifelse(v3_introcheck3==9,-999,
                                  ifelse(v3_introcheck3==0,NA,NA)))

summary(subset(v3_nrpsy_dg_sym,v3_nrpsy_dg_sym>=0))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   15.00   52.00   68.00   67.93   83.75  133.00

Create dataset

v3_nrpsy<-data.frame(v3_nrpsy_com,
                     v3_nrpsy_lng,
                     v3_nrpsy_mtv,
                     v3_nrpsy_vlmt_check,
                     v3_nrpsy_vlmt_corr,
                     v3_nrpsy_vlmt_lss_d,
                     v3_nrpsy_vlmt_lss_t,
                     v3_nrpsy_vlmt_rec,
                     v3_nrpsy_tmt_A_rt,
                     v3_nrpsy_tmt_A_err,
                     v3_nrpsy_tmt_B_rt,
                     v3_nrpsy_tmt_B_err,
                     v3_nrpsy_dgt_sp_frw,
                     v3_nrpsy_dgt_sp_bck,
                     v3_nrpsy_dg_sym)

Visit 3: Questionnaires (patient rates her/himself)

Participants were asked to fill out questionnaires on the following topics: childhood trauma/early life stress (CTS), current medication adherence (compliance), current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 2 and 3) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1 and 2, questionnaires that were not filled out correctly were excluded from the dataset.

SF-12

For explanation, please refer to the section in Visit 1

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v3_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v3_sf12_recode(v3_con$v3_sf12_sf_allgemein,"v3_sf12_itm0")
##                -999 2   3   4   5   6   7   8  9   10  <NA>     
## [1,] No. cases 1223 1   4   4   6   8   27  61 59  26  124  1543
## [2,] Percent   79.3 0.1 0.3 0.3 0.4 0.5 1.7 4  3.8 1.7 8    100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v3_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v3_sf12_recode(v3_con$v3_sf12_sf1,"v3_sf12_itm1")
##                -999 1   2   3  4   5   <NA>     
## [1,] No. cases 1223 38  98  62 8   2   112  1543
## [2,] Percent   79.3 2.5 6.4 4  0.5 0.1 7.3  100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v3_sf12_itm2) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v3_sf12_recode(v3_con$v3_sf12_sf2,"v3_sf12_itm2")
##                -999 1   2  3    <NA>     
## [1,] No. cases 1223 1   16 191  112  1543
## [2,] Percent   79.3 0.1 1  12.4 7.3  100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v3_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v3_sf12_recode(v3_con$v3_sf12_sf3,"v3_sf12_itm3")
##                -999 1   2   3    <NA>     
## [1,] No. cases 1223 1   24  183  112  1543
## [2,] Percent   79.3 0.1 1.6 11.9 7.3  100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v3_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf4,"v3_sf12_itm4")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 26  182  112  1543
## [2,] Percent   79.3 1.7 11.8 7.3  100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v3_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf5,"v3_sf12_itm5")
##                -999 1  2    <NA>     
## [1,] No. cases 1223 16 190  114  1543
## [2,] Percent   79.3 1  12.3 7.4  100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v3_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf6,"v3_sf12_itm6")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 14  194  112  1543
## [2,] Percent   79.3 0.9 12.6 7.3  100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v3_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v3_sf12_recode(v3_con$v3_sf12_sf7,"v3_sf12_itm7")
##                -999 1   2    <NA>     
## [1,] No. cases 1223 12  195  113  1543
## [2,] Percent   79.3 0.8 12.6 7.3  100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v3_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.

v3_sf12_recode(v3_con$v3_sf12_st8,"v3_sf12_itm8")
##                -999 1   2  3   4   5   <NA>     
## [1,] No. cases 1223 115 47 24  18  4   112  1543
## [2,] Percent   79.3 7.5 3  1.6 1.2 0.3 7.3  100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v3_sf12_itm9)

v3_sf12_recode(v3_con$v3_sf12_st9,"v3_sf12_itm9")
##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1223 20  124 42  19  2   1   112  1543
## [2,] Percent   79.3 1.3 8   2.7 1.2 0.1 0.1 7.3  100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v3_sf12_itm10)

v3_sf12_recode(v3_con$v3_sf12_st10,"v3_sf12_itm10")
##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 1223 13  80  56  49  10  112  1543
## [2,] Percent   79.3 0.8 5.2 3.6 3.2 0.6 7.3  100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v3_sf12_itm11)

v3_sf12_recode(v3_con$v3_sf12_st11,"v3_sf12_itm11")
##                -999 2   3   4   5  6   <NA>     
## [1,] No. cases 1223 3   7   24  92 82  112  1543
## [2,] Percent   79.3 0.2 0.5 1.6 6  5.3 7.3  100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v3_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

ATTENTION: there appears to be something wrong with the coding of this item, i.e. the export of the item and the display in the GUI of the database are inconsistent. NOT CONTAINED IN DATASET FOR NOW

v3_sf12_recode(v3_con$v3_sf12_st12,"v3_sf12_itm12")

Create dataset

v3_sf12<-data.frame(v3_sf12_itm0,
                    v3_sf12_itm1,
                    v3_sf12_itm2,
                    v3_sf12_itm3,
                    v3_sf12_itm4,
                    v3_sf12_itm5,
                    v3_sf12_itm6,
                    v3_sf12_itm7,
                    v3_sf12_itm8,
                    v3_sf12_itm9,
                    v3_sf12_itm10,
                    v3_sf12_itm11)

#INCLUDE v3_sf12_itm12 when issues are settled

Childhood Trauma Screener (CTS)

The CTS (David P. Bernstein et al., 2003) used here is a German short version (H. J. Grabe et al., 2012) of the CTQ (D. P. Bernstein et al., 1994). It is used as a screening instrument to assess childhood trauma/early life stress. Validated threshold values are available (Glaesmer et al., 2013) to transform these values into a dichotomous scale (childhood trauma/early life stress: yes/no; see below). Each of the five questions is on a five-point scale.
Important: analogous to other questionnaires, we have, as specified in the test manual, reversed the encoding so that, in the present dataset, higher scores on every item indicate a higher level of childhood trauma/early life stress. Do not reverse encoding. Each questions starts with “When I grew up”

CTS single items (ordinal)

1. “…I had the feeling to be loved” (ordinal [1,2,3,4,5], v3_cts_1)
Encoding reversed so that higher scores on each item indicate a higher level of childhood trauma/early life stress. This item measures emotional neglect.

cts_recode(v3_clin$v3_chidlhood_childhood_1,v3_con$v3_chidlhood_childhood_1,"v3_cts_1",1)
##                1    2    3   4  5   NA's     
## [1,] No. cases 215  290  106 92 25  815  1543
## [2,] Percent   13.9 18.8 6.9 6  1.6 52.8 100
descT(v3_cts_1)
##                1    2    3   4  5   <NA>     
## [1,] No. cases 215  290  106 92 25  815  1543
## [2,] Percent   13.9 18.8 6.9 6  1.6 52.8 100

2. “…persons in my family hit me so hard that I bruised” (ordinal [1,2,3,4,5], v3_cts_2)
This item measures physical abuse.

cts_recode(v3_clin$v3_chidlhood_childhood_2,v3_con$v3_chidlhood_childhood_2,"v3_cts_2",0)
##                1    2  3   4   5   NA's     
## [1,] No. cases 513  93 64  35  11  827  1543
## [2,] Percent   33.2 6  4.1 2.3 0.7 53.6 100

3. “…I had the feeling someone in my family hated me” (ordinal [1,2,3,4,5], v3_cts_3)
This item measures emotional abuse.

cts_recode(v3_clin$v3_chidlhood_childhood_3,v3_con$v3_chidlhood_childhood_3,"v3_cts_3",0)
##                0   1    2   3   4   5  NA's     
## [1,] No. cases 1   461  107 72  48  31 823  1543
## [2,] Percent   0.1 29.9 6.9 4.7 3.1 2  53.3 100

4. “…someone harassed me sexually” (ordinal [1,2,3,4,5], v3_cts_4)
This items measures sexual abuse.

cts_recode(v3_clin$v3_chidlhood_childhood_4,v3_con$v3_chidlhood_childhood_4,"v3_cts_4",0)
##                0   1    2  3   4  5   NA's     
## [1,] No. cases 1   605  47 37  15 5   833  1543
## [2,] Percent   0.1 39.2 3  2.4 1  0.3 54   100

5. “…there was someone who took me to the doctor when I needed it” (ordinal [1,2,3,4,5], v3_cts_5) Encoding reversed so that higher scores on each item indicate a higher level of childhood trauma/early life stress. This item measures physical neglect.

cts_recode(v3_clin$v3_chidlhood_childhood_5,v3_con$v3_chidlhood_childhood_5,"v3_cts_5",1)
##                1    2    3   4   5  NA's     
## [1,] No. cases 369  181  101 45  31 816  1543
## [2,] Percent   23.9 11.7 6.5 2.9 2  52.9 100

Overall assessment whether childhood trauma/early life stress was present or not (dichotomous, v3_cts_els_dic)

This assessment indicates whether a participant suffered from childhood trauma/early life stress or not (see description above). More specifically, if any of the five items exceeded the threshold given by (Glaesmer et al., 2013), an individual was determined to have experienced childhood trauma/early life stress. If individuals filled out the questionnaire incompletely but one item nevertheless passed the threshold, these individuals are included in the present dataset. Only those individuals in which all items are completet and below the threshold are set to “N”.

v3_cts_els_dic<-c(rep(NA,dim(v3_clin)[1]),rep(NA,dim(v3_clin)[1]))
v3_cts_els_dic<-ifelse(v3_cts_1>3 | v3_cts_2>2 | v3_cts_3>2 | v3_cts_4>1 | v3_cts_5>3, "Y",
                      ifelse((is.na(v3_cts_1)==F & is.na(v3_cts_2)==F & is.na(v3_cts_3)==F & 
                                is.na(v3_cts_4)==F & is.na(v3_cts_5)==F),"N", v3_cts_els_dic))

descT(v3_cts_els_dic)
##                N    Y    <NA>     
## [1,] No. cases 413  300  830  1543
## [2,] Percent   26.8 19.4 53.8 100

Assessments of each item whether it exceeds the given theshold for childhood trauma/early life stress (ordinal)

1. “…I had the feeling to be loved” (dichotomous, v3_cts_1_dic)

v3_cts_1_dic<-ifelse(v3_cts_1>3, "Y","N")
descT(v3_cts_1_dic)
##                N    Y   <NA>     
## [1,] No. cases 611  117 815  1543
## [2,] Percent   39.6 7.6 52.8 100

2. “…persons in my family hit me so hard that I bruised” (dichotomous, v3_cts_2_dic)

v3_cts_2_dic<-ifelse(v3_cts_2>2, "Y","N")
descT(v3_cts_2_dic)
##                N    Y   <NA>     
## [1,] No. cases 606  110 827  1543
## [2,] Percent   39.3 7.1 53.6 100

3. “…I had the feeling someone in my family hated me” (dichotomous, v3_cts_3_dic)

v3_cts_3_dic<-ifelse(v3_cts_3>2, "Y","N")
descT(v3_cts_3_dic)
##                N    Y   <NA>     
## [1,] No. cases 569  151 823  1543
## [2,] Percent   36.9 9.8 53.3 100

4. “…someone harassed me sexually” (dichotomous, v3_cts_4_dic)

v3_cts_4_dic<-ifelse(v3_cts_4>1, "Y","N")
descT(v3_cts_4_dic)
##                N    Y   <NA>     
## [1,] No. cases 606  104 833  1543
## [2,] Percent   39.3 6.7 54   100

5. “…there was someone who took me to the doctor when I needed it” (dichotomous, v3_cts_5_dic)

v3_cts_5_dic<-ifelse(v3_cts_5>3, "Y","N")
descT(v3_cts_5_dic)
##                N    Y   <NA>     
## [1,] No. cases 651  76  816  1543
## [2,] Percent   42.2 4.9 52.9 100

Create dataset

v3_cts<-data.frame(v3_cts_1,v3_cts_2,v3_cts_3,v3_cts_4,v3_cts_5,v3_cts_els_dic,
                   v3_cts_1_dic,v3_cts_2_dic,v3_cts_3_dic,v3_cts_4_dic,v3_cts_5_dic)

Medication adherence (compliance)

For a description of the questionnaire, see Visit 1.

Past seven days (ordinal [1,2,3,4,5,6], v3_med_pst_wk)

v3_med_chk<-c(v3_clin$v3_compl_verwer_fragebogen,rep(1,dim(v3_con)[1]))
v3_med_pst_wk_pre<-c(v3_clin$v3_compl_psychopharm_7_tag,rep(-999,dim(v3_con)[1]))
  
v3_med_pst_wk<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_med_pst_wk<-ifelse((is.na(v3_med_chk) | v3_med_chk!=2), 
                      v3_med_pst_wk_pre, v3_med_pst_wk)

descT(v3_med_pst_wk)
##                -999 1    2   3  4   5   6   <NA>     
## [1,] No. cases 320  433  50  16 3   3   8   710  1543
## [2,] Percent   20.7 28.1 3.2 1  0.2 0.2 0.5 46   100

Past six months (ordinal [1,2,3,4,5,6], v3_med_pst_sx_mths)

v3_med_pre<-c(v3_clin$v3_compl_psychopharm_6_mon,rep(-999,dim(v3_con)[1]))

v3_med_pst_sx_mths<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])
v3_med_pst_sx_mths<-ifelse((is.na(v3_med_chk) | v3_med_chk!=2),
                           v3_med_pre, v3_med_pst_sx_mths)

descT(v3_med_pst_sx_mths)
##                -999 1    2   3   4   5   6   <NA>     
## [1,] No. cases 320  393  71  33  10  4   6   706  1543
## [2,] Percent   20.7 25.5 4.6 2.1 0.6 0.3 0.4 45.8 100

Create dataset

v3_med_adh<-data.frame(v3_med_pst_wk,v3_med_pst_sx_mths)

BDI-II

For explanation, please refer to the section in Visit 1

1. Sadness (ordinal [0,1,2,3], v3_bdi2_itm1)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi1_traurigkeit,v3_con$v3_bdi2_s1_bdi1,"v3_bdi2_itm1")
##                0    1    2  3   <NA>     
## [1,] No. cases 552  171  16 2   802  1543
## [2,] Percent   35.8 11.1 1  0.1 52   100

2. Pessimism (ordinal [0,1,2,3], v3_bdi2_itm2)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi2_pessimismus,v3_con$v3_bdi2_s1_bdi2,"v3_bdi2_itm2")
##                0    1   2   3   <NA>     
## [1,] No. cases 575  119 38  7   804  1543
## [2,] Percent   37.3 7.7 2.5 0.5 52.1 100

3. Past failure (ordinal [0,1,2,3], v3_bdi2_itm3)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi3_versagensgef,v3_con$v3_bdi2_s1_bdi3,"v3_bdi2_itm3")
##                0    1   2   3   <NA>     
## [1,] No. cases 522  123 84  12  802  1543
## [2,] Percent   33.8 8   5.4 0.8 52   100

4. Loss of pleasure (ordinal [0,1,2,3], v3_bdi2_itm4)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi4_verlust_freude,v3_con$v3_bdi2_s1_bdi4,"v3_bdi2_itm4")
##                0    1    2  3   <NA>     
## [1,] No. cases 484  210  31 14  804  1543
## [2,] Percent   31.4 13.6 2  0.9 52.1 100

5. Guilty feelings (ordinal [0,1,2,3], v3_bdi2_itm5)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi5_schuldgef,v3_con$v3_bdi2_s1_bdi5,"v3_bdi2_itm5")
##                0    1   2  3   <NA>     
## [1,] No. cases 563  153 15 8   804  1543
## [2,] Percent   36.5 9.9 1  0.5 52.1 100

6. Punishment feelings (ordinal [0,1,2,3], v3_bdi2_itm6)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi6_bestrafungsgef,v3_con$v3_bdi2_s1_bdi6,"v3_bdi2_itm6")
##                0   1   2   3   <NA>     
## [1,] No. cases 601 100 7   29  806  1543
## [2,] Percent   39  6.5 0.5 1.9 52.2 100

7. Self-dislike (ordinal [0,1,2,3], v3_bdi2_itm7)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi7_selbstablehnung,v3_con$v3_bdi2_s1_bdi7,"v3_bdi2_itm7")
##                0    1  2   3   <NA>     
## [1,] No. cases 591  92 45  10  805  1543
## [2,] Percent   38.3 6  2.9 0.6 52.2 100

8. Self-criticalness (ordinal [0,1,2,3], v3_bdi2_itm8)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi8_selbstvorwuerfe,v3_con$v3_bdi2_s1_bdi8,"v3_bdi2_itm8")
##                0    1    2   3   <NA>     
## [1,] No. cases 507  175  44  14  803  1543
## [2,] Percent   32.9 11.3 2.9 0.9 52   100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v3_bdi2_itm9)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi9_selbstmordged,v3_con$v3_bdi2_s1_bdi9,"v3_bdi2_itm9")
##                0    1   2   3   <NA>     
## [1,] No. cases 638  96  3   5   801  1543
## [2,] Percent   41.3 6.2 0.2 0.3 51.9 100

10. Crying (ordinal [0,1,2,3], v3_bdi2_itm10)

v3_bdi2_recode(v3_clin$v3_bdi2_s1_bdi10_weinen,v3_con$v3_bdi2_s1_bdi10,"v3_bdi2_itm10")
##                0    1   2   3   <NA>     
## [1,] No. cases 619  56  11  53  804  1543
## [2,] Percent   40.1 3.6 0.7 3.4 52.1 100

11. Agitation (ordinal [0,1,2,3], v3_bdi2_itm11)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi11_unruhe,v3_con$v3_bdi2_s2_bdi11,"v3_bdi2_itm11")
##                0    1   2   3   <NA>     
## [1,] No. cases 553  150 20  9   811  1543
## [2,] Percent   35.8 9.7 1.3 0.6 52.6 100

12. Loss of interest (ordinal [0,1,2,3], v3_bdi2_itm12)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi12_interessverl,v3_con$v3_bdi2_s2_bdi12,"v3_bdi2_itm12")
##                0    1   2   3  <NA>     
## [1,] No. cases 568  123 24  15 813  1543
## [2,] Percent   36.8 8   1.6 1  52.7 100

13. Indecisiveness (ordinal [0,1,2,3], v3_bdi2_itm13)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi13_entschlussunf,v3_con$v3_bdi2_s2_bdi13,"v3_bdi2_itm13")
##                0    1    2   3   <NA>     
## [1,] No. cases 503  167  28  32  813  1543
## [2,] Percent   32.6 10.8 1.8 2.1 52.7 100

14. Worthlessness (ordinal [0,1,2,3], v3_bdi2_itm14)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi14_wertlosigkeit,v3_con$v3_bdi2_s2_bdi14,"v3_bdi2_itm14")
##                0    1   2   3   <NA>     
## [1,] No. cases 563  100 54  12  814  1543
## [2,] Percent   36.5 6.5 3.5 0.8 52.8 100

15. Loss of energy (ordinal [0,1,2,3], v3_bdi2_itm15)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi15_energieverlust,v3_con$v3_bdi2_s2_bdi15,"v3_bdi2_itm15")
##                0    1    2   3   <NA>     
## [1,] No. cases 430  239  51  6   817  1543
## [2,] Percent   27.9 15.5 3.3 0.4 52.9 100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v3_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep“. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v3_itm_bdi2_chk<-c(v3_clin$v3_bdi2_s1_verwer_fragebogen,v3_con$v3_bdi2_s1_bdi_korrekt)
v3_itm_bdi2_itm16_clin_con<-c(v3_clin$v3_bdi2_s2_bdi16_schlafgewohn,v3_con$v3_bdi2_s2_bdi16)

v3_bdi2_itm16<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])

v3_bdi2_itm16<-ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & v3_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                           (v3_itm_bdi2_itm16_clin_con==1 | v3_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm16_clin_con==2 | v3_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm16_clin_con==3 | v3_itm_bdi2_itm16_clin_con==300), 3, v3_bdi2_itm16))))  

v3_bdi2_itm16<-factor(v3_bdi2_itm16,ordered=T)
descT(v3_bdi2_itm16)
##                0    1   2   3   <NA>     
## [1,] No. cases 421  231 52  27  812  1543
## [2,] Percent   27.3 15  3.4 1.7 52.6 100

17. Irritability (ordinal [0,1,2,3], v3_bdi2_itm17)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi17_reizbarkeit,v3_con$v3_bdi2_s2_bdi17,"v3_bdi2_itm17")
##                0    1   2   3   <NA>     
## [1,] No. cases 566  139 17  7   814  1543
## [2,] Percent   36.7 9   1.1 0.5 52.8 100

18. Change in appetite (ordinal [0,1,2,3], v3_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v3_itm_bdi2_itm18_clin_con<-c(v3_clin$v3_bdi2_s2_bdi18_appetit,v3_con$v3_bdi2_s2_bdi18)
v3_bdi2_itm18<-rep(NA,dim(v3_clin)[1]+dim(v3_con)[1])

v3_bdi2_itm18<-ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & v3_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                           (v3_itm_bdi2_itm18_clin_con==1 | v3_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm18_clin_con==2 | v3_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v3_itm_bdi2_chk) | v3_itm_bdi2_chk!=2) & 
                               (v3_itm_bdi2_itm18_clin_con==3 | v3_itm_bdi2_itm18_clin_con==300), 3, v3_bdi2_itm18))))  

v3_bdi2_itm18<-factor(v3_bdi2_itm18,ordered=T)
descT(v3_bdi2_itm18)
##                0    1    2   3   <NA>     
## [1,] No. cases 510  174  32  13  814  1543
## [2,] Percent   33.1 11.3 2.1 0.8 52.8 100

19. Concentration difficulty (ordinal [0,1,2,3], v3_bdi2_itm19)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi19_konzschw,v3_con$v3_bdi2_s2_bdi19,"v3_bdi2_itm19")
##                0    1    2   3   <NA>     
## [1,] No. cases 450  193  84  5   811  1543
## [2,] Percent   29.2 12.5 5.4 0.3 52.6 100

20. Tiredness or fatigue (ordinal [0,1,2,3], v3_bdi2_itm20)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi20_ermued_ersch,v3_con$v3_bdi2_s2_bdi20,"v3_bdi2_itm20")
##                0    1    2   3   <NA>     
## [1,] No. cases 442  234  41  14  812  1543
## [2,] Percent   28.6 15.2 2.7 0.9 52.6 100

21. Loss of interest in sex (ordinal [0,1,2,3], v3_bdi2_itm21)

v3_bdi2_recode(v3_clin$v3_bdi2_s2_bdi21_sex_interess,v3_con$v3_bdi2_s2_bdi21,"v3_bdi2_itm21")
##                0    1   2  3   <NA>     
## [1,] No. cases 533  110 31 54  815  1543
## [2,] Percent   34.5 7.1 2  3.5 52.8 100

BDI-II sum score calculation (continuous [0-63], v3_bdi2_sum)

v3_bdi2_sum<-as.numeric.factor(v3_bdi2_itm1)+
              as.numeric.factor(v3_bdi2_itm2)+
              as.numeric.factor(v3_bdi2_itm3)+
              as.numeric.factor(v3_bdi2_itm4)+
              as.numeric.factor(v3_bdi2_itm5)+
              as.numeric.factor(v3_bdi2_itm6)+
              as.numeric.factor(v3_bdi2_itm7)+
              as.numeric.factor(v3_bdi2_itm8)+
              as.numeric.factor(v3_bdi2_itm9)+
              as.numeric.factor(v3_bdi2_itm10)+
              as.numeric.factor(v3_bdi2_itm11)+
              as.numeric.factor(v3_bdi2_itm12)+
              as.numeric.factor(v3_bdi2_itm13)+
              as.numeric.factor(v3_bdi2_itm14)+
              as.numeric.factor(v3_bdi2_itm15)+
              as.numeric.factor(v3_bdi2_itm16)+
              as.numeric.factor(v3_bdi2_itm17)+
              as.numeric.factor(v3_bdi2_itm18)+
              as.numeric.factor(v3_bdi2_itm19)+
              as.numeric.factor(v3_bdi2_itm20)+
              as.numeric.factor(v3_bdi2_itm21)

summary(v3_bdi2_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   4.000   7.495  11.000  53.000     846

Create dataset

v3_bdi2<-data.frame(v3_bdi2_itm1,v3_bdi2_itm2,v3_bdi2_itm3,v3_bdi2_itm4,v3_bdi2_itm5,
                    v3_bdi2_itm6,v3_bdi2_itm7,v3_bdi2_itm8,v3_bdi2_itm9,v3_bdi2_itm10,
                    v3_bdi2_itm11,v3_bdi2_itm12,v3_bdi2_itm13,v3_bdi2_itm14,
                    v3_bdi2_itm15,v3_bdi2_itm16,v3_bdi2_itm17,v3_bdi2_itm18,
                    v3_bdi2_itm19,v3_bdi2_itm20,v3_bdi2_itm21, v3_bdi2_sum)

ASRM

For explanation, please refer to the section in Visit 1

1. Positive Mood (ordinal [0,1,2,3,4], v3_asrm_itm1)

v3_asrm_recode(v3_clin$v3_asrm_asrm1_gluecklich,v3_con$v3_asrm_asrm1,"v3_asrm_itm1")
##                0    1    2   3   4   <NA>     
## [1,] No. cases 492  171  49  21  5   805  1543
## [2,] Percent   31.9 11.1 3.2 1.4 0.3 52.2 100

2 Self-Confidence (ordinal [0,1,2,3,4], v3_asrm_itm2)

v3_asrm_recode(v3_clin$v3_asrm_asrm2_selbstbewusst,v3_con$v3_asrm_asrm2,"v3_asrm_itm2")
##                0    1   2   3   4   <NA>     
## [1,] No. cases 532  147 36  19  3   806  1543
## [2,] Percent   34.5 9.5 2.3 1.2 0.2 52.2 100

3. Sleep (ordinal [0,1,2,3,4], v3_asrm_itm3)

v3_asrm_recode(v3_clin$v3_asrm_asrm3_schlaf,v3_con$v3_asrm_asrm3,"v3_asrm_itm3")
##                0    1   2   3   4   <NA>     
## [1,] No. cases 610  84  25  13  6   805  1543
## [2,] Percent   39.5 5.4 1.6 0.8 0.4 52.2 100

4. Speech (ordinal [0,1,2,3,4], v3_asrm_itm4)

v3_asrm_recode(v3_clin$v3_asrm_asrm4_reden,v3_con$v3_asrm_asrm4,"v3_asrm_itm4")
##                0    1   2  3   4   <NA>     
## [1,] No. cases 579  132 16 8   2   806  1543
## [2,] Percent   37.5 8.6 1  0.5 0.1 52.2 100

5. Activity Level (ordinal [0,1,2,3,4], v3_asrm_itm5)

v3_asrm_recode(v3_clin$v3_asrm_asrm5_aktiv,v3_con$v3_asrm_asrm5,"v3_asrm_itm5")
##                0   1    2   3   4   <NA>     
## [1,] No. cases 524 162  34  7   9   807  1543
## [2,] Percent   34  10.5 2.2 0.5 0.6 52.3 100

Create ASRM sum score (continuous [0-20],v3_asrm_sum)

v3_asrm_sum<-as.numeric.factor(v3_asrm_itm1)+
            as.numeric.factor(v3_asrm_itm2)+
            as.numeric.factor(v3_asrm_itm3)+
            as.numeric.factor(v3_asrm_itm4)+
            as.numeric.factor(v3_asrm_itm5)

summary(v3_asrm_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   1.000   1.771   2.500  18.000     808

Create dataset

v3_asrm<-data.frame(v3_asrm_itm1,v3_asrm_itm2,v3_asrm_itm3,v3_asrm_itm4,v3_asrm_itm5,v3_asrm_sum)

MSS

For explanation, please refer to the section in Visit 1

1. “I had more energy” (dichotomous, v3_mss_itm1)

v3_mss_recode(v3_clin$v3_mss_s1_mss1_energie,v3_con$v3_mss_s1_mss1,"v3_mss_itm1")
##                N    Y   <NA>     
## [1,] No. cases 608  133 802  1543
## [2,] Percent   39.4 8.6 52   100

2. “I had trouble sitting still” (dichotomous, v3_mss_itm2)

v3_mss_recode(v3_clin$v3_mss_s1_mss2_ruhig_sitzen,v3_con$v3_mss_s1_mss2,"v3_mss_itm2")
##                N    Y   <NA>     
## [1,] No. cases 642  98  803  1543
## [2,] Percent   41.6 6.4 52   100

3. “I drove faster” (dichotomous, v3_mss_itm3)

v3_mss_recode(v3_clin$v3_mss_s1_mss3_auto_fahren,v3_con$v3_mss_s1_mss3,"v3_mss_itm3")
##                N    Y   <NA>     
## [1,] No. cases 690  25  828  1543
## [2,] Percent   44.7 1.6 53.7 100

4. “I drank more alcoholic beverages” (dichotomous, v3_mss_itm4)

v3_mss_recode(v3_clin$v3_mss_s1_mss4_alkohol,v3_con$v3_mss_s1_mss4,"v3_mss_itm4")
##                N    Y   <NA>     
## [1,] No. cases 674  60  809  1543
## [2,] Percent   43.7 3.9 52.4 100

5. “I changed clothes several times a day” (dichotomous, v3_mss_itm5)

v3_mss_recode(v3_clin$v3_mss_s1_mss5_umziehen, v3_con$v3_mss_s1_mss5,"v3_mss_itm5")
##                N    Y   <NA>     
## [1,] No. cases 677  58  808  1543
## [2,] Percent   43.9 3.8 52.4 100

6. “I wore brighter clothes/make-up” (dichotomous, v3_mss_itm6)

v3_mss_recode(v3_clin$v3_mss_s1_mss6_bunter,v3_con$v3_mss_s1_mss6,"v3_mss_itm6")
##                N    Y   <NA>     
## [1,] No. cases 696  44  803  1543
## [2,] Percent   45.1 2.9 52   100

7. “I played music louder” (dichotomous, v3_mss_itm7)

v3_mss_recode(v3_clin$v3_mss_s1_mss7_musik_lauter,v3_con$v3_mss_s1_mss7,"v3_mss_itm7")
##                N    Y   <NA>     
## [1,] No. cases 644  96  803  1543
## [2,] Percent   41.7 6.2 52   100

8. “I ate faster than usual” (dichotomous, v3_mss_itm8)

v3_mss_recode(v3_clin$v3_mss_s1_mss8_hastiger_essen,v3_con$v3_mss_s1_mss8,"v3_mss_itm8")
##                N    Y  <NA>     
## [1,] No. cases 662  77 804  1543
## [2,] Percent   42.9 5  52.1 100

9. “I ate more than usual” (dichotomous, v3_mss_itm9)

v3_mss_recode(v3_clin$v3_mss_s1_mss9_mehr_essen,v3_con$v3_mss_s1_mss9,"v3_mss_itm9")
##                N    Y   <NA>     
## [1,] No. cases 612  127 804  1543
## [2,] Percent   39.7 8.2 52.1 100

10. “I slept fewer hours than usual” (dichotomous, v3_mss_itm10)

v3_mss_recode(v3_clin$v3_mss_s1_mss10_weniger_schlaf,v3_con$v3_mss_s1_mss10,"v3_mss_itm10")
##                N    Y   <NA>     
## [1,] No. cases 652  87  804  1543
## [2,] Percent   42.3 5.6 52.1 100

11. “I started things that I didn’t finish” (dichotomous, v3_mss_itm11)

v3_mss_recode(v3_clin$v3_mss_s1_mss11_unbeendet,v3_con$v3_mss_s1_mss11,"v3_mss_itm11")
##                N    Y   <NA>     
## [1,] No. cases 604  136 803  1543
## [2,] Percent   39.1 8.8 52   100

12. “I gave away my own possessions” (dichotomous, v3_mss_itm12)

v3_mss_recode(v3_clin$v3_mss_s1_mss12_weggeben,v3_con$v3_mss_s1_mss12,"v3_mss_itm12")
##                N    Y  <NA>     
## [1,] No. cases 676  62 805  1543
## [2,] Percent   43.8 4  52.2 100

13. “I bought gifts for people” (dichotomous, v3_mss_itm13)

v3_mss_recode(v3_clin$v3_mss_s1_mss13_geschenke,v3_con$v3_mss_s1_mss13,"v3_mss_itm13")
##                N    Y   <NA>     
## [1,] No. cases 673  66  804  1543
## [2,] Percent   43.6 4.3 52.1 100

14. “I spent money more freely” (dichotomous, v3_mss_itm14)

v3_mss_recode(v3_clin$v3_mss_s1_mss14_mehr_geld,v3_con$v3_mss_s1_mss14,"v3_mss_itm14")
##                N    Y    <NA>     
## [1,] No. cases 572  168  803  1543
## [2,] Percent   37.1 10.9 52   100

15. “I accumulated debts” (dichotomous, v3_mss_itm15)

v3_mss_recode(v3_clin$v3_mss_s1_mss15_schulden,v3_con$v3_mss_s1_mss15,"v3_mss_itm15")
##                N    Y  <NA>     
## [1,] No. cases 693  47 803  1543
## [2,] Percent   44.9 3  52   100

16. “I made unwise business decisions” (dichotomous, v3_mss_itm16)

v3_mss_recode(v3_clin$v3_mss_s1_mss16_unkluge_entsch,v3_con$v3_mss_s1_mss16,"v3_mss_itm16")
##                N    Y   <NA>     
## [1,] No. cases 716  24  803  1543
## [2,] Percent   46.4 1.6 52   100

17. “I partied more” (dichotomous, v3_mss_itm17)

v3_mss_recode(v3_clin$v3_mss_s1_mss17_parties,v3_con$v3_mss_s1_mss17,"v3_mss_itm17")
##                N    Y   <NA>     
## [1,] No. cases 704  37  802  1543
## [2,] Percent   45.6 2.4 52   100

18. “I enjoyed flirting” (dichotomous, v3_mss_itm18)

v3_mss_recode(v3_clin$v3_mss_s1_mss18_flirten,v3_con$v3_mss_s1_mss18,"v3_mss_itm18")
##                N    Y   <NA>     
## [1,] No. cases 686  51  806  1543
## [2,] Percent   44.5 3.3 52.2 100

19. “I masturbated more often” (dichotomous, v3_mss_itm19)

v3_mss_recode(v3_clin$v3_mss_s2_mss19_selbstbefried,v3_con$v3_mss_s2_mss19,"v3_mss_itm19")
##                N   Y   <NA>     
## [1,] No. cases 694 33  816  1543
## [2,] Percent   45  2.1 52.9 100

20. “I was more interested in sex than usual” (dichotomous, v3_mss_itm20)

v3_mss_recode(v3_clin$v3_mss_s2_mss20_sex_interess,v3_con$v3_mss_s2_mss20,"v3_mss_itm20")
##                N    Y   <NA>     
## [1,] No. cases 655  75  813  1543
## [2,] Percent   42.4 4.9 52.7 100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v3_mss_itm21)

v3_mss_recode(v3_clin$v3_mss_s2_mss21_sexpartner,v3_con$v3_mss_s2_mss21,"v3_mss_itm21")
##                N    Y   <NA>     
## [1,] No. cases 716  13  814  1543
## [2,] Percent   46.4 0.8 52.8 100

22. “I spent more time on the phone” (dichotomous, v3_mss_itm22)

v3_mss_recode(v3_clin$v3_mss_s2_mss22_mehr_telefon,v3_con$v3_mss_s2_mss22,"v3_mss_itm22")
##                N    Y   <NA>     
## [1,] No. cases 646  87  810  1543
## [2,] Percent   41.9 5.6 52.5 100

23. “I spoke louder than usual” (dichotomous, v3_mss_itm23)

v3_mss_recode(v3_clin$v3_mss_s2_mss23_sprache_lauter,v3_con$v3_mss_s2_mss23,"v3_mss_itm23")
##                N    Y   <NA>     
## [1,] No. cases 680  51  812  1543
## [2,] Percent   44.1 3.3 52.6 100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v3_mss_itm24)

v3_mss_recode(v3_clin$v3_mss_s2_mss24_spr_schneller,v3_con$v3_mss_s2_mss24,"v3_mss_itm24")
##                N    Y   <NA>     
## [1,] No. cases 689  40  814  1543
## [2,] Percent   44.7 2.6 52.8 100

25. “1 enjoyed punning or rhyming” (dichotomous, v3_mss_itm25)

v3_mss_recode(v3_clin$v3_mss_s2_mss25_witze,v3_con$v3_mss_s2_mss25,"v3_mss_itm25")
##                N    Y   <NA>     
## [1,] No. cases 659  74  810  1543
## [2,] Percent   42.7 4.8 52.5 100

26. “I butted into conversations” (dichotomous, v3_mss_itm26)

v3_mss_recode(v3_clin$v3_mss_s2_mss26_einmischen,v3_con$v3_mss_s2_mss26,"v3_mss_itm26")
##                N    Y  <NA>     
## [1,] No. cases 687  47 809  1543
## [2,] Percent   44.5 3  52.4 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v3_mss_itm27)

v3_mss_recode(v3_clin$v3_mss_s2_mss27_red_pausenlos,v3_con$v3_mss_s2_mss27,"v3_mss_itm27")
##                N    Y   <NA>     
## [1,] No. cases 713  21  809  1543
## [2,] Percent   46.2 1.4 52.4 100

28. “I enjoyed being the centre of attention” (dichotomous, v3_mss_itm28)

v3_mss_recode(v3_clin$v3_mss_s2_mss28_mittelpunkt,v3_con$v3_mss_s2_mss28,"v3_mss_itm28")
##                N    Y   <NA>     
## [1,] No. cases 682  51  810  1543
## [2,] Percent   44.2 3.3 52.5 100

29. “I liked to joke and laugh” (dichotomous, v3_mss_itm29)

v3_mss_recode(v3_clin$v3_mss_s2_mss29_herumalbern,v3_con$v3_mss_s2_mss29,"v3_mss_itm29")
##                N    Y   <NA>     
## [1,] No. cases 630  102 811  1543
## [2,] Percent   40.8 6.6 52.6 100

30. “People found me entertaining” (dichotomous, v3_mss_itm30)

v3_mss_recode(v3_clin$v3_mss_s2_mss30_unterhaltsamer,v3_con$v3_mss_s2_mss30,"v3_mss_itm30")
##                N    Y   <NA>     
## [1,] No. cases 666  67  810  1543
## [2,] Percent   43.2 4.3 52.5 100

31. “I felt as if I was on top of the world” (dichotomous, v3_mss_itm31)

v3_mss_recode(v3_clin$v3_mss_s2_mss31_obenauf,v3_con$v3_mss_s2_mss31,"v3_mss_itm31")
##                N    Y   <NA>     
## [1,] No. cases 660  72  811  1543
## [2,] Percent   42.8 4.7 52.6 100

32. “I was more cheerful than my usual self” (dichotomous, v3_mss_itm32)

v3_mss_recode(v3_clin$v3_mss_s2_mss32_froehlicher,v3_con$v3_mss_s2_mss32,"v3_mss_itm32")
##                N   Y   <NA>     
## [1,] No. cases 602 130 811  1543
## [2,] Percent   39  8.4 52.6 100

33. “Other people got on my nerves” (dichotomous, v3_mss_itm33)

v3_mss_recode(v3_clin$v3_mss_s2_mss33_ungeduldiger,v3_con$v3_mss_s2_mss33,"v3_mss_itm33")
##                N    Y    <NA>     
## [1,] No. cases 569  163  811  1543
## [2,] Percent   36.9 10.6 52.6 100

34. “I was getting into arguments” (dichotomous, v3_mss_itm34)

v3_mss_recode(v3_clin$v3_mss_s2_mss34_streiten,v3_con$v3_mss_s2_mss34,"v3_mss_itm34")
##                N    Y   <NA>     
## [1,] No. cases 677  54  812  1543
## [2,] Percent   43.9 3.5 52.6 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v3_mss_itm35)

v3_mss_recode(v3_clin$v3_mss_s2_mss35_ideen,v3_con$v3_mss_s2_mss35,"v3_mss_itm35")
##                N    Y   <NA>     
## [1,] No. cases 625  108 810  1543
## [2,] Percent   40.5 7   52.5 100

36. “My thoughts raced through my mind” (dichotomous, v3_mss_itm36)

v3_mss_recode(v3_clin$v3_mss_s2_mss36_gedanken,v3_con$v3_mss_s2_mss36,"v3_mss_itm36")
##                N    Y    <NA>     
## [1,] No. cases 558  174  811  1543
## [2,] Percent   36.2 11.3 52.6 100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v3_mss_itm37)

v3_mss_recode(v3_clin$v3_mss_s2_mss37_konzentration,v3_con$v3_mss_s2_mss37,"v3_mss_itm37")
##                N    Y   <NA>     
## [1,] No. cases 630  102 811  1543
## [2,] Percent   40.8 6.6 52.6 100

38. “I thought I was an especially important person” (dichotomous, v3_mss_itm38)

v3_mss_recode(v3_clin$v3_mss_s2_mss38_etw_besonderes,v3_con$v3_mss_s2_mss38,"v3_mss_itm38")
##                N    Y   <NA>     
## [1,] No. cases 683  48  812  1543
## [2,] Percent   44.3 3.1 52.6 100

39. “I thought I could change the world” (dichotomous, v3_mss_itm39)

v3_mss_recode(v3_clin$v3_mss_s2_mss39_welt_veraender,v3_con$v3_mss_s2_mss39,"v3_mss_itm39")
##                N    Y   <NA>     
## [1,] No. cases 687  45  811  1543
## [2,] Percent   44.5 2.9 52.6 100

40. “I thought I was right most of the time” (dichotomous, v3_mss_itm40)

v3_mss_recode(v3_clin$v3_mss_s2_mss40_recht_haben,v3_con$v3_mss_s2_mss40,"v3_mss_itm40")
##                N   Y   <NA>     
## [1,] No. cases 695 36  812  1543
## [2,] Percent   45  2.3 52.6 100

41. “I thought I was superior to others” (dichotomous, v3_mss_itm41)

v3_mss_recode(v3_clin$v3_mss_s3_mss41_ueberlegen,v3_con$v3_mss_s3_mss41,"v3_mss_itm41")
##                N    Y   <NA>     
## [1,] No. cases 711  27  805  1543
## [2,] Percent   46.1 1.7 52.2 100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v3_mss_itm42)

v3_mss_recode(v3_clin$v3_mss_s3_mss42_uebermut,v3_con$v3_mss_s3_mss42,"v3_mss_itm42")
##                N    Y   <NA>     
## [1,] No. cases 682  56  805  1543
## [2,] Percent   44.2 3.6 52.2 100

43. “I thought I knew what other people were thinking” (dichotomous, v3_mss_itm43)

v3_mss_recode(v3_clin$v3_mss_s3_mss43_ged_lesen_akt,v3_con$v3_mss_s3_mss43,"v3_mss_itm43")
##                N    Y   <NA>     
## [1,] No. cases 684  55  804  1543
## [2,] Percent   44.3 3.6 52.1 100

44. “I thought other people knew what I was thinking” (dichotomous, v3_mss_itm44)

v3_mss_recode(v3_clin$v3_mss_s3_mss44_ged_lesen_pas,v3_con$v3_mss_s3_mss44,"v3_mss_itm44")
##                N    Y   <NA>     
## [1,] No. cases 698  38  807  1543
## [2,] Percent   45.2 2.5 52.3 100

45. “I thought someone wanted to harm me” (dichotomous, v3_mss_itm45)

v3_mss_recode(v3_clin$v3_mss_s3_mss45_etw_antun,v3_con$v3_mss_s3_mss45,"v3_mss_itm45")
##                N    Y   <NA>     
## [1,] No. cases 704  34  805  1543
## [2,] Percent   45.6 2.2 52.2 100

46. “I heard voices when people weren’t there” (dichotomous, v3_mss_itm46)

v3_mss_recode(v3_clin$v3_mss_s3_mss46_stimmen,v3_con$v3_mss_s3_mss46,"v3_mss_itm46")
##                N    Y  <NA>     
## [1,] No. cases 693  46 804  1543
## [2,] Percent   44.9 3  52.1 100

47. “I had false beliefs concerning who I was” (dichotomous, v3_mss_itm47)

v3_mss_recode(v3_clin$v3_mss_s3_mss47_jmd_anders,v3_con$v3_mss_s3_mss47,"v3_mss_itm47")
##                N    Y   <NA>     
## [1,] No. cases 716  22  805  1543
## [2,] Percent   46.4 1.4 52.2 100

48. “I knew I was getting ill” (dichotomous, v3_mss_itm48)

v3_mss_recode(v3_clin$v3_mss_s3_mss48_krank_einsicht,v3_con$v3_mss_s3_mss48,"v3_mss_itm48")
##                N    Y   <NA>     
## [1,] No. cases 658  73  812  1543
## [2,] Percent   42.6 4.7 52.6 100

Create MSS sum score (continuous [0-48],v3_mss_sum)

v3_mss_sum<-ifelse(v3_mss_itm1=="Y",1,0)+
            ifelse(v3_mss_itm2=="Y",1,0)+
            ifelse(v3_mss_itm3=="Y",1,0)+
            ifelse(v3_mss_itm4=="Y",1,0)+
            ifelse(v3_mss_itm5=="Y",1,0)+
            ifelse(v3_mss_itm6=="Y",1,0)+
            ifelse(v3_mss_itm7=="Y",1,0)+
            ifelse(v3_mss_itm8=="Y",1,0)+
            ifelse(v3_mss_itm9=="Y",1,0)+
            ifelse(v3_mss_itm10=="Y",1,0)+
            ifelse(v3_mss_itm11=="Y",1,0)+
            ifelse(v3_mss_itm12=="Y",1,0)+
            ifelse(v3_mss_itm13=="Y",1,0)+
            ifelse(v3_mss_itm14=="Y",1,0)+
            ifelse(v3_mss_itm15=="Y",1,0)+
            ifelse(v3_mss_itm16=="Y",1,0)+
            ifelse(v3_mss_itm17=="Y",1,0)+
            ifelse(v3_mss_itm18=="Y",1,0)+
            ifelse(v3_mss_itm19=="Y",1,0)+
            ifelse(v3_mss_itm20=="Y",1,0)+
            ifelse(v3_mss_itm21=="Y",1,0)+
            ifelse(v3_mss_itm22=="Y",1,0)+
            ifelse(v3_mss_itm23=="Y",1,0)+
            ifelse(v3_mss_itm24=="Y",1,0)+
            ifelse(v3_mss_itm25=="Y",1,0)+
            ifelse(v3_mss_itm26=="Y",1,0)+
            ifelse(v3_mss_itm27=="Y",1,0)+
            ifelse(v3_mss_itm28=="Y",1,0)+
            ifelse(v3_mss_itm29=="Y",1,0)+
            ifelse(v3_mss_itm30=="Y",1,0)+
            ifelse(v3_mss_itm31=="Y",1,0)+
            ifelse(v3_mss_itm32=="Y",1,0)+
            ifelse(v3_mss_itm33=="Y",1,0)+
            ifelse(v3_mss_itm34=="Y",1,0)+
            ifelse(v3_mss_itm35=="Y",1,0)+
            ifelse(v3_mss_itm36=="Y",1,0)+
            ifelse(v3_mss_itm37=="Y",1,0)+
            ifelse(v3_mss_itm38=="Y",1,0)+
            ifelse(v3_mss_itm39=="Y",1,0)+
            ifelse(v3_mss_itm40=="Y",1,0)+
            ifelse(v3_mss_itm41=="Y",1,0)+
            ifelse(v3_mss_itm42=="Y",1,0)+
            ifelse(v3_mss_itm43=="Y",1,0)+
            ifelse(v3_mss_itm44=="Y",1,0)+
            ifelse(v3_mss_itm45=="Y",1,0)+
            ifelse(v3_mss_itm46=="Y",1,0)+
            ifelse(v3_mss_itm47=="Y",1,0)+
            ifelse(v3_mss_itm48=="Y",1,0)

summary(v3_mss_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   2.000   4.233   6.000  33.000     887

Create dataset

v3_mss<-data.frame(v3_mss_itm1,v3_mss_itm2,v3_mss_itm3,v3_mss_itm4,v3_mss_itm5,v3_mss_itm6,
                   v3_mss_itm7,v3_mss_itm8,v3_mss_itm9,v3_mss_itm10,v3_mss_itm11,
                   v3_mss_itm12,v3_mss_itm13,v3_mss_itm14,v3_mss_itm15,v3_mss_itm16,
                   v3_mss_itm17,v3_mss_itm18,v3_mss_itm19,v3_mss_itm20,v3_mss_itm21,
                   v3_mss_itm22,v3_mss_itm23,v3_mss_itm24,v3_mss_itm25,v3_mss_itm26,
                   v3_mss_itm27,v3_mss_itm28,v3_mss_itm29,v3_mss_itm30,v3_mss_itm31,
                   v3_mss_itm32,v3_mss_itm33,v3_mss_itm34,v3_mss_itm35,v3_mss_itm36,
                   v3_mss_itm37,v3_mss_itm38,v3_mss_itm39,v3_mss_itm40,v3_mss_itm41,
                   v3_mss_itm42,v3_mss_itm43,v3_mss_itm44,v3_mss_itm45,v3_mss_itm46,
                   v3_mss_itm47,v3_mss_itm48, v3_mss_sum)

LEQ

For explanation, please refer to the section in Visit 1

Health

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v3_leq_A_1A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq1a_schw_krankh,v3_con$v3_leq_a_leq1a,"v3_leq_A_1A")
##                -999 bad  good <NA>     
## [1,] No. cases 515  157  24   847  1543
## [2,] Percent   33.4 10.2 1.6  54.9 100

1B Impact (ordinal [0,1,2,3], v3_leq_A_1B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq1e_schw_krankh,v3_con$v3_leq_a_leq1e,"v3_leq_A_1B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 510  14  28  50  94  847  1543
## [2,] Percent   33.1 0.9 1.8 3.2 6.1 54.9 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v3_leq_A_2A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq2a_ernaehrung,v3_con$v3_leq_a_leq2a,"v3_leq_A_2A")
##                -999 bad good <NA>     
## [1,] No. cases 530  76  90   847  1543
## [2,] Percent   34.3 4.9 5.8  54.9 100

2B Impact (ordinal [0,1,2,3], v3_leq_A_2B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq2e_ernaehrung,v3_con$v3_leq_a_leq2e,"v3_leq_A_2B")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 524  15 45  54  58  847  1543
## [2,] Percent   34   1  2.9 3.5 3.8 54.9 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v3_leq_A_3A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq3a_schlaf,v3_con$v3_leq_a_leq3a,"v3_leq_A_3A")
##                -999 bad good <NA>     
## [1,] No. cases 524  110 62   847  1543
## [2,] Percent   34   7.1 4    54.9 100

3B Impact (ordinal [0,1,2,3], v3_leq_A_3B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq3e_schlaf,v3_con$v3_leq_a_leq3e,"v3_leq_A_3B")
##                -999 0  1   2   3   <NA>     
## [1,] No. cases 522  16 26  63  69  847  1543
## [2,] Percent   33.8 1  1.7 4.1 4.5 54.9 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v3_leq_A_4A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq4a_freizeit,v3_con$v3_leq_a_leq4a,"v3_leq_A_4A")
##                -999 bad good <NA>     
## [1,] No. cases 478  71  147  847  1543
## [2,] Percent   31   4.6 9.5  54.9 100

4B Impact (ordinal [0,1,2,3], v3_leq_A_4B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq4e_freizeit,v3_con$v3_leq_a_leq4e,"v3_leq_A_4B")
##                -999 0   1  2  3   <NA>     
## [1,] No. cases 473  6   46 93 78  847  1543
## [2,] Percent   30.7 0.4 3  6  5.1 54.9 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v3_leq_A_5A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq5a_zahnarzt,v3_con$v3_leq_a_leq5a,"v3_leq_A_5A")
##                -999 bad good <NA>     
## [1,] No. cases 602  39  55   847  1543
## [2,] Percent   39   2.5 3.6  54.9 100

5B Impact (ordinal [0,1,2,3], v3_leq_A_5B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq5e_zahnarzt,v3_con$v3_leq_a_leq5e,"v3_leq_A_5B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 598  21  23  28  26  847  1543
## [2,] Percent   38.8 1.4 1.5 1.8 1.7 54.9 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v3_leq_A_6A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq6a_schwanger,v3_con$v3_leq_a_leq6a,"v3_leq_A_6A")
##                -999 bad good <NA>     
## [1,] No. cases 692  1   3    847  1543
## [2,] Percent   44.8 0.1 0.2  54.9 100

6B Impact (ordinal [0,1,2,3], v3_leq_A_6B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq6e_schwanger,v3_con$v3_leq_a_leq6e,"v3_leq_A_6B")
##                -999 0   1   3   <NA>     
## [1,] No. cases 692  1   1   2   847  1543
## [2,] Percent   44.8 0.1 0.1 0.1 54.9 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v3_leq_A_7A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq7a_fehlg_abtr,v3_con$v3_leq_a_leq7a,"v3_leq_A_7A")
##                -999 bad <NA>     
## [1,] No. cases 695  1   847  1543
## [2,] Percent   45   0.1 54.9 100

7B Impact (ordinal [0,1,2,3], v3_leq_A_7B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq7e_fehlg_abtr,v3_con$v3_leq_a_leq7e,"v3_leq_A_7B")
##                -999 1   <NA>     
## [1,] No. cases 695  1   847  1543
## [2,] Percent   45   0.1 54.9 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v3_leq_A_8A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq8a_wechseljahre,v3_con$v3_leq_a_leq8a,"v3_leq_A_8A")
##                -999 bad good <NA>     
## [1,] No. cases 668  21  7    847  1543
## [2,] Percent   43.3 1.4 0.5  54.9 100

8B Impact (ordinal [0,1,2,3], v3_leq_A_8B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq8e_wechseljahre,v3_con$v3_leq_a_leq8e,"v3_leq_A_8B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 667  4   6   14  5   847  1543
## [2,] Percent   43.2 0.3 0.4 0.9 0.3 54.9 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v3_leq_A_9A)

v3_leq_a_recode(v3_clin$v3_leq_a_leq9a_verhuetung,v3_con$v3_leq_a_leq9a,"v3_leq_A_9A")
##                -999 bad good <NA>     
## [1,] No. cases 683  10  3    847  1543
## [2,] Percent   44.3 0.6 0.2  54.9 100

9B Impact (ordinal [0,1,2,3], v3_leq_A_9B)

v3_leq_b_recode(v3_clin$v3_leq_a_leq9e_verhuetung,v3_con$v3_leq_a_leq9e,"v3_leq_A_9B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 683  2   2   5   4   847  1543
## [2,] Percent   44.3 0.1 0.1 0.3 0.3 54.9 100

Create dataset

v3_leq_A<-data.frame(v3_leq_A_1A,v3_leq_A_1B,v3_leq_A_2A,v3_leq_A_2B,v3_leq_A_3A,
                     v3_leq_A_3B,v3_leq_A_4A,v3_leq_A_4B,v3_leq_A_5A,v3_leq_A_5B,
                     v3_leq_A_6A,v3_leq_A_6B,v3_leq_A_7A,v3_leq_A_7B,v3_leq_A_8A,
                     v3_leq_A_8B,v3_leq_A_9A,v3_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v3_leq_B_10A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq10a_arbeitssuche,v3_con$v3_leq_b_leq10a,"v3_leq_B_10A")
##                -999 bad good <NA>     
## [1,] No. cases 596  76  24   847  1543
## [2,] Percent   38.6 4.9 1.6  54.9 100

10B Impact (ordinal [0,1,2,3], v3_leq_B_10B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq10e_arbeitssuche,v3_con$v3_leq_b_leq10e,"v3_leq_B_10B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 595  7   29  33  32  847  1543
## [2,] Percent   38.6 0.5 1.9 2.1 2.1 54.9 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v3_leq_B_11A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq11a_arbeit_aussen,v3_con$v3_leq_b_leq11a,"v3_leq_B_11A")
##                -999 bad good <NA>     
## [1,] No. cases 611  13  72   847  1543
## [2,] Percent   39.6 0.8 4.7  54.9 100

11B Impact (ordinal [0,1,2,3], v3_leq_B_11B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq11e_arbeit_aussen,v3_con$v3_leq_b_leq11e,"v3_leq_B_11B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 608  5   15 32  36  847  1543
## [2,] Percent   39.4 0.3 1  2.1 2.3 54.9 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v3_leq_B_12A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq12a_arbeitswechs,v3_con$v3_leq_b_leq12a,"v3_leq_B_12A")
##                -999 bad good <NA>     
## [1,] No. cases 607  12  77   847  1543
## [2,] Percent   39.3 0.8 5    54.9 100

12B Impact (ordinal [0,1,2,3], v3_leq_B_12B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq12e_arbeitswechs,v3_con$v3_leq_b_leq12e,"v3_leq_B_12B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 605  7   14  27  43  847  1543
## [2,] Percent   39.2 0.5 0.9 1.7 2.8 54.9 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v3_leq_B_13A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq13a_veraend_arb,v3_con$v3_leq_b_leq13a,"v3_leq_B_13A")
##                -999 bad good <NA>     
## [1,] No. cases 567  31  98   847  1543
## [2,] Percent   36.7 2   6.4  54.9 100

13B Impact (ordinal [0,1,2,3], v3_leq_B_13B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq13e_veraend_arb,v3_con$v3_leq_b_leq13e,"v3_leq_B_13B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 566  7   37  49  37  847  1543
## [2,] Percent   36.7 0.5 2.4 3.2 2.4 54.9 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v3_leq_B_14A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq14a_veraend_ba,v3_con$v3_leq_b_leq14a,"v3_leq_B_14A")
##                -999 bad good <NA>     
## [1,] No. cases 565  22  109  847  1543
## [2,] Percent   36.6 1.4 7.1  54.9 100

14B Impact (ordinal [0,1,2,3], v3_leq_B_14B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq14e_veraend_ba,v3_con$v3_leq_b_leq14e,"v3_leq_B_14B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 562  8   32  54  40  847  1543
## [2,] Percent   36.4 0.5 2.1 3.5 2.6 54.9 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v3_leq_B_15A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq15a_schw_arbeit,v3_con$v3_leq_b_leq15a,"v3_leq_B_15A")
##                -999 bad good <NA>     
## [1,] No. cases 610  71  15   847  1543
## [2,] Percent   39.5 4.6 1    54.9 100

15B Impact (ordinal [0,1,2,3], v3_leq_B_15B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq15e_schw_arbeit,v3_con$v3_leq_b_leq15e,"v3_leq_B_15B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 609  12  32  25  18  847  1543
## [2,] Percent   39.5 0.8 2.1 1.6 1.2 54.9 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v3_leq_B_16A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq16a_betr_reorg,v3_con$v3_leq_b_leq16a,"v3_leq_B_16A")
##                -999 bad good <NA>     
## [1,] No. cases 664  18  14   847  1543
## [2,] Percent   43   1.2 0.9  54.9 100

16B Impact (ordinal [0,1,2,3], v3_leq_B_16B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq16e_betr_reorg,v3_con$v3_leq_b_leq16e,"v3_leq_B_16B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 663  6   6   8   13  847  1543
## [2,] Percent   43   0.4 0.4 0.5 0.8 54.9 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v3_leq_B_17A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq17a_kuendigung,v3_con$v3_leq_b_leq17a,"v3_leq_B_17A")
##                -999 bad good <NA>     
## [1,] No. cases 650  26  20   847  1543
## [2,] Percent   42.1 1.7 1.3  54.9 100

17B Impact (ordinal [0,1,2,3], v3_leq_B_17B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq17e_kuendigung,v3_con$v3_leq_b_leq17e,"v3_leq_B_17B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 649  4   9   15 19  847  1543
## [2,] Percent   42.1 0.3 0.6 1  1.2 54.9 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v3_leq_B_18A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq18a_ende_beruf,v3_con$v3_leq_b_leq18a,"v3_leq_B_18A")
##                -999 bad good <NA>     
## [1,] No. cases 676  7   13   847  1543
## [2,] Percent   43.8 0.5 0.8  54.9 100

18B Impact (ordinal [0,1,2,3], v3_leq_B_18B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq18e_ende_beruf,v3_con$v3_leq_b_leq18e,"v3_leq_B_18B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 676  1   5   5   9   847  1543
## [2,] Percent   43.8 0.1 0.3 0.3 0.6 54.9 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v3_leq_B_19A)

v3_leq_a_recode(v3_clin$v3_leq_b_leq19a_fortbildung,v3_con$v3_leq_b_leq19a,"v3_leq_B_19A")
##                -999 bad good <NA>     
## [1,] No. cases 656  5   35   847  1543
## [2,] Percent   42.5 0.3 2.3  54.9 100

19B Impact (ordinal [0,1,2,3], v3_leq_B_19B)

v3_leq_b_recode(v3_clin$v3_leq_b_leq19e_fortbildung,v3_con$v3_leq_b_leq19e,"v3_leq_B_19B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 656  5   12  13  10  847  1543
## [2,] Percent   42.5 0.3 0.8 0.8 0.6 54.9 100
v3_leq_B<-data.frame(v3_leq_B_10A,v3_leq_B_10B,v3_leq_B_11A,v3_leq_B_11B,v3_leq_B_12A,
                     v3_leq_B_12B,v3_leq_B_13A,v3_leq_B_13B,v3_leq_B_14A,v3_leq_B_14B,
                     v3_leq_B_15A,v3_leq_B_15B,v3_leq_B_16A,v3_leq_B_16B,v3_leq_B_17A,
                     v3_leq_B_17B,v3_leq_B_18A,v3_leq_B_18B,v3_leq_B_19A,v3_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v3_leq_C_20A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq20a_beginn_ende,v3_con$v3_leq_c_d_leq20a,"v3_leq_C_20A")
##                -999 bad good <NA>     
## [1,] No. cases 654  5   37   847  1543
## [2,] Percent   42.4 0.3 2.4  54.9 100

20B Impact (ordinal [0,1,2,3], v3_leq_C_20B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq20e_beginn_ende,v3_con$v3_leq_c_d_leq20e,"v3_leq_C_20B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 653  4   7   8   24  847  1543
## [2,] Percent   42.3 0.3 0.5 0.5 1.6 54.9 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v3_leq_C_21A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq21a_schulwechsel,v3_con$v3_leq_c_d_leq21a,"v3_leq_C_21A")
##                -999 bad good <NA>     
## [1,] No. cases 690  3   3    847  1543
## [2,] Percent   44.7 0.2 0.2  54.9 100

21B Impact (ordinal [0,1,2,3], v3_leq_C_21B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq21e_schulwechsel,v3_con$v3_leq_c_d_leq21e,"v3_leq_C_21B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 689  1   1   2   3   847  1543
## [2,] Percent   44.7 0.1 0.1 0.1 0.2 54.9 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v3_leq_C_22A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq22a_aend_karriere,v3_con$v3_leq_c_d_leq22a,"v3_leq_C_22A")
##                -999 bad good <NA>     
## [1,] No. cases 668  4   24   847  1543
## [2,] Percent   43.3 0.3 1.6  54.9 100

B Impact (ordinal [0,1,2,3], v3_leq_C_22B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq22e_aend_karriere,v3_con$v3_leq_c_d_leq22e,"v3_leq_C_22B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 667  2   5   9   13  847  1543
## [2,] Percent   43.2 0.1 0.3 0.6 0.8 54.9 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v3_leq_C_23A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq23a_schulprob,v3_con$v3_leq_c_d_leq23a,"v3_leq_C_23A")
##                -999 bad <NA>     
## [1,] No. cases 682  14  847  1543
## [2,] Percent   44.2 0.9 54.9 100

23B Impact (ordinal [0,1,2,3], v3_leq_C_23B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq23e_schulprob,v3_con$v3_leq_c_d_leq23e,"v3_leq_C_23B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 681  2   3   6   4   847  1543
## [2,] Percent   44.1 0.1 0.2 0.4 0.3 54.9 100

Create dataset

v3_leq_C<-data.frame(v3_leq_C_20A,v3_leq_C_20B,v3_leq_C_21A,v3_leq_C_21B,v3_leq_C_22A,v3_leq_C_22B,v3_leq_C_23A,v3_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v3_leq_D_24A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq24a_schw_wsuche,v3_con$v3_leq_c_d_leq24a,"v3_leq_D_24A")
##                -999 bad good <NA>     
## [1,] No. cases 655  33  8    847  1543
## [2,] Percent   42.4 2.1 0.5  54.9 100

24B Impact (ordinal [0,1,2,3], v3_leq_D_24B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq24e_schw_wsuche,v3_con$v3_leq_c_d_leq24e,"v3_leq_D_24B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 655  2   8   11  20  847  1543
## [2,] Percent   42.4 0.1 0.5 0.7 1.3 54.9 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v3_leq_D_25A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq25a_umzug_nah,v3_con$v3_leq_c_d_leq25a,"v3_leq_D_25A")
##                -999 bad good <NA>     
## [1,] No. cases 662  4   30   847  1543
## [2,] Percent   42.9 0.3 1.9  54.9 100

B Impact (ordinal [0,1,2,3], v3_leq_D_25B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq25e_umzug_nah,v3_con$v3_leq_c_d_leq25e,"v3_leq_D_25B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 662  2   2   9   21  847  1543
## [2,] Percent   42.9 0.1 0.1 0.6 1.4 54.9 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v3_leq_D_26A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq26a_umzug_fern,v3_con$v3_leq_c_d_leq26a,"v3_leq_D_26A")
##                -999 bad good <NA>     
## [1,] No. cases 674  3   19   847  1543
## [2,] Percent   43.7 0.2 1.2  54.9 100

26B Impact (ordinal [0,1,2,3], v3_leq_D_26B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq26e_umzug_fern,v3_con$v3_leq_c_d_leq26e,"v3_leq_D_26B")
##                -999 1   2   3  <NA>     
## [1,] No. cases 674  3   3   16 847  1543
## [2,] Percent   43.7 0.2 0.2 1  54.9 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v3_leq_D_27A)

v3_leq_a_recode(v3_clin$v3_leq_c_d_leq27a_veraend_lu,v3_con$v3_leq_c_d_leq27a,"v3_leq_D_27A")
##                -999 bad good <NA>     
## [1,] No. cases 604  35  57   847  1543
## [2,] Percent   39.1 2.3 3.7  54.9 100

27B Impact (ordinal [0,1,2,3], v3_leq_D_27B)

v3_leq_b_recode(v3_clin$v3_leq_c_d_leq27e_veraend_lu,v3_con$v3_leq_c_d_leq27e,"v3_leq_D_27B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 603  4   19  42  28  847  1543
## [2,] Percent   39.1 0.3 1.2 2.7 1.8 54.9 100

Create dataset

v3_leq_D<-data.frame(v3_leq_D_24A,v3_leq_D_24B,v3_leq_D_25A,v3_leq_D_25B,v3_leq_D_26A,
                     v3_leq_D_26B,v3_leq_D_27A,v3_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v3_leq_E_28A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq28a_neue_bez,v3_con$v3_leq_e_leq28a,"v3_leq_E_28A")
##                -999 bad good <NA>     
## [1,] No. cases 634  3   59   847  1543
## [2,] Percent   41.1 0.2 3.8  54.9 100

28B Impact (ordinal [0,1,2,3], v3_leq_E_28B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq28e_neue_bez,v3_con$v3_leq_e_leq28e,"v3_leq_E_28B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 633  4   7   26  26  847  1543
## [2,] Percent   41   0.3 0.5 1.7 1.7 54.9 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v3_leq_E_29A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq29a_verlobung,v3_con$v3_leq_e_leq29a,"v3_leq_E_29A")
##                -999 good <NA>     
## [1,] No. cases 689  7    847  1543
## [2,] Percent   44.7 0.5  54.9 100

29B Impact (ordinal [0,1,2,3], v3_leq_E_29B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq29e_verlobung,v3_con$v3_leq_e_leq29e,"v3_leq_E_29B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 688  1   1   2   4   847  1543
## [2,] Percent   44.6 0.1 0.1 0.1 0.3 54.9 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v3_leq_E_30A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq30a_prob_partner,v3_con$v3_leq_e_leq30a,"v3_leq_E_30A")
##                -999 bad good <NA>     
## [1,] No. cases 611  81  4    847  1543
## [2,] Percent   39.6 5.2 0.3  54.9 100

30B Impact (ordinal [0,1,2,3], v3_leq_E_30B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq30e_prob_partner,v3_con$v3_leq_e_leq30e,"v3_leq_E_30B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 610  4   18  39  25  847  1543
## [2,] Percent   39.5 0.3 1.2 2.5 1.6 54.9 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v3_leq_E_31A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq31a_trennung,v3_con$v3_leq_e_leq31a,"v3_leq_E_31A")
##                -999 bad good <NA>     
## [1,] No. cases 652  27  17   847  1543
## [2,] Percent   42.3 1.7 1.1  54.9 100

31B Impact (ordinal [0,1,2,3], v3_leq_E_31B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq31e_trennung,v3_con$v3_leq_e_leq31e,"v3_leq_E_31B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 652  2   6   14  22  847  1543
## [2,] Percent   42.3 0.1 0.4 0.9 1.4 54.9 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v3_leq_E_32A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq32a_schwanger_p,v3_con$v3_leq_e_leq32a,"v3_leq_E_32A")
##                -999 good <NA>     
## [1,] No. cases 693  3    847  1543
## [2,] Percent   44.9 0.2  54.9 100

32B Impact (ordinal [0,1,2,3], v3_leq_E_32B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq32e_schwanger_p,v3_con$v3_leq_e_leq32e,"v3_leq_E_32B")
##                -999 0   3   <NA>     
## [1,] No. cases 693  1   2   847  1543
## [2,] Percent   44.9 0.1 0.1 54.9 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v3_leq_E_33A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq33a_fehlg_abtr_p,v3_con$v3_leq_e_leq33a,"v3_leq_E_33A")
##                -999 <NA>     
## [1,] No. cases 696  847  1543
## [2,] Percent   45.1 54.9 100

33B Impact (ordinal [0,1,2,3], v3_leq_E_33B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq33e_fehlg_abtr_p,v3_con$v3_leq_e_leq33e,"v3_leq_E_33B")
##                -999 <NA>     
## [1,] No. cases 696  847  1543
## [2,] Percent   45.1 54.9 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v3_leq_E_34A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq34a_heirat,v3_con$v3_leq_e_leq34a,"v3_leq_E_34A")
##                -999 bad good <NA>     
## [1,] No. cases 682  2   12   847  1543
## [2,] Percent   44.2 0.1 0.8  54.9 100

34B Impact (ordinal [0,1,2,3], v3_leq_E_34B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq34e_heirat,v3_con$v3_leq_e_leq34e,"v3_leq_E_34B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 682  2   4   5   3   847  1543
## [2,] Percent   44.2 0.1 0.3 0.3 0.2 54.9 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v3_leq_E_35A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq35a_veraend_naehe,v3_con$v3_leq_e_leq35a,"v3_leq_E_35A")
##                -999 bad good <NA>     
## [1,] No. cases 626  31  39   847  1543
## [2,] Percent   40.6 2   2.5  54.9 100

35B Impact (ordinal [0,1,2,3], v3_leq_E_35B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq35e_veraend_naehe,v3_con$v3_leq_e_leq35e,"v3_leq_E_35B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 625  3   12  26  30  847  1543
## [2,] Percent   40.5 0.2 0.8 1.7 1.9 54.9 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v3_leq_E_36A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq36a_untreue,v3_con$v3_leq_e_leq36a,"v3_leq_E_36A")
##                -999 bad good <NA>     
## [1,] No. cases 681  14  1    847  1543
## [2,] Percent   44.1 0.9 0.1  54.9 100

36B Impact (ordinal [0,1,2,3], v3_leq_E_36B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq36e_untreue,v3_con$v3_leq_e_leq36e,"v3_leq_E_36B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 680  1   6   4   5   847  1543
## [2,] Percent   44.1 0.1 0.4 0.3 0.3 54.9 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v3_leq_E_37A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq37a_konf_schwiege,v3_con$v3_leq_e_leq37a,"v3_leq_E_37A")
##                -999 bad good <NA>     
## [1,] No. cases 670  24  2    847  1543
## [2,] Percent   43.4 1.6 0.1  54.9 100

37B Impact (ordinal [0,1,2,3], v3_leq_E_37B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq37e_konf_schwiege,v3_con$v3_leq_e_leq37e,"v3_leq_E_37B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 669  2   11  7   7   847  1543
## [2,] Percent   43.4 0.1 0.7 0.5 0.5 54.9 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v3_leq_E_38A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq38a_trennung_str,v3_con$v3_leq_e_leq38a,"v3_leq_E_38A")
##                -999 bad good <NA>     
## [1,] No. cases 688  4   4    847  1543
## [2,] Percent   44.6 0.3 0.3  54.9 100

38B Impact (ordinal [0,1,2,3], v3_leq_E_38B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq38e_trennung_str,v3_con$v3_leq_e_leq38e,"v3_leq_E_38B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 686  2   2   2   4   847  1543
## [2,] Percent   44.5 0.1 0.1 0.1 0.3 54.9 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v3_leq_E_39A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq39a_trennung_ber,v3_con$v3_leq_e_leq39a,"v3_leq_E_39A")
##                -999 bad good <NA>     
## [1,] No. cases 692  3   1    847  1543
## [2,] Percent   44.8 0.2 0.1  54.9 100

39B Impact (ordinal [0,1,2,3], v3_leq_E_39B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq39e_trennung_ber,v3_con$v3_leq_e_leq39e,"v3_leq_E_39B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 691  2   2   1   847  1543
## [2,] Percent   44.8 0.1 0.1 0.1 54.9 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v3_leq_E_40A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq40a_versoehnung,v3_con$v3_leq_e_leq40a,"v3_leq_E_40A")
##                -999 good <NA>     
## [1,] No. cases 681  15   847  1543
## [2,] Percent   44.1 1    54.9 100

40B Impact (ordinal [0,1,2,3], v3_leq_E_40B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq40a_versoehnung,v3_con$v3_leq_e_leq40e,"v3_leq_E_40B")
##                -999 1   2   3   <NA>     
## [1,] No. cases 681  13  1   1   847  1543
## [2,] Percent   44.1 0.8 0.1 0.1 54.9 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v3_leq_E_41A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq41a_scheidung,v3_con$v3_leq_e_leq41a,"v3_leq_E_41A")
##                -999 bad good <NA>     
## [1,] No. cases 688  4   4    847  1543
## [2,] Percent   44.6 0.3 0.3  54.9 100

41B Impact (ordinal [0,1,2,3], v3_leq_E_41B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq41e_scheidung,v3_con$v3_leq_e_leq41e,"v3_leq_E_41B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 687  3   2   4   847  1543
## [2,] Percent   44.5 0.2 0.1 0.3 54.9 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v3_leq_E_42A)

v3_leq_a_recode(v3_clin$v3_leq_e_leq42a_veraend_taet,v3_con$v3_leq_e_leq42a,"v3_leq_E_42A")
##                -999 bad good <NA>     
## [1,] No. cases 667  6   23   847  1543
## [2,] Percent   43.2 0.4 1.5  54.9 100

42B Impact (ordinal [0,1,2,3], v3_leq_E_42B)

v3_leq_b_recode(v3_clin$v3_leq_e_leq42e_veraend_taet,v3_con$v3_leq_e_leq42e,"v3_leq_E_42B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 666  2   8   11  9   847  1543
## [2,] Percent   43.2 0.1 0.5 0.7 0.6 54.9 100

Create dataset

v3_leq_E<-data.frame(v3_leq_E_28A,v3_leq_E_28B,v3_leq_E_29A,v3_leq_E_29B,v3_leq_E_30A,
                     v3_leq_E_30B,v3_leq_E_31A,v3_leq_E_31B,v3_leq_E_32A,v3_leq_E_32B,
                     v3_leq_E_33A,v3_leq_E_33B,v3_leq_E_34A,v3_leq_E_34B,v3_leq_E_35A,
                     v3_leq_E_35B,v3_leq_E_36A,v3_leq_E_36B,v3_leq_E_37A,v3_leq_E_37B,
                     v3_leq_E_38A,v3_leq_E_38B,v3_leq_E_39A,v3_leq_E_39B,v3_leq_E_40A,
                     v3_leq_E_40B,v3_leq_E_41A,v3_leq_E_41B,v3_leq_E_42A,v3_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v3_leq_F_43A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq43a_neu_fmitglied,v3_con$v3_leq_f_g_leq43a,"v3_leq_F_43A")
##                -999 bad good <NA>     
## [1,] No. cases 659  3   34   847  1543
## [2,] Percent   42.7 0.2 2.2  54.9 100

43B Impact (ordinal [0,1,2,3], v3_leq_F_43B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq43e_neu_fmitglied,v3_con$v3_leq_f_g_leq43e,"v3_leq_F_43B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 658  6   9   12  11  847  1543
## [2,] Percent   42.6 0.4 0.6 0.8 0.7 54.9 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v3_leq_F_44A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq44a_auszug_fm,v3_con$v3_leq_f_g_leq44a,"v3_leq_F_44A")
##                -999 bad good <NA>     
## [1,] No. cases 680  6   10   847  1543
## [2,] Percent   44.1 0.4 0.6  54.9 100

44B Impact (ordinal [0,1,2,3], v3_leq_F_44B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq44e_auszug_fm,v3_con$v3_leq_f_g_leq44e,"v3_leq_F_44B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 679  3   4   4   6   847  1543
## [2,] Percent   44   0.2 0.3 0.3 0.4 54.9 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v3_leq_F_45A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq45a_gz_verh_fm,v3_con$v3_leq_f_g_leq45a,"v3_leq_F_45A")
##                -999 bad good <NA>     
## [1,] No. cases 585  99  12   847  1543
## [2,] Percent   37.9 6.4 0.8  54.9 100

45B Impact (ordinal [0,1,2,3], v3_leq_F_45B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq45e_gz_verh_fm,v3_con$v3_leq_f_g_leq45e,"v3_leq_F_45B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 584  7   25  43  37  847  1543
## [2,] Percent   37.8 0.5 1.6 2.8 2.4 54.9 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v3_leq_F_46A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq46a_tod_partner,v3_con$v3_leq_f_g_leq46a,"v3_leq_F_46A")
##                -999 bad <NA>     
## [1,] No. cases 695  1   847  1543
## [2,] Percent   45   0.1 54.9 100

46B Impact (ordinal [0,1,2,3], v3_leq_F_46B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq46e_tod_partner,v3_con$v3_leq_f_g_leq46e,"v3_leq_F_46B")
##                -999 0   2   <NA>     
## [1,] No. cases 693  2   1   847  1543
## [2,] Percent   44.9 0.1 0.1 54.9 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v3_leq_F_47A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq47a_tod_kind,v3_con$v3_leq_f_g_leq47a,"v3_leq_F_47A")
##                -999 bad <NA>     
## [1,] No. cases 694  2   847  1543
## [2,] Percent   45   0.1 54.9 100

47B Impact (ordinal [0,1,2,3], v3_leq_F_47B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq47e_tod_kind,v3_con$v3_leq_f_g_leq47e,"v3_leq_F_47B")
##                -999 0   3   <NA>     
## [1,] No. cases 692  2   2   847  1543
## [2,] Percent   44.8 0.1 0.1 54.9 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v3_leq_F_48A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq48a_tod_fm_ef,v3_con$v3_leq_f_g_leq48a,"v3_leq_F_48A")
##                -999 bad good <NA>     
## [1,] No. cases 637  55  4    847  1543
## [2,] Percent   41.3 3.6 0.3  54.9 100

48B Impact (ordinal [0,1,2,3], v3_leq_F_48B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq48e_tod_fm_ef,v3_con$v3_leq_f_g_leq48e,"v3_leq_F_48B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 635  10  15 14  22  847  1543
## [2,] Percent   41.2 0.6 1  0.9 1.4 54.9 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v3_leq_F_49A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq49a_geb_enkel,v3_con$v3_leq_f_g_leq49a,"v3_leq_F_49A")
##                -999 good <NA>     
## [1,] No. cases 683  13   847  1543
## [2,] Percent   44.3 0.8  54.9 100

49B Impact (ordinal [0,1,2,3], v3_leq_F_49B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq49e_geb_enkel,v3_con$v3_leq_f_g_leq49e,"v3_leq_F_49B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 682  1   4   2   7   847  1543
## [2,] Percent   44.2 0.1 0.3 0.1 0.5 54.9 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v3_leq_F_50A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq50a_fstand_eltern,v3_con$v3_leq_f_g_leq50a,"v3_leq_F_50A")
##                -999 bad good <NA>     
## [1,] No. cases 691  3   2    847  1543
## [2,] Percent   44.8 0.2 0.1  54.9 100

50B Impact (ordinal [0,1,2,3], v3_leq_F_50B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq50e_fstand_eltern,v3_con$v3_leq_f_g_leq50e,"v3_leq_F_50B")
##                -999 0   1   3   <NA>     
## [1,] No. cases 690  1   2   3   847  1543
## [2,] Percent   44.7 0.1 0.1 0.2 54.9 100

Create dataset

v3_leq_F<-data.frame(v3_leq_F_43A,v3_leq_F_43B,v3_leq_F_44A,v3_leq_F_44B,v3_leq_F_45A,
                     v3_leq_F_45B,v3_leq_F_46A,v3_leq_F_46B,v3_leq_F_47A,v3_leq_F_47B,
                     v3_leq_F_48A,v3_leq_F_48B,v3_leq_F_49A,v3_leq_F_49B,v3_leq_F_50A,
                     v3_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v3_leq_G_51A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq51a_kindbetr,v3_con$v3_leq_f_g_leq51a,"v3_leq_G_51A")
##                -999 bad good <NA>     
## [1,] No. cases 680  3   13   847  1543
## [2,] Percent   44.1 0.2 0.8  54.9 100

51B Impact (ordinal [0,1,2,3], v3_leq_G_51B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq51e_kindbetr,v3_con$v3_leq_f_g_leq51e,"v3_leq_G_51B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 679  1   4   4   8   847  1543
## [2,] Percent   44   0.1 0.3 0.3 0.5 54.9 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v3_leq_G_52A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq52a_konf_eschaft,v3_con$v3_leq_f_g_leq52a,"v3_leq_G_52A")
##                -999 bad good <NA>     
## [1,] No. cases 687  7   2    847  1543
## [2,] Percent   44.5 0.5 0.1  54.9 100

52B Impact (ordinal [0,1,2,3], v3_leq_G_52B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq52e_konf_eschaft,v3_con$v3_leq_f_g_leq52e,"v3_leq_G_52B")
##                -999 1   2   3   <NA>     
## [1,] No. cases 686  5   4   1   847  1543
## [2,] Percent   44.5 0.3 0.3 0.1 54.9 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v3_leq_G_53A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq53a_konf_geltern,v3_con$v3_leq_f_g_leq53a,"v3_leq_G_53A")
##                -999 bad good <NA>     
## [1,] No. cases 689  6   1    847  1543
## [2,] Percent   44.7 0.4 0.1  54.9 100

53B Impact (ordinal [0,1,2,3], v3_leq_G_53B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq53e_konf_geltern,v3_con$v3_leq_f_g_leq53e,"v3_leq_G_53B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 688  1   1   2   4   847  1543
## [2,] Percent   44.6 0.1 0.1 0.1 0.3 54.9 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v3_leq_G_54A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq54a_alleinerz,v3_con$v3_leq_f_g_leq54a,"v3_leq_G_54A")
##                -999 good <NA>     
## [1,] No. cases 692  4    847  1543
## [2,] Percent   44.8 0.3  54.9 100

54B Impact (ordinal [0,1,2,3], v3_leq_G_54B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq54e_alleinerz,v3_con$v3_leq_f_g_leq54e,"v3_leq_G_54B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 691  1   2   2   847  1543
## [2,] Percent   44.8 0.1 0.1 0.1 54.9 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v3_leq_G_55A)

v3_leq_a_recode(v3_clin$v3_leq_f_g_leq55a_sorgerecht,v3_con$v3_leq_f_g_leq55a,"v3_leq_G_55A")
##                -999 bad good <NA>     
## [1,] No. cases 689  6   1    847  1543
## [2,] Percent   44.7 0.4 0.1  54.9 100

55B Impact (ordinal [0,1,2,3], v3_leq_G_55B)

v3_leq_b_recode(v3_clin$v3_leq_f_g_leq55e_sorgerecht,v3_con$v3_leq_f_g_leq55e,"v3_leq_G_55B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 688  2   2   1   3   847  1543
## [2,] Percent   44.6 0.1 0.1 0.1 0.2 54.9 100

Create dataset

v3_leq_G<-data.frame(v3_leq_G_51A,v3_leq_G_51B,v3_leq_G_52A,v3_leq_G_52B,v3_leq_G_53A,
                     v3_leq_G_53B,v3_leq_G_54A,v3_leq_G_54B,v3_leq_G_55A,v3_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v3_leq_H_56A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq56a_pers_leistung,v3_con$v3_leq_h_leq56a,"v3_leq_H_56A")
##                -999 bad good <NA>     
## [1,] No. cases 570  6   120  847  1543
## [2,] Percent   36.9 0.4 7.8  54.9 100

56B Impact (ordinal [0,1,2,3], v3_leq_H_56B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq56e_pers_leistung,v3_con$v3_leq_h_leq56e,"v3_leq_H_56B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 567  4   24  53  48  847  1543
## [2,] Percent   36.7 0.3 1.6 3.4 3.1 54.9 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v3_leq_H_57A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq57a_wicht_entsch,v3_con$v3_leq_h_leq57a,"v3_leq_H_57A")
##                -999 bad good <NA>     
## [1,] No. cases 505  24  167  847  1543
## [2,] Percent   32.7 1.6 10.8 54.9 100

57B Impact (ordinal [0,1,2,3], v3_leq_H_57B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq57e_wicht_entsch,v3_con$v3_leq_h_leq57e,"v3_leq_H_57B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 503  1   34  69  89  847  1543
## [2,] Percent   32.6 0.1 2.2 4.5 5.8 54.9 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v3_leq_H_58A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq58a_pers_gewohn,v3_con$v3_leq_h_leq58a,"v3_leq_H_58A")
##                -999 bad good <NA>     
## [1,] No. cases 562  16  118  847  1543
## [2,] Percent   36.4 1   7.6  54.9 100

58B Impact (ordinal [0,1,2,3], v3_leq_H_58B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq58e_pers_gewohn,v3_con$v3_leq_h_leq58e,"v3_leq_H_58B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 561  6   38  48  43  847  1543
## [2,] Percent   36.4 0.4 2.5 3.1 2.8 54.9 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v3_leq_H_59A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq59a_relig_ueberz,v3_con$v3_leq_h_leq59a,"v3_leq_H_59A")
##                -999 bad good <NA>     
## [1,] No. cases 669  1   26   847  1543
## [2,] Percent   43.4 0.1 1.7  54.9 100

59B Impact (ordinal [0,1,2,3], v3_leq_H_59B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq59e_relig_ueberz,v3_con$v3_leq_h_leq59e,"v3_leq_H_59B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 667  5   5   11  8   847  1543
## [2,] Percent   43.2 0.3 0.3 0.7 0.5 54.9 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v3_leq_H_60A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq60a_pol_ansichten,v3_clin$v3_leq_h_leq60a,"v3_leq_H_60A")
## Warning in (is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con != 2) &
## is.na(leq_con_old_name) == : longer object length is not a multiple of
## shorter object length
## Warning in (is.na(v3_itm_leq_chk_con) | v3_itm_leq_chk_con != 2) &
## is.na(leq_con_old_name): longer object length is not a multiple of shorter
## object length
##                -999 bad good <NA>     
## [1,] No. cases 668  7   21   847  1543
## [2,] Percent   43.3 0.5 1.4  54.9 100

60B Impact (ordinal [0,1,2,3], v3_leq_H_60B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq60e_pol_ansichten,v3_con$v3_leq_h_leq60e,"v3_leq_H_60B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 662  4   16 10  4   847  1543
## [2,] Percent   42.9 0.3 1  0.6 0.3 54.9 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v3_leq_H_61A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq61a_pers_eigent,v3_con$v3_leq_h_leq61a,"v3_leq_H_61A")
##                -999 bad good <NA>     
## [1,] No. cases 659  33  4    847  1543
## [2,] Percent   42.7 2.1 0.3  54.9 100

61B Impact (ordinal [0,1,2,3], v3_leq_H_61B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq61e_pers_eigent,v3_con$v3_leq_h_leq61e,"v3_leq_H_61B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 658  2   15 11  10  847  1543
## [2,] Percent   42.6 0.1 1  0.7 0.6 54.9 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v3_leq_H_62A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq62a_erholungsurl,v3_con$v3_leq_h_leq62a,"v3_leq_H_62A")
##                -999 bad good <NA>     
## [1,] No. cases 476  9   211  847  1543
## [2,] Percent   30.8 0.6 13.7 54.9 100

62B Impact (ordinal [0,1,2,3], v3_leq_H_62B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq62e_erholungsurl,v3_con$v3_leq_h_leq62e,"v3_leq_H_62B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 474  13  37  101 71  847  1543
## [2,] Percent   30.7 0.8 2.4 6.5 4.6 54.9 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v3_leq_H_63A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq63a_reise_andere,v3_con$v3_leq_h_leq63a,"v3_leq_H_63A")
##                -999 bad good <NA>     
## [1,] No. cases 609  6   81   847  1543
## [2,] Percent   39.5 0.4 5.2  54.9 100

63B Impact (ordinal [0,1,2,3], v3_leq_H_63B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq63e_reise_andere,v3_con$v3_leq_h_leq63e,"v3_leq_H_63B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 608  5   24  38  21  847  1543
## [2,] Percent   39.4 0.3 1.6 2.5 1.4 54.9 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v3_leq_H_64A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq64a_familientreff,v3_con$v3_leq_h_leq64a,"v3_leq_H_64A")
##                -999 bad good <NA>     
## [1,] No. cases 640  11  45   847  1543
## [2,] Percent   41.5 0.7 2.9  54.9 100

64B Impact (ordinal [0,1,2,3], v3_leq_H_64B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq64e_familientreff,v3_con$v3_leq_h_leq64e,"v3_leq_H_64B")
##                -999 0   1  2   3  <NA>     
## [1,] No. cases 639  5   16 20  16 847  1543
## [2,] Percent   41.4 0.3 1  1.3 1  54.9 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v3_leq_H_65A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq65a_ges_unternehm,v3_con$v3_leq_h_leq65a,"v3_leq_H_65A")
##                -999 bad good <NA>     
## [1,] No. cases 610  13  73   847  1543
## [2,] Percent   39.5 0.8 4.7  54.9 100

65B Impact (ordinal [0,1,2,3], v3_leq_H_65B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq65e_ges_unternehm,v3_con$v3_leq_h_leq65e,"v3_leq_H_65B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 606  4   28  32  26  847  1543
## [2,] Percent   39.3 0.3 1.8 2.1 1.7 54.9 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v3_leq_H_66A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq66a_neue_freunds,v3_con$v3_leq_h_leq66a,"v3_leq_H_66A")
##                -999 bad good <NA>     
## [1,] No. cases 540  2   154  847  1543
## [2,] Percent   35   0.1 10   54.9 100

66B Impact (ordinal [0,1,2,3], v3_leq_H_66B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq66e_neue_freunds,v3_con$v3_leq_h_leq66e,"v3_leq_H_66B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 538  4   47 63  44  847  1543
## [2,] Percent   34.9 0.3 3  4.1 2.9 54.9 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v3_leq_H_67A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq67a_ende_freunds,v3_con$v3_leq_h_leq67a,"v3_leq_H_67A")
##                -999 bad good <NA>     
## [1,] No. cases 636  42  18   847  1543
## [2,] Percent   41.2 2.7 1.2  54.9 100

67B Impact (ordinal [0,1,2,3], v3_leq_H_67B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq67e_ende_freunds,v3_con$v3_leq_h_leq67e,"v3_leq_H_67B")
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 635  7   26  13  15 847  1543
## [2,] Percent   41.2 0.5 1.7 0.8 1  54.9 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v3_leq_H_68A)

v3_leq_a_recode(v3_clin$v3_leq_h_leq68a_haustier,v3_con$v3_leq_h_leq68a,"v3_leq_H_68A")
##                -999 bad good <NA>     
## [1,] No. cases 657  16  23   847  1543
## [2,] Percent   42.6 1   1.5  54.9 100

68B Impact (ordinal [0,1,2,3], v3_leq_H_68B)

v3_leq_b_recode(v3_clin$v3_leq_h_leq68e_haustier,v3_con$v3_leq_h_leq68e,"v3_leq_H_68B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 656  2   3   13  22  847  1543
## [2,] Percent   42.5 0.1 0.2 0.8 1.4 54.9 100

Create dataset

v3_leq_H<-data.frame(v3_leq_H_56A,v3_leq_H_56B,v3_leq_H_57A,v3_leq_H_57B,v3_leq_H_58A,
                     v3_leq_H_58B,v3_leq_H_59A,v3_leq_H_59B,v3_leq_H_60A,v3_leq_H_60B,
                     v3_leq_H_61A,v3_leq_H_61B,v3_leq_H_62A,v3_leq_H_62B,v3_leq_H_63A,
                     v3_leq_H_63B,v3_leq_H_64A,v3_leq_H_64B,v3_leq_H_65A,v3_leq_H_65B,
                     v3_leq_H_66A,v3_leq_H_66B,v3_leq_H_67A,v3_leq_H_67B,v3_leq_H_68A,
                     v3_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v3_leq_I_69A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq69a_finanz_sit,v3_con$v3_leq_i_j_k_leq69a,"v3_leq_I_69A")
##                -999 bad good <NA>     
## [1,] No. cases 541  61  94   847  1543
## [2,] Percent   35.1 4   6.1  54.9 100

69B Impact (ordinal [0,1,2,3], v3_leq_I_69B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq69e_finanz_sit,v3_con$v3_leq_i_j_k_leq69e,"v3_leq_I_69B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 537  8   39  56  56  847  1543
## [2,] Percent   34.8 0.5 2.5 3.6 3.6 54.9 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v3_leq_I_70A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq70a_finanz_verpfl,v3_con$v3_leq_i_j_k_leq70a,"v3_leq_I_70A")
##                -999 bad good <NA>     
## [1,] No. cases 650  14  32   847  1543
## [2,] Percent   42.1 0.9 2.1  54.9 100

70B Impact (ordinal [0,1,2,3], v3_leq_I_70B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq70e_finanz_verpfl,v3_con$v3_leq_i_j_k_leq70e,"v3_leq_I_70B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 649  3   17  18  9   847  1543
## [2,] Percent   42.1 0.2 1.1 1.2 0.6 54.9 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v3_leq_I_71A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq71a_hypothek,v3_con$v3_leq_i_j_k_leq71a,"v3_leq_I_71A")
##                -999 bad good <NA>     
## [1,] No. cases 686  5   5    847  1543
## [2,] Percent   44.5 0.3 0.3  54.9 100

71B Impact (ordinal [0,1,2,3], v3_leq_I_71B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq71e_hypothek,v3_con$v3_leq_i_j_k_leq71e,"v3_leq_I_71B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 685  2   1   4   4   847  1543
## [2,] Percent   44.4 0.1 0.1 0.3 0.3 54.9 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v3_leq_I_72A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq72a_hypoth_kuend,v3_con$v3_leq_i_j_k_leq72a,"v3_leq_I_72A")
##                -999 bad good <NA>     
## [1,] No. cases 687  1   8    847  1543
## [2,] Percent   44.5 0.1 0.5  54.9 100

72B Impact (ordinal [0,1,2,3], v3_leq_I_72B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq72e_hypoth_kuend,v3_con$v3_leq_i_j_k_leq72e,"v3_leq_I_72B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 686  2   1   4   3   847  1543
## [2,] Percent   44.5 0.1 0.1 0.3 0.2 54.9 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v3_leq_I_73A)

v3_leq_a_recode(v3_clin$v3_leq_i_j_k_leq73a_kreditwuerdk,v3_con$v3_leq_i_j_k_leq73a,"v3_leq_I_73A")
##                -999 bad <NA>     
## [1,] No. cases 675  21  847  1543
## [2,] Percent   43.7 1.4 54.9 100

73B Impact (ordinal [0,1,2,3], v3_leq_I_73B)

v3_leq_b_recode(v3_clin$v3_leq_i_j_k_leq73e_kreditwuerdk,v3_con$v3_leq_i_j_k_leq73e,"v3_leq_I_73B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 673  5   3   5   10  847  1543
## [2,] Percent   43.6 0.3 0.2 0.3 0.6 54.9 100

Create dataset

v3_leq_I<-data.frame(v3_leq_I_69A,v3_leq_I_69B,v3_leq_I_70A,v3_leq_I_70B,v3_leq_I_71A,
                     v3_leq_I_71B,v3_leq_I_72A,v3_leq_I_72B,v3_leq_I_73A,v3_leq_I_73B)

WHOQOL-BREF

For explanation, please refer to the section in Visit 1

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v3_whoqol_itm1)

v3_quol_recode(v3_clin$v3_whoqol_bref_who1_lebensqualitaet,v3_con$v3_whoqol_bref_who1,"v3_whoqol_itm1",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 11  45  185 336  149 817  1543
## [2,] Percent   0.7 2.9 12  21.8 9.7 52.9 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v3_whoqol_itm2)”

v3_quol_recode(v3_clin$v3_whoqol_bref_who2_gesundheit,v3_con$v3_whoqol_bref_who2,"v3_whoqol_itm2",0)
##                1   2   3   4   5   NA's     
## [1,] No. cases 29  127 149 308 114 816  1543
## [2,] Percent   1.9 8.2 9.7 20  7.4 52.9 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v3_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v3_quol_recode(v3_clin$v3_whoqol_bref_who3_schmerzen,v3_con$v3_whoqol_bref_who3,"v3_whoqol_itm3",1)
##                1   2  3   4   5    NA's     
## [1,] No. cases 7   31 66  144 474  821  1543
## [2,] Percent   0.5 2  4.3 9.3 30.7 53.2 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v3_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v3_quol_recode(v3_clin$v3_whoqol_bref_who4_med_behand,v3_con$v3_whoqol_bref_who4,"v3_whoqol_itm4",1)
##                1   2   3   4   5    NA's     
## [1,] No. cases 83  141 91  127 280  821  1543
## [2,] Percent   5.4 9.1 5.9 8.2 18.1 53.2 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v3_whoqol_itm5)

v3_quol_recode(v3_clin$v3_whoqol_bref_who5_lebensgenuss,v3_con$v3_whoqol_bref_who5,"v3_whoqol_itm5",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 19  71  194  320  120 819  1543
## [2,] Percent   1.2 4.6 12.6 20.7 7.8 53.1 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v3_whoqol_itm6)

v3_quol_recode(v3_clin$v3_whoqol_bref_who6_lebenssinn,v3_con$v3_whoqol_bref_who6,"v3_whoqol_itm6",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 32  71  143 269  206  822  1543
## [2,] Percent   2.1 4.6 9.3 17.4 13.4 53.3 100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v3_whoqol_itm7)

v3_quol_recode(v3_clin$v3_whoqol_bref_who7_konzentration,v3_con$v3_whoqol_bref_who7,"v3_whoqol_itm7",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 12  101 259  300  55  816  1543
## [2,] Percent   0.8 6.5 16.8 19.4 3.6 52.9 100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v3_whoqol_itm8)

v3_quol_recode(v3_clin$v3_whoqol_bref_who8_sicherheit,v3_con$v3_whoqol_bref_who8,"v3_whoqol_itm8",0)
##                1   2  3    4    5    NA's     
## [1,] No. cases 11  46 160  348  161  817  1543
## [2,] Percent   0.7 3  10.4 22.6 10.4 52.9 100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v3_whoqol_itm9)

v3_quol_recode(v3_clin$v3_whoqol_bref_who9_umweltbed,v3_con$v3_whoqol_bref_who9,"v3_whoqol_itm9",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 11  24  151 347  192  818  1543
## [2,] Percent   0.7 1.6 9.8 22.5 12.4 53   100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v3_whoqol_itm10)

v3_quol_recode(v3_clin$v3_whoqol_bref_who10_energie,v3_con$v3_whoqol_bref_who10,"v3_whoqol_itm10",0)
##                1   2   3    4    5    NA's     
## [1,] No. cases 12  55  164  305  191  816  1543
## [2,] Percent   0.8 3.6 10.6 19.8 12.4 52.9 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v3_whoqol_itm11)

v3_quol_recode(v3_clin$v3_whoqol_bref_who11_aussehen,v3_con$v3_whoqol_bref_who11,"v3_whoqol_itm11",0)
##                1   2   3   4   5   NA's     
## [1,] No. cases 17  44  140 324 200 818  1543
## [2,] Percent   1.1 2.9 9.1 21  13  53   100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v3_whoqol_itm12)

v3_quol_recode(v3_clin$v3_whoqol_bref_who12_genug_geld,v3_con$v3_whoqol_bref_who12,"v3_whoqol_itm12",0)
##                1  2   3    4    5    NA's     
## [1,] No. cases 31 91  156  249  197  819  1543
## [2,] Percent   2  5.9 10.1 16.1 12.8 53.1 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v3_whoqol_itm13)

v3_quol_recode(v3_clin$v3_whoqol_bref_who13_infozugang,v3_con$v3_whoqol_bref_who13,"v3_whoqol_itm13",0)
##                1   2  3   4    5    NA's     
## [1,] No. cases 3   16 68  264  374  818  1543
## [2,] Percent   0.2 1  4.4 17.1 24.2 53   100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v3_whoqol_itm14)

v3_quol_recode(v3_clin$v3_whoqol_bref_who14_freizeitaktiv,v3_con$v3_whoqol_bref_who14,"v3_whoqol_itm14",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 7   48  148 246  277 817  1543
## [2,] Percent   0.5 3.1 9.6 15.9 18  52.9 100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v3_whoqol_itm15)”

v3_quol_recode(v3_clin$v3_whoqol_bref_who15_fortbewegung,v3_con$v3_whoqol_bref_who15,"v3_whoqol_itm15",0)
##                1   2   3  4    5    NA's     
## [1,] No. cases 6   25  93 269  330  820  1543
## [2,] Percent   0.4 1.6 6  17.4 21.4 53.1 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v3_whoqol_itm16)

v3_quol_recode(v3_clin$v3_whoqol_bref_who16_schlaf,v3_con$v3_whoqol_bref_who16,"v3_whoqol_itm16",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 24  104 121 336  144 814  1543
## [2,] Percent   1.6 6.7 7.8 21.8 9.3 52.8 100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v3_whoqol_itm17)

v3_quol_recode(v3_clin$v3_whoqol_bref_who17_alltag,v3_con$v3_whoqol_bref_who17,"v3_whoqol_itm17",0)
##                1  2   3   4    5    NA's     
## [1,] No. cases 15 74  119 348  174  813  1543
## [2,] Percent   1  4.8 7.7 22.6 11.3 52.7 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v3_whoqol_itm18)

v3_quol_recode(v3_clin$v3_whoqol_bref_who18_arbeitsfhgk,v3_con$v3_whoqol_bref_who18,"v3_whoqol_itm18",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 43  120 148 252  155 825  1543
## [2,] Percent   2.8 7.8 9.6 16.3 10  53.5 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v3_whoqol_itm19)

v3_quol_recode(v3_clin$v3_whoqol_bref_who19_selbstzufried,v3_con$v3_whoqol_bref_who19,"v3_whoqol_itm19",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 23  81  166  345  111 817  1543
## [2,] Percent   1.5 5.2 10.8 22.4 7.2 52.9 100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v3_whoqol_itm20)

v3_quol_recode(v3_clin$v3_whoqol_bref_who20_pers_bezieh,v3_con$v3_whoqol_bref_who20,"v3_whoqol_itm20",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 14  67  144 338  159  821  1543
## [2,] Percent   0.9 4.3 9.3 21.9 10.3 53.2 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v3_whoqol_itm21)

v3_quol_recode(v3_clin$v3_whoqol_bref_who21_sexualleben,v3_con$v3_whoqol_bref_who21,"v3_whoqol_itm21",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 67  110 210  207  114 835  1543
## [2,] Percent   4.3 7.1 13.6 13.4 7.4 54.1 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v3_whoqol_itm22)

v3_quol_recode(v3_clin$v3_whoqol_bref_who22_freunde,v3_con$v3_whoqol_bref_who22,"v3_whoqol_itm22",0)
##                1   2  3   4    5    NA's     
## [1,] No. cases 18  46 143 356  165  815  1543
## [2,] Percent   1.2 3  9.3 23.1 10.7 52.8 100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v3_whoqol_itm23)

v3_quol_recode(v3_clin$v3_whoqol_bref_who23_wohnbeding,v3_con$v3_whoqol_bref_who23,"v3_whoqol_itm23",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 18  56  104 318  233  814  1543
## [2,] Percent   1.2 3.6 6.7 20.6 15.1 52.8 100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v3_whoqol_itm24)

v3_quol_recode(v3_clin$v3_whoqol_bref_who24_gesundhdiens,v3_con$v3_whoqol_bref_who24,"v3_whoqol_itm24",0)
##                1   2   3   4   5    NA's     
## [1,] No. cases 6   13  71  340 299  814  1543
## [2,] Percent   0.4 0.8 4.6 22  19.4 52.8 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v3_whoqol_itm25)

v3_quol_recode(v3_clin$v3_whoqol_bref_who25_transport,v3_con$v3_whoqol_bref_who25,"v3_whoqol_itm25",0)
##                1   2   3   4   5    NA's     
## [1,] No. cases 8   30  86  293 311  815  1543
## [2,] Percent   0.5 1.9 5.6 19  20.2 52.8 100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v3_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v3_quol_recode(v3_clin$v3_whoqol_bref_who26_neg_gefuehle,v3_con$v3_whoqol_bref_who26,"v3_whoqol_itm26",1)
##                1  2   3    4    5    NA's     
## [1,] No. cases 15 82  186  284  161  815  1543
## [2,] Percent   1  5.3 12.1 18.4 10.4 52.8 100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v3_whoqol_dom_glob)

v3_whoqol_dom_glob_df<-data.frame(as.numeric(v3_whoqol_itm1),as.numeric(v3_whoqol_itm2))

v3_who_glob_no_nas<-rowSums(is.na(v3_whoqol_dom_glob_df))

v3_whoqol_dom_glob<-ifelse((v3_who_glob_no_nas==0) | (v3_who_glob_no_nas==1), 
                            rowMeans(v3_whoqol_dom_glob_df,na.rm=T)*4,NA)

v3_whoqol_dom_glob<-round(v3_whoqol_dom_glob,2)

summary(v3_whoqol_dom_glob)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   16.00   14.53   16.00   20.00     814

Physical Health (continuous [4-20],v3_whoqol_dom_phys)

v3_whoqol_dom_phys_df<-data.frame(as.numeric(v3_whoqol_itm3),as.numeric(v3_whoqol_itm10),as.numeric(v3_whoqol_itm16),as.numeric(v3_whoqol_itm15),as.numeric(v3_whoqol_itm17),as.numeric(v3_whoqol_itm4),as.numeric(v3_whoqol_itm18))

v3_who_phys_no_nas<-rowSums(is.na(v3_whoqol_dom_phys_df))

v3_whoqol_dom_phys<-ifelse((v3_who_phys_no_nas==0) | (v3_who_phys_no_nas==1), 
                            rowMeans(v3_whoqol_dom_phys_df,na.rm=T)*4,NA)

v3_whoqol_dom_phys<-round(v3_whoqol_dom_phys,2)

summary(v3_whoqol_dom_phys)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   13.71   15.43   15.43   17.71   20.00     822

Psychological (continuous [4-20],v3_whoqol_dom_psy)

v3_whoqol_dom_psy_df<-data.frame(as.numeric(v3_whoqol_itm5),as.numeric(v3_whoqol_itm7),as.numeric(v3_whoqol_itm19),as.numeric(v3_whoqol_itm11),as.numeric(v3_whoqol_itm26),as.numeric(v3_whoqol_itm6))

v3_who_psy_no_nas<-rowSums(is.na(v3_whoqol_dom_psy_df))

v3_whoqol_dom_psy<-ifelse((v3_who_psy_no_nas==0) | (v3_who_psy_no_nas==1), 
                            rowMeans(v3_whoqol_dom_psy_df,na.rm=T)*4,NA)

v3_whoqol_dom_psy<-round(v3_whoqol_dom_psy,2)

summary(v3_whoqol_dom_psy)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.67   12.67   15.33   14.64   16.67   20.00     818

Social relationships (continuous [4-20],v3_whoqol_dom_soc)

v3_whoqol_dom_soc_df<-data.frame(as.numeric(v3_whoqol_itm20),as.numeric(v3_whoqol_itm22),as.numeric(v3_whoqol_itm21))

v3_who_soc_no_nas<-rowSums(is.na(v3_whoqol_dom_soc_df))

v3_whoqol_dom_soc<-ifelse((v3_who_soc_no_nas==0) | (v3_who_soc_no_nas==1), 
                            rowMeans(v3_whoqol_dom_soc_df,na.rm=T)*4,NA)

v3_whoqol_dom_soc<-round(v3_whoqol_dom_soc,2)

summary(v3_whoqol_dom_soc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.51   16.00   20.00     817

Environment (continuous [4-20],v3_whoqol_dom_env)

v3_whoqol_dom_env_df<-data.frame(as.numeric(v3_whoqol_itm8),as.numeric(v3_whoqol_itm23),as.numeric(v3_whoqol_itm12),as.numeric(v3_whoqol_itm24),as.numeric(v3_whoqol_itm13),as.numeric(v3_whoqol_itm14),as.numeric(v3_whoqol_itm9),as.numeric(v3_whoqol_itm25))

v3_who_env_no_nas<-rowSums(is.na(v3_whoqol_dom_env_df))

v3_whoqol_dom_env<-ifelse((v3_who_env_no_nas==0) | (v3_who_env_no_nas==1), 
                            rowMeans(v3_whoqol_dom_env_df,na.rm=T)*4,NA)

v3_whoqol_dom_env<-round(v3_whoqol_dom_env,2)

summary(v3_whoqol_dom_env)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    6.00   14.50   16.00   16.12   18.00   20.00     819

Create dataset

v3_whoqol<-data.frame(v3_whoqol_itm1,v3_whoqol_itm2,v3_whoqol_itm3,v3_whoqol_itm4,
                      v3_whoqol_itm5,v3_whoqol_itm6,v3_whoqol_itm7,v3_whoqol_itm8,
                      v3_whoqol_itm9,v3_whoqol_itm10,v3_whoqol_itm11,v3_whoqol_itm12,
                      v3_whoqol_itm13,v3_whoqol_itm14,v3_whoqol_itm15,v3_whoqol_itm16,
                      v3_whoqol_itm17,v3_whoqol_itm18,v3_whoqol_itm19,v3_whoqol_itm20,
                      v3_whoqol_itm21,v3_whoqol_itm22,v3_whoqol_itm23,v3_whoqol_itm24,
                      v3_whoqol_itm25,v3_whoqol_itm26,v3_whoqol_dom_glob,
                      v3_whoqol_dom_phys,v3_whoqol_dom_psy,v3_whoqol_dom_soc,
                      v3_whoqol_dom_env)

Visit 3: Create dataframe

v3_df<-data.frame(v3_id,
                  v3_rec,
                  v3_clin_ill_ep,
                  v3_con_problems,
                  v3_dem,
                  v3_leprcp,
                  v3_suic,
                  v3_med,
                  v3_subst,
                  v3_symp_panss,
                  v3_symp_ids_c,
                  v3_symp_ymrs,
                  v3_ill_sev,
                  v3_nrpsy,
                  v3_sf12,
                  v3_cts,
                  v3_med_adh,
                  v3_bdi2,
                  v3_asrm,
                  v3_mss,
                  v3_leq,
                  v3_whoqol)

Visit 4: Data preparation

Read in data of clinical participants

## [1] 1344

Read in data of control participants

## [1] 329

Modify column names

Only include subjects for which data for the first visit is present

v4_clin<-subset(v4_clin, as.character(v4_clin$mnppsd)%in%as.character(v1_clin$mnppsd))
dim(v4_clin)[1] 
## [1] 1223
v4_con<-subset(v4_con, as.character(v4_con$mnppsd)%in%as.character(v1_con$mnppsd))
dim(v4_con)[1] 
## [1] 320

Participant identity column (categorical [id], v4_id)

v4_id<-as.factor(c(as.character(v4_clin$mnppsd),as.character(v4_con$mnppsd)))

Visit 4: Recruitment data

Participant identity column (categorical [id], v4_id)

v4_id<-as.factor(c(as.character(v4_clin$mnppsd),as.character(v4_con$mnppsd)))

Date of interview (categorical [year-month-day], v4_interv_date)

v4_interv_date<-c(as.Date(as.character(v4_clin$v4_ausschluss1_rekr_datum), "%Y%m%d"),as.Date(as.character(v4_con$v4_rekru_visit_rekr_datum), "%Y%m%d"))

Age at third interview (continuous [years], v4_age)

v4_age_years_clin<-as.numeric(substr(v4_clin$v4_ausschluss1_rekr_datum,1,4))-
  as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,1,4))

v4_age_years_con<-as.numeric(substr(v4_con$v4_rekru_visit_rekr_datu,1,4))-
  as.numeric(substr(v1_con$v1_demo1_gebdat,1,4))

v4_age_years<-c(v4_age_years_clin,v4_age_years_con)

v4_age<-ifelse(c(as.numeric(substr(v4_clin$v4_ausschluss1_rekr_datum,5,6)),as.numeric(substr(v4_con$v4_rekru_visit_rekr_datu,5,6)))<
                 c(as.numeric(substr(v1_clin$v1_demogr_s1_dem2_ses02_geb,5,6)),as.numeric(substr(v1_con$v1_demo1_gebdat,5,6))),
                   v4_age_years-1,v4_age_years)
summary(v4_age) 
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   19.00   36.00   48.00   46.51   56.00   79.00     997

Create dataset

v4_rec<-data.frame(v4_age,v4_interv_date)

Visit 4: Illness episodes between study visits

Study participant are asked whether an acute illness episode occurred since the last study visit. Possible answers are “Y”-yes, “N”-no and “C”-chronic symptomatology. The latter category is for people which continually experience symptoms. If the answer was yes, additional questions were asked about the episodes, if not these are omitted. For participants with chronic symptomatology, the participant is asked about the nature of the chronic symptomatology (manic/depressive/mixed/psychotic) and answers are coded in the questions “Did you experience … symptoms during this illness episode?” (first illness episode).
Importantly, if the participant experienced multiple illness episodes since the last study visit, a set of questions (see below) is supposed to be answered for each illness episode. As most interviewers answered these questions only for a maximum of two illness episodes and few participants experienced more than two illness episodes, data are included only for the first two illness episodes.

Illness episodes since last study visit (categorical [Y, N, C], v4_ill_ep_snc_lst)

Illness episodes since last study visit (only in clinical participants)

“Did you experience an acute illness episode since the last study visit?” (categorical [Y, N, C], v4_clin_ill_ep_snc_lst)

v4_clin_ill_ep_snc_lst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_ill_ep_snc_lst<-ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==1,"Y",
                          ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==2,"N",
                            ifelse(c(v4_clin$v4_aktu_situat_aenderung_akt_sit,rep(-999,dim(v4_con)[1]))==3,"C",v4_clin_ill_ep_snc_lst)))

v4_clin_ill_ep_snc_lst<-factor(v4_clin_ill_ep_snc_lst)                         
descT(v4_clin_ill_ep_snc_lst)
##                -999 C   N    Y   <NA>     
## [1,] No. cases 320  58  279  139 747  1543
## [2,] Percent   20.7 3.8 18.1 9   48.4 100

“If yes, how many illness episodes? (continuous [no. illness episodes], v4_clin_no_ep)”

v4_clin_no_ep<-ifelse(v4_clin_ill_ep_snc_lst=="Y",c(v4_clin$v4_aktu_situat_anzahl_episoden,rep(-999,dim(v4_con)[1])),-999)
descT(v4_clin_no_ep)
##                -999 1   2   3   5   <NA>     
## [1,] No. cases 657  101 24  8   2   751  1543
## [2,] Percent   42.6 6.5 1.6 0.5 0.1 48.7 100

In the following, characteristics of each illness episode are assessed. Checkboxes are supposed to be ticked if a criterion applies. Please note that episodes can have more than one characteristic (e.g. episodes with both manic and psychotic symptoms).

First illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_man)

v4_clin_fst_ill_ep_man<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_man<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_manisch_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_fst_ill_ep_man)
##                -999 Y   <NA>     
## [1,] No. cases 779  23  741  1543
## [2,] Percent   50.5 1.5 48   100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_dep)

v4_clin_fst_ill_ep_dep<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_dep<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_depressiv_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_fst_ill_ep_dep)
##                -999 Y   <NA>     
## [1,] No. cases 736  66  741  1543
## [2,] Percent   47.7 4.3 48   100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v4_clin_fst_ill_ep_mx<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_mx<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_gemischt_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                         -999)

descT(v4_clin_fst_ill_ep_mx)
##                -999 Y   <NA>     
## [1,] No. cases 794  8   741  1543
## [2,] Percent   51.5 0.5 48   100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v4_clin_fst_ill_ep_psy)

v4_clin_fst_ill_ep_psy<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_psy<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_psych_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_fst_ill_ep_psy)
##                -999 Y   <NA>     
## [1,] No. cases 752  50  741  1543
## [2,] Percent   48.7 3.2 48   100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v4_clin_fst_ill_ep_dur)

v4_clin_fst_ill_ep_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_dur<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",
                               ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==2,"two to four weeks", 
                                      ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_1,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
                                             ifelse(v4_clin_ill_ep_snc_lst=="N",-999,v4_clin_fst_ill_ep_dur))))

v4_clin_fst_ill_ep_dur<-ordered(v4_clin_fst_ill_ep_dur, 
                               levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_fst_ill_ep_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 599  33                  31               
## [2,] Percent   38.8 2.1                 2                
##      more than four weeks <NA>     
## [1,] 71                   809  1543
## [2,] 4.6                  52.4 100

“During this episode, were you hospitalized?” (dichotomous, v4_clin_fst_ill_ep_hsp)

v4_clin_fst_ill_ep_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="N",-999,
                          ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                            c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v4_con)[1]))==2,"N",
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                                c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y", v4_clin_fst_ill_ep_hsp)))

v4_clin_fst_ill_ep_hsp<-factor(v4_clin_fst_ill_ep_hsp)                         
descT(v4_clin_fst_ill_ep_hsp)
##                -999 N   Y   <NA>     
## [1,] No. cases 599  83  52  809  1543
## [2,] Percent   38.8 5.4 3.4 52.4 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v4_clin_fst_ill_ep_hsp_dur)

v4_clin_fst_ill_ep_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_hsp_dur<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",  
                              ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
                                ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_1,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",    
                                            -999)))

v4_clin_fst_ill_ep_hsp_dur<-ordered(v4_clin_fst_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v4_clin_fst_ill_ep_hsp_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 740  7                   16               
## [2,] Percent   48   0.5                 1                
##      more than four weeks <NA>     
## [1,] 25                   755  1543
## [2,] 1.6                  48.9 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes):

Reason for hospitalization: symptom worsensing (checkbox [Y], v4_clin_fst_ill_ep_symp_wrs)

v4_clin_fst_ill_ep_symp_wrs<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_symp_wrs<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund1_31642_1,rep(-999,dim(v4_con)[1]))==1,"Y",-999)
  
descT(v4_clin_fst_ill_ep_symp_wrs)
##                -999 Y  <NA>     
## [1,] No. cases 754  47 742  1543
## [2,] Percent   48.9 3  48.1 100

Reason for hospitalization: self-endangerment (checkbox [Y], v4_clin_fst_ill_ep_slf_end)

v4_clin_fst_ill_ep_slf_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_slf_end<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund2_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y", 
                              -999)

descT(v4_clin_fst_ill_ep_slf_end)
##                -999 Y   <NA>     
## [1,] No. cases 792  10  741  1543
## [2,] Percent   51.3 0.6 48   100

Reason for hospitalization: suicidality (checkbox [Y], v4_clin_fst_ill_ep_suic)

v4_clin_fst_ill_ep_suic<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_suic<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund3_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y", 
                           -999)

descT(v4_clin_fst_ill_ep_suic)
##                -999 Y   <NA>     
## [1,] No. cases 797  5   741  1543
## [2,] Percent   51.7 0.3 48   100

Reason for hospitalization: endangerment of others (checkbox [Y], v4_clin_fst_ill_ep_oth_end)

v4_clin_fst_ill_ep_oth_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_oth_end<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund4_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_fst_ill_ep_oth_end)
##                -999 Y   <NA>     
## [1,] No. cases 800  2   741  1543
## [2,] Percent   51.8 0.1 48   100

Reason for hospitalization: medication change (checkbox [Y], v4_clin_fst_ill_ep_med_chg)

v4_clin_fst_ill_ep_med_chg<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_fst_ill_ep_med_chg<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund5_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_fst_ill_ep_med_chg)
##                -999 Y   <NA>     
## [1,] No. cases 797  5   741  1543
## [2,] Percent   51.7 0.3 48   100

Reason for hospitalization: other (checkbox [Y], v4_clin_fst_ill_ep_othr)

v4_clin_fst_ill_ep_othr<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_fst_ill_ep_othr<-ifelse(v4_clin_fst_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_grund6_31642_1,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
 
descT(v4_clin_fst_ill_ep_othr)
##                -999 Y   <NA>     
## [1,] No. cases 792  10  741  1543
## [2,] Percent   51.3 0.6 48   100

Second illness episode

“Did you experience manic symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_man)

v4_clin_sec_ill_ep_man<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_man<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_manisch_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",-999)

descT(v4_clin_sec_ill_ep_man)
##                -999 Y   <NA>     
## [1,] No. cases 686  3   854  1543
## [2,] Percent   44.5 0.2 55.3 100

“Did you experience depressive symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_dep) #frstill

v4_clin_sec_ill_ep_dep<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_dep<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_depressiv_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",
                          -999)

descT(v4_clin_sec_ill_ep_dep)
##                -999 Y   <NA>     
## [1,] No. cases 666  23  854  1543
## [2,] Percent   43.2 1.5 55.3 100

“Did you experience mixed symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_mx) This checkbox assesses whether this was a mixed mood episode (both depressive and manic symptoms present).

v4_clin_sec_ill_ep_mx<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_mx<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_gemischt_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y", 
                         -999)

descT(v4_clin_sec_ill_ep_mx)
##                -999 Y   <NA>     
## [1,] No. cases 686  3   854  1543
## [2,] Percent   44.5 0.2 55.3 100

“Did you experience psychotic symptoms during this illness episode?” (checkbox [Y], v4_clin_sec_ill_ep_psy)

v4_clin_sec_ill_ep_psy<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_psy<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_psych_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y", 
                          -999)

descT(v4_clin_sec_ill_ep_psy)
##                -999 Y   <NA>     
## [1,] No. cases 685  4   854  1543
## [2,] Percent   44.4 0.3 55.3 100

“How long did this episode last?” (ordinal [less than two weeks, two to four weeks, more than four weeks], v4_clin_sec_ill_ep_dur)

v4_clin_sec_ill_ep_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_dur<-ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==1,"less than two weeks", 
                           ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",    
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_dauer_31642_2,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",  
                                ifelse(v4_clin_ill_ep_snc_lst=="N",-999,v4_clin_sec_ill_ep_dur))))
 
v4_clin_sec_ill_ep_dur<-ordered(v4_clin_sec_ill_ep_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_sec_ill_ep_dur)
##                less than two weeks two to four weeks more than four weeks
## [1,] No. cases 9                   6                 16                  
## [2,] Percent   0.6                 0.4               1                   
##      <NA>     
## [1,] 1512 1543
## [2,] 98   100

“During this episode, were you hospitalized?” (dichotomous, v4_clin_sec_ill_ep_hsp)

v4_clin_sec_ill_ep_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="N",-999,
                          ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                            c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v4_con)[1]))==2,"N",
                              ifelse(v4_clin_ill_ep_snc_lst=="Y" &
                                c(v4_clin$v4_aktu_situat_k_episode_aufenthalt_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y", v4_clin_sec_ill_ep_hsp)))

v4_clin_sec_ill_ep_hsp<-factor(v4_clin_sec_ill_ep_hsp)                         
descT(v4_clin_sec_ill_ep_hsp)
##                -999 N   Y   <NA>     
## [1,] No. cases 599  22  10  912  1543
## [2,] Percent   38.8 1.4 0.6 59.1 100

“If yes, for how long?” (ordinal, [less than two weeks, two to four weeks, more than four weeks] v4_clin_sec_ill_ep_hsp_dur)

v4_clin_sec_ill_ep_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_hsp_dur<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==1,"less than two weeks",  
                              ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
                                ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_woche_31642_2,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",    
                                            -999)))

v4_clin_sec_ill_ep_hsp_dur<-ordered(v4_clin_sec_ill_ep_hsp_dur, levels=c("-999","less than two weeks","two to four weeks","more than four weeks"))

descT(v4_clin_sec_ill_ep_hsp_dur)
##                -999 less than two weeks two to four weeks
## [1,] No. cases 679  1                   6                
## [2,] Percent   44   0.1                 0.4              
##      more than four weeks <NA>     
## [1,] 3                    854  1543
## [2,] 0.2                  55.3 100

The following questions ask for the reasons for hospitalization, multiple answers are possible (checkboxes): Reason for hospitalization: symptom worsensing (checkbox [Y], v4_clin_sec_ill_ep_symp_wrs)

v4_clin_sec_ill_ep_symp_wrs<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_symp_wrs<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund1_31642_2,rep(-999,dim(v4_con)[1]))==1,"Y",-999)
  
descT(v4_clin_sec_ill_ep_symp_wrs)
##                -999 Y   <NA>     
## [1,] No. cases 681  8   854  1543
## [2,] Percent   44.1 0.5 55.3 100

Reason for hospitalization: self-endangerment (checkbox [Y], v4_clin_sec_ill_ep_slf_end)

v4_clin_sec_ill_ep_slf_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_slf_end<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund2_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y", 
                              -999)

descT(v4_clin_sec_ill_ep_slf_end)
##                -999 Y   <NA>     
## [1,] No. cases 688  1   854  1543
## [2,] Percent   44.6 0.1 55.3 100

Reason for hospitalization: suicidality (checkbox [Y], v4_clin_sec_ill_ep_suic)

v4_clin_sec_ill_ep_suic<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_suic<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund3_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y", 
                           -999)

descT(v4_clin_sec_ill_ep_suic)
##                -999 Y   <NA>     
## [1,] No. cases 688  1   854  1543
## [2,] Percent   44.6 0.1 55.3 100

Reason for hospitalization: endangerment of others (checkbox [Y], v4_clin_sec_ill_ep_oth_end)

v4_clin_sec_ill_ep_oth_end<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_oth_end<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund4_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_sec_ill_ep_oth_end)
##                -999 <NA>     
## [1,] No. cases 689  854  1543
## [2,] Percent   44.7 55.3 100

Reason for hospitalization: medication change (checkbox [Y], v4_clin_sec_ill_ep_med_chg)

v4_clin_sec_ill_ep_med_chg<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_sec_ill_ep_med_chg<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_k_episode_grund5_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)

descT(v4_clin_sec_ill_ep_med_chg)
##                -999 <NA>     
## [1,] No. cases 689  854  1543
## [2,] Percent   44.7 55.3 100

Reason for hospitalization: other (checkbox [Y], v4_clin_sec_ill_ep_othr)

v4_clin_sec_ill_ep_othr<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_sec_ill_ep_othr<-ifelse(v4_clin_sec_ill_ep_hsp=="Y" & v4_clin_ill_ep_snc_lst=="Y" & c(v4_clin$v4_aktu_situat_k_episode_grund6_31642_2,rep(-999,dim(v4_con)[1]))==1, "Y",-999)
 
descT(v4_clin_sec_ill_ep_othr)
##                -999 Y   <NA>     
## [1,] No. cases 686  3   854  1543
## [2,] Percent   44.5 0.2 55.3 100

Additional psychiatric hospitalization as in- or daypatient? (dichotomous, v4_clin_add_oth_hsp)

v4_clin_add_oth_hsp<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_add_oth_hsp<-ifelse(v4_clin_ill_ep_snc_lst=="Y" &
  c(v4_clin$v4_aktu_situat_aenderung_aufent,rep(-999,dim(v4_con)[1]))==1,"Y","N")

descT(v4_clin_add_oth_hsp)
##                N    Y   <NA>     
## [1,] No. cases 781  12  750  1543
## [2,] Percent   50.6 0.8 48.6 100

If yes, how many other hospitalizations? (continous [no. of hospitalizations], v4_clin_oth_hsp_nmb)

v4_clin_oth_hsp_nmb<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_oth_hsp_nmb<-ifelse(v4_clin_add_oth_hsp=="Y",
          c(v4_clin$v4_aktu_situat_aenderung_anzahl,rep(-999,dim(v4_con)[1])),-999)

descT(v4_clin_oth_hsp_nmb)
##                -999 1   2   3   <NA>     
## [1,] No. cases 781  7   1   1   753  1543
## [2,] Percent   50.6 0.5 0.1 0.1 48.8 100

If yes, duration of other hospitalizations? (ordinal, v4_clin_oth_hsp_dur)

v4_clin_oth_hsp_dur<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_clin_oth_hsp_dur<-
  ifelse(v4_clin_add_oth_hsp=="Y" & 
           c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==1,"less than two weeks", 
   ifelse(v4_clin_add_oth_hsp=="Y" & 
            c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==2,"two to four weeks",
    ifelse(v4_clin_add_oth_hsp=="Y" & 
             c(v4_clin$v4_aktu_situat_aenderung_dauer,rep(-999,dim(v4_con)[1]))==3,"more than four weeks",
     ifelse(v4_clin_add_oth_hsp=="N",-999,v4_clin_add_oth_hsp))))

v4_clin_oth_hsp_dur<-ordered(v4_clin_oth_hsp_dur, levels=c("less than two weeks","two to four weeks","more than four weeks"))
descT(v4_clin_oth_hsp_dur)
##                less than two weeks two to four weeks more than four weeks
## [1,] No. cases 2                   3                 5                   
## [2,] Percent   0.1                 0.2               0.3                 
##      <NA>     
## [1,] 1533 1543
## [2,] 99.4 100

If yes, reason for other hospitalization(s) medication change? (checkbox [Y], v4_clin_othr_psy_med)

v4_clin_othr_psy_med<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_clin_othr_psy_med<-ifelse(v4_clin_add_oth_hsp=="Y" & v4_clin_add_oth_hsp=="Y" & 
      c(v4_clin$v4_aktu_situat_aenderung_medikament,rep(-999,dim(v4_con)[1]))==1,"Y",
                        ifelse(v4_clin_add_oth_hsp=="N",-999,v4_clin_othr_psy_med))
  
descT(v4_clin_othr_psy_med)
##                -999 Y   <NA>     
## [1,] No. cases 781  2   760  1543
## [2,] Percent   50.6 0.1 49.3 100

Current psychiatric treatment of both clinical and control participants (ordinal [1,2,3,4], v4_cur_psy_trm)

This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.

v4_clin_cur_psy_trm<-rep(NA,dim(v4_clin)[1])
v4_con_cur_psy_trm<-rep(NA,dim(v4_con)[1])

v4_clin_cur_psy_trm<-ifelse(v4_clin$v4_aktu_situat_psybehandlung==0,"1",
                        ifelse(v4_clin$v4_aktu_situat_psybehandlung==3,"2", 
                          ifelse(v4_clin$v4_aktu_situat_psybehandlung==2,"3",
                            ifelse(v4_clin$v4_aktu_situat_psybehandlung==1,"4",v4_clin_cur_psy_trm)))) 

v4_con_cur_psy_trm<-ifelse(v4_con$v4_bildung_beruf_psybehandlung==0,"1",
                      ifelse(v4_con$v4_bildung_beruf_psybehandlung==3,"2",
                        ifelse(v4_con$v4_bildung_beruf_psybehandlung==2,"3",
                          ifelse(v4_con$v4_bildung_beruf_psybehandlung==1,"4",v4_con_cur_psy_trm))))

v4_cur_psy_trm<-factor(c(v4_clin_cur_psy_trm,v4_con_cur_psy_trm),ordered=T)
descT(v4_cur_psy_trm)
##                1   2    3   4   <NA>     
## [1,] No. cases 91  415  6   22  1009 1543
## [2,] Percent   5.9 26.9 0.4 1.4 65.4 100

Create dataset

v4_clin_ill_ep<-data.frame(v4_clin_ill_ep_snc_lst,
                           v4_clin_no_ep,
                           v4_clin_fst_ill_ep_man,
                           v4_clin_fst_ill_ep_dep,
                           v4_clin_fst_ill_ep_mx,
                           v4_clin_fst_ill_ep_psy,
                           v4_clin_fst_ill_ep_dur,
                           v4_clin_fst_ill_ep_hsp,
                           v4_clin_fst_ill_ep_hsp_dur,
                           v4_clin_fst_ill_ep_symp_wrs,
                           v4_clin_fst_ill_ep_slf_end,
                           v4_clin_fst_ill_ep_suic,
                           v4_clin_fst_ill_ep_oth_end,
                           v4_clin_fst_ill_ep_med_chg,
                           v4_clin_fst_ill_ep_othr,
                           v4_clin_sec_ill_ep_man,
                           v4_clin_sec_ill_ep_dep,
                           v4_clin_sec_ill_ep_mx,
                           v4_clin_sec_ill_ep_psy,
                           v4_clin_sec_ill_ep_dur,
                           v4_clin_sec_ill_ep_hsp,
                           v4_clin_sec_ill_ep_hsp_dur,
                           v4_clin_sec_ill_ep_symp_wrs,
                           v4_clin_sec_ill_ep_slf_end,
                           v4_clin_sec_ill_ep_suic,
                           v4_clin_sec_ill_ep_oth_end,
                           v4_clin_sec_ill_ep_med_chg,
                           v4_clin_sec_ill_ep_othr,
                           v4_clin_add_oth_hsp,
                           v4_clin_oth_hsp_nmb,
                           v4_clin_oth_hsp_dur,
                           v4_clin_othr_psy_med,
                           v4_cur_psy_trm)

Visit 4: Demographic information

Did your marital status change since the last study visit? (dichotomous, v4_cng_mar_stat)

v4_clin_cng_mar_stat<-rep(NA,dim(v4_clin)[1]) 
v4_clin_cng_mar_stat<-ifelse(v4_clin$v4_aktu_situat_fam_stand==1, "Y", 
                        ifelse(v4_clin$v4_aktu_situat_fam_stand==2, "N", v4_clin_cng_mar_stat))

v4_con_cng_mar_stat<-rep(NA,dim(v4_con)[1]) 
v4_con_cng_mar_stat<-ifelse(v4_con$v4_famil_wohn_fam_stand==1, "Y", 
                        ifelse(v4_con$v4_famil_wohn_fam_stand==2, "N", v4_con_cng_mar_stat))

v4_cng_mar_stat<-factor(c(v4_clin_cng_mar_stat,v4_con_cng_mar_stat))

Marital status (categorical [married, married but living separately, single, divorced, widowed], v4_marital_stat)

v4_clin_marital_stat<-rep(NA,dim(v4_clin)[1])
v4_clin_marital_stat<-ifelse(v4_clin$v4_aktu_situat_fam_familienstand==1,"Married", 
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==2,"Married_living_sep",
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==3,"Single",
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==4,"Divorced",
                 ifelse(v4_clin$v4_aktu_situat_fam_familienstand==5,"Widowed",v4_clin_marital_stat)))))

v4_con_marital_stat<-rep(NA,dim(v4_con)[1])
v4_con_marital_stat<-ifelse(v4_con$v4_famil_wohn_fam_famstand==1,"Married", 
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==2,"Married_living_sep",
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==3,"Single",
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==4,"Divorced",
                 ifelse(v4_con$v4_famil_wohn_fam_famstand==5,"Widowed",v4_con_marital_stat)))))

v4_marital_stat<-factor(c(v4_clin_marital_stat,v4_con_marital_stat))
desc(v4_marital_stat)
##                Divorced Married Married_living_sep Single Widowed NA's
## [1,] No. cases 84       136     16                 277    10      1020
## [2,] Percent   5.4      8.8     1                  18     0.6     66.1
##          
## [1,] 1543
## [2,] 100

Relationship status

“Do you currently have a partner?” (dichotomous, v4_partner)

v4_clin_partner<-rep(NA,dim(v4_clin)[1])
v4_clin_partner<-ifelse(v4_clin$v4_aktu_situat_fam_fest_partner==1,"Y",
            ifelse(v4_clin$v4_aktu_situat_fam_fest_partner==2,"N",v4_clin_partner))

v4_con_partner<-rep(NA,dim(v4_con)[1])
v4_con_partner<-ifelse(v4_con$v4_famil_wohn_fam_partner==1,"Y",
            ifelse(v4_con$v4_famil_wohn_fam_partner==2,"N",v4_con_partner))


v4_partner<-factor(c(v4_clin_partner,v4_con_partner))
descT(v4_partner)
##                N    Y    <NA>     
## [1,] No. cases 259  253  1031 1543
## [2,] Percent   16.8 16.4 66.8 100

Children

Biological (continuous [number], v4_no_bio_chld)

v4_no_bio_chld<-c(v4_clin$v4_aktu_situat_fam_kind_gesamt,v4_con$v4_famil_wohn_fam_lkind)
descT(v4_no_bio_chld)
##                0    1   2   3   4   5   <NA>     
## [1,] No. cases 297  109 70  41  5   4   1017 1543
## [2,] Percent   19.2 7.1 4.5 2.7 0.3 0.3 65.9 100

Non-biological

Adoptive children (continuous [number], v4_no_adpt_chld)

v4_no_adpt_chld<-c(v4_clin$v4_aktu_situat_fam_adopt_gesamt,v4_con$v4_famil_wohn_fam_adkind)
descT(v4_no_adpt_chld)  
##                0    1   2   <NA>     
## [1,] No. cases 516  2   2   1023 1543
## [2,] Percent   33.4 0.1 0.1 66.3 100

Step children (continuous [number], v4_stp_chld)

v4_stp_chld<-c(v4_clin$v4_aktu_situat_fam_stift_gesamt,v4_con$v4_famil_wohn_fam_skind)
descT(v4_stp_chld)      
##                0    1   2   3   4   5   <NA>     
## [1,] No. cases 480  24  12  4   1   1   1021 1543
## [2,] Percent   31.1 1.6 0.8 0.3 0.1 0.1 66.2 100

Change in housing situation since last study visit? (dichotomous, v4_chg_hsng)

v4_clin_chg_hsng<-rep(NA,dim(v4_clin)[1])
v4_clin_chg_hsng<-ifelse(v4_clin$v4_wohnsituation_wohn_aenderung==1,"Y",
                  ifelse(v4_clin$v4_wohnsituation_wohn_aenderung==2,"N",v4_clin_chg_hsng))  

v4_con_chg_hsng<-rep(NA,dim(v4_con)[1])
v4_con_chg_hsng<-ifelse(v4_con$v4_famil_wohn_wohn_stand==1,"Y",
                 ifelse(v4_con$v4_famil_wohn_wohn_stand==2,"N",v4_con_chg_hsng))

v4_chg_hsng<-factor(c(v4_clin_chg_hsng,v4_con_chg_hsng))
descT(v4_chg_hsng)
##                N    Y   <NA>     
## [1,] No. cases 476  64  1003 1543
## [2,] Percent   30.8 4.1 65   100

Living alone (dichotomous, v4_liv_aln)

v4_clin_liv_aln<-rep(NA,dim(v4_clin)[1])
v4_clin_liv_aln<-ifelse(v4_clin$v4_wohnsituation_wohn_allein==1,"Y",    
                 ifelse(v4_clin$v4_wohnsituation_wohn_allein==0,"N",v4_clin_liv_aln))   

v4_con_liv_aln<-rep(NA,dim(v4_con)[1])
v4_con_liv_aln<-ifelse(v4_con$v4_famil_wohn_wohn_allein==1,"Y", 
                 ifelse(v4_con$v4_famil_wohn_wohn_allein==0,"N",v4_con_liv_aln))
                 
v4_liv_aln<-factor(c(v4_clin_liv_aln,v4_con_liv_aln))
descT(v4_liv_aln)
##                N    Y    <NA>     
## [1,] No. cases 332  214  997  1543
## [2,] Percent   21.5 13.9 64.6 100

Employment

Did your employment situation change since the last study visit?

v4_clin_chg_empl_stat<-rep(NA,dim(v4_clin)[1]) 
v4_clin_chg_empl_stat<-ifelse(v4_clin$v4_wohnsituation_erwerb_aenderung==1, "Y", 
                  ifelse(v4_clin$v4_wohnsituation_erwerb_aenderung==2, "N",v4_clin_chg_empl_stat))

v4_con_chg_empl_stat<-rep(NA,dim(v4_con)[1]) 
v4_con_chg_empl_stat<-ifelse(v4_con$v4_bildung_beruf_bild_stand==1, "Y", 
                  ifelse(v4_con$v4_bildung_beruf_bild_stand==2, "N",v4_con_chg_empl_stat))

v4_chg_empl_stat<-factor(c(v4_clin_chg_empl_stat,v4_con_chg_empl_stat))
descT(v4_chg_empl_stat)
##                N    Y   <NA>     
## [1,] No. cases 467  65  1011 1543
## [2,] Percent   30.3 4.2 65.5 100

Currently paid employment (dichotomous, v4_curr_paid_empl)

Because of several categories that are unique to the Germany labor market, several of answer categories were pooled to arrive at a more clear-cut (Y/N) answer to this question. Thr following transformations were used: “no information”-NA, “full-time”-Y, “part-time”-Y, “partial retirement”-Y, “marginal employment”-Y, “1-euro-job”-Y, “Occassionally/infrequently”-999, “in professional training”-Y, “professional retraining”-Y, “voluntary service/alternative military service”-Y, “maternity leave or other leave”-Y, “not employed”-N.

v4_clin_curr_paid_empl<-rep(NA,dim(v4_clin)[1])
v4_clin_curr_paid_empl<-ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==1,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==2,"Y",   
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==3,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==4,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==5,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==6,-999,  
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==7,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==8,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==9,"Y",
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==10,"Y",  
                        ifelse(v4_clin$v4_wohnsituation_erwerbstaetig==11,"N",v4_clin_curr_paid_empl)))))))))))

v4_con_curr_paid_empl<-rep(NA,dim(v4_con)[1])
v4_con_curr_paid_empl<-ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==1,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==2,"Y",    
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==3,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==4,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==5,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==6,-999,   
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==7,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==8,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==9,"Y",
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==10,"Y",   
                        ifelse(v4_con$v4_bildung_beruf_erwerb_taetig==11,"N",v4_con_curr_paid_empl)))))))))))

v4_curr_paid_empl<-factor(c(v4_clin_curr_paid_empl,v4_con_curr_paid_empl))
descT(v4_curr_paid_empl)
##                -999 N    Y   <NA>     
## [1,] No. cases 2    289  231 1021 1543
## [2,] Percent   0.1  18.7 15  66.2 100

Disability pension due to psychological/psychiatric illness (dichotomous, v4_disabl_pens)

NB: Not available (-999) in control participants

v4_clin_disabl_pens<-rep(NA,dim(v4_clin)[1])
v4_clin_disabl_pens<-ifelse(v4_clin$v4_wohnsituation_rente_psych==1,"Y",        
                     ifelse(v4_clin$v4_wohnsituation_rente_psych==2,"N",v4_clin_disabl_pens))       

v4_con_disabl_pens<-rep(-999,dim(v4_con)[1])

v4_disabl_pens<-factor(c(v4_clin_disabl_pens,v4_con_disabl_pens))
descT(v4_disabl_pens)
##                -999 N   Y    <NA>     
## [1,] No. cases 320  144 211  868  1543
## [2,] Percent   20.7 9.3 13.7 56.3 100

Employed in workshop for handicapped persons (dichotomous, v4_spec_emp)

v4_clin_spec_emp<-rep(NA,dim(v4_clin)[1])
v4_clin_spec_emp<-ifelse(v4_clin$v4_wohnsituation_erwerb_werk==1,"Y",           
                  ifelse(v4_clin$v4_wohnsituation_erwerb_werk==2,"N",v4_clin_spec_emp))         

v4_con_spec_emp<-rep(NA,dim(v4_con)[1])
v4_con_spec_emp<-ifelse(v4_con$v4_bildung_beruf_erwerb_wfbm==1,"Y",         
                 ifelse(v4_con$v4_bildung_beruf_erwerb_wfbm==2,"N",v4_con_spec_emp))            


v4_spec_emp<-factor(c(v4_clin_spec_emp,v4_con_spec_emp))
descT(v4_spec_emp)
##                N   Y   <NA>     
## [1,] No. cases 151 51  1341 1543
## [2,] Percent   9.8 3.3 86.9 100

Weeks of work absence due to psychological distress in past six months (continuous [weeks], v4_wrk_abs_pst_6_mths)

Cases are set ot -999 in the following cases: 1) Pension, 2) Unknown, 3) Filled out but >26 weeks.

v4_clin_wrk_abs_pst_6_mths<-rep(NA,dim(v4_clin)[1])
v4_clin_wrk_abs_pst_6_mths<-ifelse((v4_clin$v4_wohnsituation_erwerb_unbekannt==1 | v4_clin$v4_wohnsituation_erwerb_rente==1 |  
                                 v4_clin$v4_wohnsituation_erwerb_fehlen>26),-999, v4_clin$v4_wohnsituation_erwerb_fehlen)

v4_con_wrk_abs_pst_6_mths<-rep(NA,dim(v4_con)[1])
v4_con_wrk_abs_pst_6_mths<-ifelse((v4_con$v4_bildung_beruf_erwerb_ausfallu==1 | v4_con$v4_bildung_beruf_erwerb_rente==1 |  
                                 v4_con$v4_bildung_beruf_erwerb_ausfallm>26),-999, v4_con$v4_bildung_beruf_erwerb_ausfallm)

v4_wrk_abs_pst_6_mths<-c(v4_clin_wrk_abs_pst_6_mths,v4_con_wrk_abs_pst_6_mths)
descT(v4_wrk_abs_pst_6_mths)
##                -999 0   1   2   3   4   5   6   8   10  12  13  15  16 
## [1,] No. cases 258  123 12  3   6   5   2   5   1   3   4   1   1   1  
## [2,] Percent   16.7 8   0.8 0.2 0.4 0.3 0.1 0.3 0.1 0.2 0.3 0.1 0.1 0.1
##      20  22  24  26  <NA>     
## [1,] 2   1   3   4   1108 1543
## [2,] 0.1 0.1 0.2 0.3 71.8 100

Currently impaired by psychological/psychiatric symptoms in exercising profession (dichotomous, v4_cur_work_restr)

Important: if receiving pension, this question refers to impairments in the household

v4_clin_cur_work_restr<-rep(NA,dim(v4_clin)[1])
v4_clin_cur_work_restr<-ifelse(v4_clin$v4_wohnsituation_erwerb_psysymptom==1,"Y",   
                    ifelse(v4_clin$v4_wohnsituation_erwerb_psysymptom==2,"N",v4_clin_cur_work_restr))    

v4_con_cur_work_restr<-rep(NA,dim(v4_con)[1])
v4_con_cur_work_restr<-ifelse(v4_con$v4_bildung_beruf_erwerb_psyeinsch==1,"Y",  
                    ifelse(v4_con$v4_bildung_beruf_erwerb_psyeinsch==2,"N",v4_con_cur_work_restr))   

v4_cur_work_restr<-factor(c(v4_clin_cur_work_restr,v4_con_cur_work_restr))
descT(v4_cur_work_restr)
##                N    Y    <NA>     
## [1,] No. cases 279  188  1076 1543
## [2,] Percent   18.1 12.2 69.7 100

Self-reported Weight (continuous [kilograms], v4_weight)

v4_weight<-c(v4_clin$v4_wohnsituation_erwerb_gewicht,v4_con$v4_bildung_beruf_erwerb_gewicht)
summary(v4_weight)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##      25      72      85      88     100     193    1019

BMI (continuous [BMI], v4_bmi)

We here provide the body mass index of study participants, calculated as weight in kilograms divided by the squared height in meters.

v4_bmi<-v4_weight/(v1_height/100)^2
summary(v4_bmi)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    8.65   24.76   28.07   29.15   32.69   70.02    1021

Create dataset

v4_dem<-data.frame(v4_cng_mar_stat,v4_marital_stat,v4_partner,v4_no_bio_chld,v4_no_adpt_chld,v4_stp_chld,v4_chg_hsng,v4_liv_aln,
                    v4_chg_empl_stat,v4_curr_paid_empl,v4_disabl_pens,v4_spec_emp,v4_wrk_abs_pst_6_mths,v4_cur_work_restr,
                    v4_weight,v4_bmi)

Visit 4: Assessment of disease course (OPCRIT Item 90) (ordinal [1,2,3,4,5], v4_opcrit)

The OPCRIT is an operational criteria checklist (and computer program) for psychotic illness (McGuffin, Farmer, & Harvey, 1991). We use item 90 of the OPCRIT to broadly assess the course of disorder from onset to the current state. All available information is to be used to answer the item (interview, medical records etc.).

IMPORTANT: this item was assessed in CLINICAL participants only, all CONTROL participants are assigned -999.

In clinical participants, this item has the following gradation: “single episode wirh good remission”-1, “multiple episodes with good remission between episodes”-2,“multiple episodes with partial remission between episodes”-3, “ongoing chronic disease”-4, “ongoing chronic disease with deterioration”-5 and “not estimable”-99. In the current dataset, 99 is replaced with -999. Note: this item is to be rated hierarchically, meaning if the past course of disease is to be rated with 2 but the present course of disease would require a 4, 4 is the right assessment.

v4_opcrit<-c(v4_clin$v4_opcrit_opcrit_verlauf,rep(-999,dim(v4_con)[1]))
v4_opcrit[v4_opcrit==99]<--999
descT(v4_opcrit)
##                -999 1   2    3    4   5   <NA>     
## [1,] No. cases 321  19  166  165  99  9   764  1543
## [2,] Percent   20.8 1.2 10.8 10.7 6.4 0.6 49.5 100

Visit 4: Life events precipitating illness episode between study visits

Please see Visit 2 for explanation.

**Life events: Occurred before illness episode? (dichotomous, v4_evnt_prcp_b4_*)**

for(i in 1:length(grep("v4_ergaenz_leq_leq_zeit_31055_",names(v4_clin)))){
  b4_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_zeit_31055_",names(v4_clin))[i]],
                   paste("v4_evnt_prcp_b4_",i,sep=""))
}

**Life events: Was a precipitating factor for illness episode (categorical [N,U,Y], v4_evnt_prcp_f_*)**

for(i in 1:length(grep("v4_ergaenz_leq_leq_ausloeser_31055_",names(v4_clin)))){
  prcp_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_ausloeser_31055_",names(v4_clin))[i]],
                   paste("v4_evnt_prcp_f_",i,sep=""))
}

**Life events: LEQ item number (categorical [LEQ item number], v4_evnt_prcp_it_*)**

for(i in 1:length(grep("v4_ergaenz_leq_leq_item_31055_",names(v4_clin)))){
  leq_event_recode_v4(v4_clin[,grep("v4_ergaenz_leq_leq_item_31055_",names(v4_clin))[i]],
                   paste("v4_evnt_prcp_it_",i,sep=""))
}

Create dataset

v4_leprcp<-data.frame(v4_evnt_prcp_it_1,v4_evnt_prcp_b4_1,v4_evnt_prcp_f_1,
                      v4_evnt_prcp_it_2,v4_evnt_prcp_b4_2,v4_evnt_prcp_f_2,
                      v4_evnt_prcp_it_3,v4_evnt_prcp_b4_3,v4_evnt_prcp_f_3,
                      v4_evnt_prcp_it_4,v4_evnt_prcp_b4_4,v4_evnt_prcp_f_4,
                      v4_evnt_prcp_it_5,v4_evnt_prcp_b4_5,v4_evnt_prcp_f_5,
                      v4_evnt_prcp_it_6,v4_evnt_prcp_b4_6,v4_evnt_prcp_f_6,
                      v4_evnt_prcp_it_7,v4_evnt_prcp_b4_7,v4_evnt_prcp_f_7,
                      v4_evnt_prcp_it_8,v4_evnt_prcp_b4_8,v4_evnt_prcp_f_8,
                      v4_evnt_prcp_it_9,v4_evnt_prcp_b4_9,v4_evnt_prcp_f_9,
                      v4_evnt_prcp_it_10,v4_evnt_prcp_b4_10,v4_evnt_prcp_f_10,
                      v4_evnt_prcp_it_11,v4_evnt_prcp_b4_11,v4_evnt_prcp_f_11,
                      v4_evnt_prcp_it_12,v4_evnt_prcp_b4_12,v4_evnt_prcp_f_12,
                      v4_evnt_prcp_it_13,v4_evnt_prcp_b4_13,v4_evnt_prcp_f_13,
                      v4_evnt_prcp_it_14,v4_evnt_prcp_b4_14,v4_evnt_prcp_f_14,
                      v4_evnt_prcp_it_15,v4_evnt_prcp_b4_15,v4_evnt_prcp_f_15,
                      v4_evnt_prcp_it_16,v4_evnt_prcp_b4_16,v4_evnt_prcp_f_16,
                      v4_evnt_prcp_it_17,v4_evnt_prcp_b4_17,v4_evnt_prcp_f_17,
                      v4_evnt_prcp_it_18,v4_evnt_prcp_b4_18,v4_evnt_prcp_f_18,
                      v4_evnt_prcp_it_19,v4_evnt_prcp_b4_19,v4_evnt_prcp_f_19,
                      v4_evnt_prcp_it_20,v4_evnt_prcp_b4_20,v4_evnt_prcp_f_20,
                      v4_evnt_prcp_it_21,v4_evnt_prcp_b4_21,v4_evnt_prcp_f_21,
                      v4_evnt_prcp_it_22,v4_evnt_prcp_b4_22,v4_evnt_prcp_f_22,
                      v4_evnt_prcp_it_23,v4_evnt_prcp_b4_23,v4_evnt_prcp_f_23,
                      v4_evnt_prcp_it_24,v4_evnt_prcp_b4_24,v4_evnt_prcp_f_24,
                      v4_evnt_prcp_it_25,v4_evnt_prcp_b4_25,v4_evnt_prcp_f_25,
                      v4_evnt_prcp_it_26,v4_evnt_prcp_b4_26,v4_evnt_prcp_f_26,
                      v4_evnt_prcp_it_27,v4_evnt_prcp_b4_27,v4_evnt_prcp_f_27,
                      v4_evnt_prcp_it_28,v4_evnt_prcp_b4_28,v4_evnt_prcp_f_28,
                      v4_evnt_prcp_it_29,v4_evnt_prcp_b4_29,v4_evnt_prcp_f_29,
                      v4_evnt_prcp_it_30,v4_evnt_prcp_b4_30,v4_evnt_prcp_f_30,
                      v4_evnt_prcp_it_31,v4_evnt_prcp_b4_31,v4_evnt_prcp_f_31)

Visit 4: Suicide attempts and suicidal ideation since last study visit

Please note that all of the following questions refer to suicide attempts and suicidal ideation occurring since the last study visit.

Suicidal ideation

Suicidal ideation since last study visit (dichotomous, v4_suic_ide_snc_lst_vst)

Please not that the following items on suicidal ideation were skipped if this question was not answered positively. If skipped these items are coded -999.

v4_suic_ide_snc_lst_vst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_suic_ide_snc_lst_vst<-ifelse(c(v4_clin$v4_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v4_con)[1]))==1, "N", 
                            ifelse(c(v4_clin$v4_snx_112_suizged1_x7_suizid_gedanken,rep(-999,dim(v4_con)[1]))==3, "Y",                                                 v4_suic_ide_snc_lst_vst))

v4_suic_ide_snc_lst_vst<-factor(v4_suic_ide_snc_lst_vst)
descT(v4_suic_ide_snc_lst_vst)
##                -999 N    Y   <NA>     
## [1,] No. cases 320  365  108 750  1543
## [2,] Percent   20.7 23.7 7   48.6 100

Suicidal ideation detailed (ordinal [1,2,3,4], v4_scid_suic_ide)

This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v4_scid_suic_ide<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_scid_suic_ide<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==3, "3",
                                       ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x8_suizged_inhalt,rep(-999,dim(v4_con)[1]))==4, "4",-999))))

v4_scid_suic_ide<-factor(v4_scid_suic_ide,ordered=T)                                   
descT(v4_scid_suic_ide)
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 685  69  18  8   13  750  1543
## [2,] Percent   44.4 4.5 1.2 0.5 0.8 48.6 100

Thoughts about methods (ordinal [1,2,3], v4_scid_suic_thght_mth)

This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3.

v4_scid_suic_thght_mth<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_scid_suic_thght_mth<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x10_suiz_methoden,rep(-999,dim(v4_con)[1]))==3, "3",-999)))

v4_scid_suic_thght_mth<-factor(v4_scid_suic_thght_mth,ordered=T)                                   
descT(v4_scid_suic_thght_mth)
##                -999 1  2   3   <NA>     
## [1,] No. cases 685  62 35  10  751  1543
## [2,] Percent   44.4 4  2.3 0.6 48.7 100

Suicidal ideation: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v4_scid_suic_note_thgts)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v4_scid_suic_note_thgts<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_scid_suic_note_thgts<-ifelse(v4_suic_ide_snc_lst_vst=="Y"&c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==3, "3",
                                       ifelse(v4_suic_ide_snc_lst_vst=="Y" & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==4, "4",-999))))

v4_scid_suic_note_thgts<-factor(v4_scid_suic_note_thgts,ordered=T)                                   
descT(v4_scid_suic_note_thgts)
##                -999 1   2   4   <NA>     
## [1,] No. cases 685  98  3   3   754  1543
## [2,] Percent   44.4 6.4 0.2 0.2 48.9 100

Suicide attemps

Suicide attempt since last study visit (ordinal [1,2,3], v4_suic_attmpt_snc_lst_vst)

This is an ordinal item with the following gradation: “no”-1, “interruption of attempt”-2, “yes”-3. Please not that the following items on suicidal attempt were skipped if this question was answered with “no”. In that case, items are coded as -999.

v4_suic_attmpt_snc_lst_vst<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_suic_attmpt_snc_lst_vst<-ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==1, "1",
                         ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==2, "2",
                                ifelse(c(v4_clin$v4_snx_111_suizvrs1_x1_suiz_vers,rep(-999,dim(v4_con)[1]))==3, "3",-999)))

v4_suic_attmpt_snc_lst_vst<-factor(v4_suic_attmpt_snc_lst_vst,ordered=T)                                   
descT(v4_suic_attmpt_snc_lst_vst)
##                -999 1    2   3   <NA>     
## [1,] No. cases 320  457  3   2   761  1543
## [2,] Percent   20.7 29.6 0.2 0.1 49.3 100

Number of suicide attempts (ordinal [1,2,3,4,5,6], v4_no_suic_attmpt)

This is an ordinal item with the following gradation: “1 time”-1, “2 times”-2, “3-times”-3, “4 times”-4, “5 times”-5, “6 or more times”-6.

v4_no_suic_attmpt<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))
v4_no_suic_attmpt<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999, ifelse(v4_suic_attmpt_snc_lst_vst>1, c(v4_clin$v4_snx_111_suizvrs1_x2_suiz_anz,rep(-999,dim(v4_con)[1])),v4_no_suic_attmpt))

v4_no_suic_attmpt<-factor(v4_no_suic_attmpt,ordered=T)
descT(v4_no_suic_attmpt)
##                -999 1   3   <NA>     
## [1,] No. cases 777  4   1   761  1543
## [2,] Percent   50.4 0.3 0.1 49.3 100

Preparation of suicide attempt (ordinal [1,2,3,4], v4_prep_suic_attp_ord)

This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4.

v4_prep_suic_attp_ord<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_prep_suic_attp_ord<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999, 
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==1, "1",
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==2, "2",             
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==3, "3",
                              ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_111_suizvrs1_x5_suiz_vorb,rep(-999,dim(v4_con)[1]))==4, "4",
                              v4_prep_suic_attp_ord))))) 

v4_prep_suic_attp_ord<-factor(v4_prep_suic_attp_ord,ordered=T)
descT(v4_prep_suic_attp_ord)
##                -999 1   3   4   <NA>     
## [1,] No. cases 777  1   1   2   762  1543
## [2,] Percent   50.4 0.1 0.1 0.1 49.4 100

Suicidal attempt: suicide note or similar [German:“Abschiedshandlung”] (ordinal [1,2,3,4], v4_suic_note_attmpt)

This is an ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4.

v4_suic_note_attmpt<-c(rep(NA,dim(v4_clin)[1]),rep(-999,dim(v4_con)[1]))

v4_suic_note_attmpt<-ifelse(v4_suic_attmpt_snc_lst_vst==1, -999, 
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==1, "1",
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==2, "2",
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==3, "3",
                            ifelse(v4_suic_attmpt_snc_lst_vst>1 & c(v4_clin$v4_snx_112_suizged1_x11_best_erledigt,rep(-999,dim(v4_con)[1]))==4, "4",
                            v4_suic_note_attmpt))))) 

v4_suic_note_attmpt<-factor(v4_suic_note_attmpt,ordered=T)
descT(v4_suic_note_attmpt)
##                -999 1   2   4   <NA>     
## [1,] No. cases 777  2   1   1   762  1543
## [2,] Percent   50.4 0.1 0.1 0.1 49.4 100

Create dataset

v4_suic<-data.frame(v4_suic_ide_snc_lst_vst,v4_scid_suic_ide,v4_scid_suic_thght_mth,v4_scid_suic_note_thgts,
                    v4_suic_attmpt_snc_lst_vst,v4_no_suic_attmpt,v4_prep_suic_attp_ord,
                    v4_suic_note_attmpt)

Visit 4: Medication

PsyCourse 3.1 contains now medication data. The code below creates the following variables for each person:

Number of antidepressants prescribed (continuous [number], v4_Antidepressants) Number of antipsychotics prescribed (continuous [number], v4_Antipsychotics) Number of mood stabilizers prescribed (continuous [number], v4_Mood_stabilizers) Number of tranquilizers prescribed (continuous [number], v4_Tranquilizers) Number of other psychiatric medications (continuous [number], v4_Other_psychiatric)

Clinical participants

#get the following variables from v4_clin
#1. Medication name     ["_med_medi_1"]
#2. Medication category ["_med_kategorie_1"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_1"]
#6. Bedarf category     ["_bedarf_kategorie_1"]

v4_clin_medication_variables_1<-as.data.frame(v4_clin[,grep("mnppsd|_med_medi_1|_med_kategorie_1|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_1|_bedarf_kategorie_1",names(v4_clin))])
dim(v4_clin_medication_variables_1) #[1] 1223   61
## [1] 1223   61
#recode the variables that are coded as characters/logicals in the "v4_clin_medication_variables_1" as factors
v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_15<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_15)

v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_15<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_15)

v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_16<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_16)

v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_16<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_16)

v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_17<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_medi_199998_17)

v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_17<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_med_kategorie_199998_17)

v4_clin_medication_variables_1$v4_medikabehand3_depot_medi_200170_3<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_depot_medi_200170_3)

v4_clin_medication_variables_1$v4_medikabehand3_depot_kategorie_200170_3<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_depot_kategorie_200170_3)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_8<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_8)
v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_8<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_8)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_9<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_9)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_9<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_9)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_10<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_medi_199584_10)

v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_10<-as.factor(v4_clin_medication_variables_1$v4_medikabehand3_bedarf_kategorie_199584_10)

#make the duplicated data frame
v4_clin_medications_duplicated_1<-as.data.frame(t(apply(v4_clin_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v4_clin_medications_duplicated_1) #1223   30
## [1] 1223   30
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_clin, not as character
v4_clin_medication_variables_1[,!c(TRUE, FALSE)][v4_clin_medications_duplicated_1=="TRUE"] <- NA
dim(v4_clin_medication_variables_1) #1223   61
## [1] 1223   61
#bind columns id and medication names, but not categories together 
v4_clin_medication_name_1<-as.data.frame(cbind("mnppsd"=v4_clin_medication_variables_1[,1], v4_clin_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v4_clin_medication_name_1) #1223   31
## [1] 1223   31
#get the medication categories from the "_medication_variables_1" dataframe
v4_clin_medication_categories_1<-as.data.frame(v4_clin_medication_variables_1[,c(TRUE, FALSE)])
dim(v4_clin_medication_categories_1) #1223   31
## [1] 1223   31
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_clin, not as character 
#Important: v4_clin_medication_name_1=="NA" replaced with is.na(v4_clin_medication_name_1)
v4_clin_medication_categories_1[is.na(v4_clin_medication_name_1)] <- NA
#write.csv(v4_clin_medication_categories_1, file="v4_clin_medication_group_1.csv") 

#Make a count table of medications
v4_clin_med_table<-data.frame("mnppsd"=v4_clin$mnppsd)
v4_clin_med_table$v4_Antidepressants<-rowSums(v4_clin_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v4_clin_med_table$v4_Antipsychotics<-rowSums(v4_clin_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v4_clin_med_table$v4_Mood_stabilizers<-rowSums(v4_clin_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v4_clin_med_table$v4_Tranquilizers<-rowSums(v4_clin_medication_categories_1 == "Sedativa", na.rm = TRUE)
v4_clin_med_table$v4_Other_psychiatric<-rowSums(v4_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Control participants

#get the following variables from v4_con
#1. Medication name     ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name          ["_depot_medi_2"]
#4. Depot category      ["_depot_kategorie_2"]
#5. Bedarf name         ["_bedarf_medi_2"]
#6. Bedarf category     ["_bedarf_kategorie_2"]

v4_con_medication_variables_1<-as.data.frame(v4_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v4_con))])
dim(v4_con_medication_variables_1) #[1] 320 29 
## [1] 320  29
#recode the variables that are coded as characters/logicals in the "v4_con_medication_variables_1" as factors
v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_7<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_7)

v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_7<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_7)

v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_8<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_medi_200705_8)

v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_8<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_med_kategorie_200705_8)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_2<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_2)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_2<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_2)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_4<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_medi_201187_4)

v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_4<-as.factor(v4_con_medication_variables_1$v4_medikabehand3_bedarf_kategorie_201187_4)

#make the duplicated data frame
v4_con_medications_duplicated_1<-as.data.frame(t(apply(v4_con_medication_variables_1[,!c(TRUE,FALSE)], 1, duplicated)))
dim(v4_con_medications_duplicated_1) #320  14
## [1] 320  14
#recode all duplicated variables, i.e. overlay "_medication_variables_" and "_medications_duplicated_". 
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_con, not as character 
v4_con_medication_variables_1[,!c(TRUE, FALSE)][v4_con_medications_duplicated_1=="TRUE"] <- NA
dim(v4_con_medication_variables_1) #320   29
## [1] 320  29
#bind columns id and medication names, but not categories together 
v4_con_medication_name_1<-as.data.frame(cbind("mnppsd"=v4_con_medication_variables_1[,1], v4_con_medication_variables_1[,!c(TRUE, FALSE)]))
dim(v4_con_medication_name_1) #320  15
## [1] 320  15
#get the medication categories from the "_medication_variables_1" dataframe
v4_con_medication_categories_1<-as.data.frame(v4_con_medication_variables_1[,c(TRUE, FALSE)])
dim(v4_con_medication_categories_1) #320  15
## [1] 320  15
#recode all duplicated variables, i.e. overlay "_medication_categories_" and "_medication_name_"
#Important: quotes from "NA" are removed, because variable are coded as facors in v4_con, not as character 
#Important: v4_con_medication_name_1=="NA" replaced with is.na(v4_con_medication_name_1)
v4_con_medication_categories_1[is.na(v4_con_medication_name_1)] <- NA
#write.csv(v4_con_medication_categories_1, file="v4_con_medication_group_1.csv")

#Make a count table of medications
v4_con_med_table<-data.frame("mnppsd"=v4_con$mnppsd)
v4_con_med_table$v4_Antidepressants<-rowSums(v4_con_medication_categories_1 == "Antidepressiva", na.rm = TRUE)
v4_con_med_table$v4_Antipsychotics<-rowSums(v4_con_medication_categories_1 == "Antipsychotika", na.rm = TRUE)
v4_con_med_table$v4_Mood_stabilizers<-rowSums(v4_con_medication_categories_1 == "Phasenprophylaktika", na.rm = TRUE)
v4_con_med_table$v4_Tranquilizers<-rowSums(v4_con_medication_categories_1 == "Sedativa", na.rm = TRUE)
v4_con_med_table$v4_Other_psychiatric<-rowSums(v4_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)

Bind v4_clin and v4_con together by rows

v4_drugs<-rbind(v4_clin_med_table,v4_con_med_table)
dim(v4_drugs) #1543    6
## [1] 1543    6
#check if the id column of v4_drugs and v1_id match
table(droplevels(v4_drugs[,1])==v1_id)
## 
## TRUE 
## 1543

Adverse events under current medication (dichotomous, v4_adv)

v4_clin_adv<-ifelse(v4_clin$v4_medikabehand_medi2_nebenwirk==1,"Y","N")
v4_con_adv<-rep("-999",dim(v4_con)[1])
v4_adv<-factor(c(v4_clin_adv,v4_con_adv))
descT(v4_adv)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  159  237  827  1543
## [2,] Percent   20.7 10.3 15.4 53.6 100

Psychiatric medication change during the past six months (dichotomous, v4_medchange)

v4_clin_medchange<-rep(NA,dim(v4_clin)[1])
v4_clin_medchange<-ifelse(v4_clin$v4_medikabehand_medi3_mediaenderung==1,"Y","N")
v4_con_medchange<-rep("-999",dim(v4_con)[1])

v4_medchange<-as.factor(c(v4_clin_medchange,v4_con_medchange))
descT(v4_medchange)
##                -999 N    Y    <NA>     
## [1,] No. cases 320  177  217  829  1543
## [2,] Percent   20.7 11.5 14.1 53.7 100

Lithium

Please see the section in Visit 1 for explanation.

“Did you ever take lithium?” (Dichotomous, v4_lith)

v4_clin_lith<-rep(NA,dim(v4_clin)[1])
v4_clin_lith<-ifelse(v4_clin$v4_medikabehand_med_zusatz_lithium==1,"Y","N")
v4_con_lith<-rep("-999",dim(v4_con)[1])

v4_lith<-as.factor(c(v4_clin_lith,v4_con_lith))
v4_lith<-as.factor(v4_lith)

descT(v4_lith)
##                -999 N    Y   <NA>     
## [1,] No. cases 320  256  147 820  1543
## [2,] Percent   20.7 16.6 9.5 53.1 100

“If yes, for how long?” (Dichotomous, v4_lith_prd)

Ordinal variable, gradation the following: “less than one year”-1, “one to two years”-2, “two or more years”-3.

v4_clin_lith_prd<-rep(NA,dim(v4_clin)[1])
v4_con_lith_prd<-rep(-999,dim(v4_con)[1])

v4_clin_lith_prd<-ifelse(v4_clin_lith=="N", -999, ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==2,1,
                  ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==1,2,    
                  ifelse(v4_clin$v4_medikabehand_med_zusatz_dauer==0,3,NA))))
                                                     
v4_lith_prd<-factor(c(v4_clin_lith_prd,v4_con_lith_prd))
descT(v4_lith_prd)
##                -999 1   2   3   <NA>     
## [1,] No. cases 576  42  26  79  820  1543
## [2,] Percent   37.3 2.7 1.7 5.1 53.1 100

Visit 4: ALDA scale

The ALDA scale (P. Grof et al., 2002) measures reponse to lithium and was thus only used in clinical participants (see below). Control subjects, and

have a -999 in this item. The scale is formally called “Retrospective criteria of long-term treatment response in research subjects with bipolar disorder”. The ALDA scale quantifies symptom improvement in the course of treatment (A score, range 0–10), which is then weighted against five criteria (B score) that assess confounding factors, each scored 0,1, or 2. The total score is then derived by subtracting the total B score from the A score. Negative scores are set to 0 by default so that the total score ranges from 0 to 10 (Hou et al., 2016).

This questionnaire was only assessed if

  1. The participant had DSM-IV diagnosis of bipolar I oder bipolar II disorder, and
  2. If the patent has ever been treated continuously with lithium for at least one year.

The scale was also assessed in some clinical participants with other diagnoses, because the bipolar diagnosis criterion had not been formalized at the start of the study.

Now, the ALDA items are coded so that all individuals with values on these item are included in the dataset. If no value is given (NA), and the fourth visit took place, all diagnoses other that BP-I and BP-II (this includes controls), including BP1 and BP2 individuals that never (or for too little time) received lithium, are coded -999.

The ALDA Total Score is given as in the paper CRF, please check yourself if it was correctly calculated

A score (continuous [0,1,2,3,4,5,6,7,8,9,10], v4_alda_A)

v4_clin_alda_A<-rep(NA,dim(v4_clin)[1])
v4_con_alda_A<-rep(-999,dim(v4_con)[1])

v4_clin_alda_A<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_a_score)==F, v4_clin$v4_lithium_lithium_crit_a_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_A<-c(v4_clin_alda_A,v4_con_alda_A)
summary(v4_alda_A[v4_alda_A>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.000   7.000   6.147   8.000  10.000     765

B1 score (continuous [0,1,2], v4_alda_B1)

v4_clin_alda_B1<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B1<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B1<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_b1)==F, v4_clin$v4_lithium_lithium_crit_b1, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
v4_alda_B1<-c(v4_clin_alda_B1,v4_con_alda_B1)
summary(v4_alda_B1[v4_alda_B1>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.3864  1.0000  2.0000     772

B2 score (continuous [0,1,2], v4_alda_B2)

v4_clin_alda_B2<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B2<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B2<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_b2)==F, v4_clin$v4_lithium_lithium_crit_b2, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B2<-c(v4_clin_alda_B2,v4_con_alda_B2)
summary(v4_alda_B2[v4_alda_B2>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0     0.0     0.0     0.5     1.0     2.0     773

B3 score (continuous [0,1,2], v4_alda_B3)

v4_clin_alda_B3<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B3<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B3<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_b3)==F, v4_clin$v4_lithium_lithium_crit_b3, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B3<-c(v4_clin_alda_B3,v4_con_alda_B3)
summary(v4_alda_B3[v4_alda_B3>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   0.236   0.000   1.000     771

B4 score (continuous [0,1,2], v4_alda_B4)

v4_clin_alda_B4<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B4<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B4<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_b4)==F, v4_clin$v4_lithium_lithium_crit_b4, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B4<-c(v4_clin_alda_B4,v4_con_alda_B4)
summary(v4_alda_B4[v4_alda_B4>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.2727  0.0000  2.0000     772

B5 score (continuous [0,1,2], v4_alda_B5)

v4_clin_alda_B5<-rep(NA,dim(v4_clin)[1])
v4_con_alda_B5<-rep(-999,dim(v4_con)[1])

v4_clin_alda_B5<-ifelse(is.na(v4_clin$v4_lithium_lithium_crit_b5)==F, v4_clin$v4_lithium_lithium_crit_b5, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))

v4_alda_B5<-c(v4_clin_alda_B5,v4_con_alda_B5)
summary(v4_alda_B5[v4_alda_B5>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   2.000   1.299   2.000   2.000     773

Total score (continuous [0,1,2,3,4,5,6,7,8,9,10], v4_alda_tot)

v4_clin_alda_tot<-rep(NA,dim(v4_clin)[1])
v4_con_alda_tot<-rep(-999,dim(v4_con)[1])

v4_clin_alda_tot<-ifelse(is.na(v4_clin$v4_lithium_lithium_total_score)==F, v4_clin$v4_lithium_lithium_total_score, 
                       ifelse(is.na(v4_interv_date),NA,
                              ifelse(!(v1_scid_dsm_dx_cat[v1_stat=="CLINICAL"]%in% c("Bipolar-I Disorder","Bipolar-II Disorder"))
                                       | (v4_clin$v4_medikabehand3_med_zusatz_lithium==0 |               
                                            v4_clin$v4_medikabehand3_med_zusatz_dauer==2),-999,NA)))
                
v4_clin_alda_tot[v4_clin_alda_tot<0]<-0 #set all negative values to zero

v4_alda_tot<-c(v4_clin_alda_tot,v4_con_alda_tot)
summary(v4_alda_tot[v4_alda_tot>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.8017  0.0000 10.0000     764

Create dataset

v4_med<-data.frame(v4_drugs[,2:6],v4_adv,v4_medchange,v4_lith,v4_lith_prd,v4_alda_A,v4_alda_B1,v4_alda_B2,v4_alda_B3,v4_alda_B4,v4_alda_B5,v4_alda_tot)

Visit 4: Substance abuse

Tobacco

For more explanation, see Visit 1

“Did you start or stop smoking during the past six months?” (categorical [NS,NN,YSP,YST], v4_smk_strt_stp)

This is a categorical item with four optional answers: “no, still smoker”-NS, “no, still nonsmoker”-NN, and “yes, stopped smoking (more than three months ago)” -YSP, “yes, started smoking (more than three months ago)”-YST.

v4_clin_smk_strt_stp<-rep(NA,dim(v4_clin)[1])
v4_clin_smk_strt_stp<-ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==1,"NS",
                        ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==2,"NN",
                          ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==3,"YSP",
                            ifelse(v4_clin$v4_tabalk1_ta1_jemals_rauch==4,"YST",v4_clin_smk_strt_stp))))
       
#ATTENTION: answering alternative: e-cigarette only in controls
v4_con_smk_strt_stp<-rep(NA,dim(v4_con)[1])
v4_con_smk_strt_stp<-ifelse(v4_con$v4_tabalk_folge_tabak1==1 | v4_con$v4_tabalk_folge_tabak1==2,"NS",
                        ifelse(v4_con$v4_tabalk_folge_tabak1==3,"NN",
                          ifelse(v4_con$v4_tabalk_folge_tabak1==4,"YSP",
                            ifelse(v4_con$v4_tabalk_folge_tabak1==5,"YST",v4_con_smk_strt_stp))))
                        
v4_smk_strt_stp<-c(v4_clin_smk_strt_stp,v4_con_smk_strt_stp)
descT(v4_smk_strt_stp)
##                NN   NS   YSP YST <NA>     
## [1,] No. cases 220  298  11  7   1007 1543
## [2,] Percent   14.3 19.3 0.7 0.5 65.3 100

“How many cigarettes do you presently smoke on average?” (continuous [number cigarettes], v4_no_cig)

In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.

Please not that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur.

v4_no_cig<-c(rep(NA,dim(v4_clin)[1]),rep(NA,dim(v4_con)[1]))

v4_no_cig<-ifelse((v4_smk_strt_stp=="NN" | v4_smk_strt_stp=="YSP"), -999, 
            ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") & 
                     c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==1,                                
                     c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*365,
            ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") & 
                     c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==2,                                
                     c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*52,
            ifelse((v4_smk_strt_stp=="NS" | v4_smk_strt_stp=="YST") & 
                     c(v4_clin$v4_tabalk1_ta3_zig_pro_zeit,v4_con$v4_tabalk_folge_tabak2_zeit)==3,                                
                     c(v4_clin$v4_tabalk1_ta3_anz_zig,v4_con$v4_tabalk_folge_tabak2_anz)*12,
                      v4_no_cig))))

summary(v4_no_cig[v4_no_cig>=0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0    3650    7300    6913    9125   21900    1081

Alcohol

“How often did you consume alcoholic beverages during the past six months?” (ordinal [1,2,3,4,5,6,7], v4_alc_pst6_mths)

This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.

v4_alc_pst6_mths<-c(v4_clin$v4_tabalk1_ta9_alkkonsum,v4_con$v4_tabalk_folge_alkohol4)
v4_alc_pst6_mths<-factor(v4_alc_pst6_mths, ordered=T)

descT(v4_alc_pst6_mths)
##                1   2   3  4   5   6   7   <NA>     
## [1,] No. cases 154 121 61 87  67  23  24  1006 1543
## [2,] Percent   10  7.8 4  5.6 4.3 1.5 1.6 65.2 100

“On how many occasions during the past six months did you drink FIVE (men)/FOUR (women) or more alcoholic beverages?”" (ordinal [1,2,3,4,5,6,7,8,9], v4_alc_5orm)

This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.

v4_alc_5orm<-ifelse(v4_alc_pst6_mths<4,-999,
                    ifelse(is.na(c(v4_clin$v4_tabalk1_ta10_alk_haeufigk_m1,v4_con$v4_tabalk_folge_alkohol5))==T,   
                            c(v4_clin$v4_tabalk1_ta11_alk_haeufigk_f1,v4_con$v4_tabalk_folge_alkohol6),
                            c(v4_clin$v4_tabalk1_ta10_alk_haeufigk_m1,v4_con$v4_tabalk_folge_alkohol5)))

v4_alc_5orm<-factor(v4_alc_5orm, ordered=T)

descT(v4_alc_5orm)
##                -999 1   2   3  4   5   6   7   8   9   <NA>     
## [1,] No. cases 336  123 27  16 5   7   6   10  3   4   1006 1543
## [2,] Percent   21.8 8   1.7 1  0.3 0.5 0.4 0.6 0.2 0.3 65.2 100

Illicit drugs

For more information see in visit 1 and 2.

“During the past six months, did you take ANY illicit drugs?” (dichotomous, v4_pst6_ill_drg)

v4_pst6_ill_drg<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_pst6_ill_drg<-ifelse(c(v4_clin$v4_drogen1_dg1_konsum,v4_con$v4_drogen_folge_drogenkonsum)==2, "Y", "N")

descT(v4_pst6_ill_drg)
##                N    Y   <NA>     
## [1,] No. cases 502  35  1006 1543
## [2,] Percent   32.5 2.3 65.2 100

Create dataset

v4_subst<-data.frame(v4_smk_strt_stp,
                     v4_no_cig,
                     v4_alc_pst6_mths,
                     v4_alc_5orm,
                     v4_pst6_ill_drg)

Visit 4: Symptom rating scales (interviewer rates patient)

PANSS

For more information on the scale, please see Visit 1

Positive subscale

P1 Delusions (ordinal [1,2,3,4,5,6,7], v4_panss_p1)

v4_panss_p1<-c(v4_clin$v4_panss_p_p1_wahnideen,v4_con$v4_panss_p_p1_wahnideen)
v4_panss_p1<-factor(v4_panss_p1, ordered=T)

descT(v4_panss_p1)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 376  27  45  22  7   7   2   1057 1543
## [2,] Percent   24.4 1.7 2.9 1.4 0.5 0.5 0.1 68.5 100

P2 Conceptual disorganization (ordinal [1,2,3,4,5,6,7], v4_panss_p2)

v4_panss_p2<-c(v4_clin$v4_panss_p_p2_form_denkst,v4_con$v4_panss_p_p2_form_denkst)
v4_panss_p2<-factor(v4_panss_p2, ordered=T)

descT(v4_panss_p2)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 351  42  59  24  10  1057 1543
## [2,] Percent   22.7 2.7 3.8 1.6 0.6 68.5 100

P3 Hallucinatory behavior (ordinal [1,2,3,4,5,6,7], v4_panss_p3)

v4_panss_p3<-c(v4_clin$v4_panss_p_p3_halluz,v4_con$v4_panss_p_p3_halluz)
v4_panss_p3<-factor(v4_panss_p3, ordered=T)

descT(v4_panss_p3)
##                1    2   3   4  5   6   <NA>     
## [1,] No. cases 413  19  24  15 13  2   1057 1543
## [2,] Percent   26.8 1.2 1.6 1  0.8 0.1 68.5 100

P4 Excitement (ordinal [1,2,3,4,5,6,7], v4_panss_p4)

v4_panss_p4<-c(v4_clin$v4_panss_p_p4_erregung,v4_con$v4_panss_p_p4_erregung)
v4_panss_p4<-factor(v4_panss_p4, ordered=T)

descT(v4_panss_p4)
##                1    2   3   4  5   <NA>     
## [1,] No. cases 376  38  54  16 2   1057 1543
## [2,] Percent   24.4 2.5 3.5 1  0.1 68.5 100

P5 Grandiosity (ordinal [1,2,3,4,5,6,7], v4_panss_p5)

v4_panss_p5<-c(v4_clin$v4_panss_p_p5_groessenideen,v4_con$v4_panss_p_p5_groessenideen)
v4_panss_p5<-factor(v4_panss_p5, ordered=T)

descT(v4_panss_p5)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 446  17  14  4   5   1057 1543
## [2,] Percent   28.9 1.1 0.9 0.3 0.3 68.5 100

P6 Suspiciousness/persecution (ordinal [1,2,3,4,5,6,7], v4_panss_p6)

v4_panss_p6<-c(v4_clin$v4_panss_p_p6_misstr_verfolg,v4_con$v4_panss_p_p6_misstr_verfolg)
v4_panss_p6<-factor(v4_panss_p6, ordered=T)

descT(v4_panss_p6)
##                1    2   3   4  5   6   <NA>     
## [1,] No. cases 377  28  54  15 9   3   1057 1543
## [2,] Percent   24.4 1.8 3.5 1  0.6 0.2 68.5 100

P7 Hostility (ordinal [1,2,3,4,5,6,7], v4_panss_p7)

v4_panss_p7<-c(v4_clin$v4_panss_p_p7_feindseligkeit,v4_con$v4_panss_p_p7_feindseligkeit)
v4_panss_p7<-factor(v4_panss_p7, ordered=T)

descT(v4_panss_p7)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 440  20  22  3   1   1057 1543
## [2,] Percent   28.5 1.3 1.4 0.2 0.1 68.5 100

PANSS Positive sum score (continuous [7-49], v4_panss_sum_pos)

v4_panss_sum_pos<-as.numeric.factor(v4_panss_p1)+
                  as.numeric.factor(v4_panss_p2)+
                  as.numeric.factor(v4_panss_p3)+
                  as.numeric.factor(v4_panss_p4)+
                  as.numeric.factor(v4_panss_p5)+
                  as.numeric.factor(v4_panss_p6)+
                  as.numeric.factor(v4_panss_p7)

summary(v4_panss_sum_pos)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   7.000   7.000   8.000   9.658  11.000  27.000    1057

Negative subscale

N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v4_panss_n1)

v4_panss_n1<-c(v4_clin$v4_panss_n_n1_affektverflachung,v4_con$v4_panss_n_n1_affektverflachung)
v4_panss_n1<-factor(v4_panss_n1, ordered=T)

descT(v4_panss_n1)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 255  57  78  41  51  4   1057 1543
## [2,] Percent   16.5 3.7 5.1 2.7 3.3 0.3 68.5 100

N2 Emotional withdrawal (ordinal [1,2,3,4,5,6,7], v4_panss_n2)

v4_panss_n2<-c(v4_clin$v4_panss_n_n2_emot_rueckzug,v4_con$v4_panss_n_n2_emot_rueckzug)
v4_panss_n2<-factor(v4_panss_n2, ordered=T)

descT(v4_panss_n2)
##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 317  47 64  40  14  4   1057 1543
## [2,] Percent   20.5 3  4.1 2.6 0.9 0.3 68.5 100

N3 Poor rapport (ordinal [1,2,3,4,5,6,7], v4_panss_n3)

v4_panss_n3<-c(v4_clin$v4_panss_n_n3_mang_aff_rapp,v4_con$v4_panss_n_n3_mang_aff_rapp)
v4_panss_n3<-factor(v4_panss_n3, ordered=T)

descT(v4_panss_n3)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 338  45  73  14  10  2   1061 1543
## [2,] Percent   21.9 2.9 4.7 0.9 0.6 0.1 68.8 100

N4 Passive/apathetic social withdrawal (ordinal [1,2,3,4,5,6,7], v4_panss_n4)

v4_panss_n4<-c(v4_clin$v4_panss_n_n4_soz_pass_apath,v4_con$v4_panss_n_n4_soz_pass_apath)
v4_panss_n4<-factor(v4_panss_n4, ordered=T)

descT(v4_panss_n4)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 306  48  78  26  22  4   1059 1543
## [2,] Percent   19.8 3.1 5.1 1.7 1.4 0.3 68.6 100

N5 difficulty in abstract thinking (ordinal [1,2,3,4,5,6,7], v4_panss_n5)

v4_panss_n5<-c(v4_clin$v4_panss_n_n5_abstr_denken,v4_con$v4_panss_n_n5_abstr_denken)
v4_panss_n5<-factor(v4_panss_n5, ordered=T)

descT(v4_panss_n5)
##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 267  71  92 35  13  3   1062 1543
## [2,] Percent   17.3 4.6 6  2.3 0.8 0.2 68.8 100

N6 Lack of spontaneity and flow of conversation (ordinal [1,2,3,4,5,6,7], v4_panss_n6)

v4_panss_n6<-c(v4_clin$v4_panss_n_n6_spon_fl_sprache,v4_con$v4_panss_n_n6_spon_fl_sprache)
v4_panss_n6<-factor(v4_panss_n6, ordered=T)

descT(v4_panss_n6)
##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 375  25  47 23  14  3   1056 1543
## [2,] Percent   24.3 1.6 3  1.5 0.9 0.2 68.4 100

N7 Stereotyped thinking (ordinal [1,2,3,4,5,6,7], v4_panss_n7)

v4_panss_n7<-c(v4_clin$v4_panss_n_n7_stereotyp_ged,v4_con$v4_panss_n_n7_stereotyp_ged)
v4_panss_n7<-factor(v4_panss_n7, ordered=T)

descT(v4_panss_n7)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 382  29  55  18  1   1058 1543
## [2,] Percent   24.8 1.9 3.6 1.2 0.1 68.6 100

PANSS Negative sum score (continuous [7-49], v4_panss_sum_neg)

v4_panss_sum_neg<-as.numeric.factor(v4_panss_n1)+
                  as.numeric.factor(v4_panss_n2)+
                  as.numeric.factor(v4_panss_n3)+
                  as.numeric.factor(v4_panss_n4)+
                  as.numeric.factor(v4_panss_n5)+
                  as.numeric.factor(v4_panss_n6)+
                  as.numeric.factor(v4_panss_n7)

summary(v4_panss_sum_neg)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     7.0     7.0    10.0    12.1    15.0    34.0    1065

General psychopathology subscale

G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v4_panss_g1)

v4_panss_g1<-c(v4_clin$v4_panss_g_g1_sorge_gesundh,v4_con$v4_panss_g_g1_sorge_gesundh)
v4_panss_g1<-factor(v4_panss_g1, ordered=T)

descT(v4_panss_g1)
##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 336  58  61 20  5   1   1062 1543
## [2,] Percent   21.8 3.8 4  1.3 0.3 0.1 68.8 100

G2 Anxiety (ordinal [1,2,3,4,5,6,7], v4_panss_g2)

v4_panss_g2<-c(v4_clin$v4_panss_g_g2_angst,v4_con$v4_panss_g_g2_angst)
v4_panss_g2<-factor(v4_panss_g2, ordered=T)

descT(v4_panss_g2)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 289  49  97  27  21  1060 1543
## [2,] Percent   18.7 3.2 6.3 1.7 1.4 68.7 100

G3 Guilt feelings (ordinal [1,2,3,4,5,6,7], v4_panss_g3)

v4_panss_g3<-c(v4_clin$v4_panss_g_g3_schuldgefuehle,v4_con$v4_panss_g_g3_schuldgefuehle)
v4_panss_g3<-factor(v4_panss_g3, ordered=T)

descT(v4_panss_g3)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 357  32  56  29  8   1061 1543
## [2,] Percent   23.1 2.1 3.6 1.9 0.5 68.8 100

G4 Tension (ordinal [1,2,3,4,5,6,7], v4_panss_g4)

v4_panss_g4<-c(v4_clin$v4_panss_g_g4_anspannung,v4_con$v4_panss_g_g4_anspannung)
v4_panss_g4<-factor(v4_panss_g4, ordered=T)

descT(v4_panss_g4)
##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 323  47 84  22  6   3   1058 1543
## [2,] Percent   20.9 3  5.4 1.4 0.4 0.2 68.6 100

G5 Mannerisms & posturing (ordinal [1,2,3,4,5,6,7], v4_panss_g5)

v4_panss_g5<-c(v4_clin$v4_panss_g_g5_manier_koerperh,v4_con$v4_panss_g_g5_manier_koerperh)
v4_panss_g5<-factor(v4_panss_g5, ordered=T)

descT(v4_panss_g5)
##                1    2   3   4   6   <NA>     
## [1,] No. cases 437  17  23  5   1   1060 1543
## [2,] Percent   28.3 1.1 1.5 0.3 0.1 68.7 100

G6 Depression (ordinal [1,2,3,4,5,6,7], v4_panss_g6)

v4_panss_g6<-c(v4_clin$v4_panss_g_g6_depression,v4_con$v4_panss_g_g6_depression)
v4_panss_g6<-factor(v4_panss_g6, ordered=T)

descT(v4_panss_g6)
##                1    2   3   4   5   6   7   <NA>     
## [1,] No. cases 279  38  85  45  32  4   1   1059 1543
## [2,] Percent   18.1 2.5 5.5 2.9 2.1 0.3 0.1 68.6 100

G7 Motor retardation (ordinal [1,2,3,4,5,6,7], v4_panss_g7)

v4_panss_g7<-c(v4_clin$v4_panss_g_g7_mot_verlangs,v4_con$v4_panss_g_g7_mot_verlangs)
v4_panss_g7<-factor(v4_panss_g7, ordered=T)

descT(v4_panss_g7)
##                1    2   3   4  5   <NA>     
## [1,] No. cases 331  41  76  31 5   1059 1543
## [2,] Percent   21.5 2.7 4.9 2  0.3 68.6 100

G8 Uncooperativeness (ordinal [1,2,3,4,5,6,7], v4_panss_g8)

v4_panss_g8<-c(v4_clin$v4_panss_g_g8_unkoop_verh,v4_con$v4_panss_g_g8_unkoop_verh)
v4_panss_g8<-factor(v4_panss_g8, ordered=T)

descT(v4_panss_g8)
##                1    2  3   5   <NA>     
## [1,] No. cases 452  16 14  2   1059 1543
## [2,] Percent   29.3 1  0.9 0.1 68.6 100

G9 Unusual thought content (ordinal [1,2,3,4,5,6,7], v4_panss_g9)

v4_panss_g9<-c(v4_clin$v4_panss_g_g9_ungew_denkinh,v4_con$v4_panss_g_g9_ungew_denkinh)
v4_panss_g9<-factor(v4_panss_g9, ordered=T)

descT(v4_panss_g9)
##                1    2   3   4  5   6   <NA>     
## [1,] No. cases 381  27  54  15 6   1   1059 1543
## [2,] Percent   24.7 1.7 3.5 1  0.4 0.1 68.6 100

G10 Disorientation (ordinal [1,2,3,4,5,6,7], v4_panss_g10)

v4_panss_g10<-c(v4_clin$v4_panss_g_g10_desorient,v4_con$v4_panss_g_g10_desorient)
v4_panss_g10<-factor(v4_panss_g10, ordered=T)

descT(v4_panss_g10)
##                1    2   3   <NA>     
## [1,] No. cases 458  19  8   1058 1543
## [2,] Percent   29.7 1.2 0.5 68.6 100

G11 Poor attention (ordinal [1,2,3,4,5,6,7], v4_panss_g11)

v4_panss_g11<-c(v4_clin$v4_panss_g_g11_mang_aufmerks,v4_con$v4_panss_g_g11_mang_aufmerks)
v4_panss_g11<-factor(v4_panss_g11, ordered=T)

descT(v4_panss_g11)
##                1    2  3   4   5   6   <NA>     
## [1,] No. cases 295  46 105 29  1   1   1066 1543
## [2,] Percent   19.1 3  6.8 1.9 0.1 0.1 69.1 100

G12 Lack of judgement & insight (ordinal [1,2,3,4,5,6,7], v4_panss_g12)

v4_panss_g12<-c(v4_clin$v4_panss_g_g12_mang_urt_einsi,v4_con$v4_panss_g_g12_mang_urt_einsi)

v4_panss_g12<-factor(v4_panss_g12, ordered=T)
descT(v4_panss_g12)
##                1    2   3   4   5   7   <NA>     
## [1,] No. cases 406  34  18  19  5   2   1059 1543
## [2,] Percent   26.3 2.2 1.2 1.2 0.3 0.1 68.6 100

G13 Disturbance of volition (ordinal [1,2,3,4,5,6,7], v4_panss_g13)

v4_panss_g13<-c(v4_clin$v4_panss_g_g13_willensschwae,v4_con$v4_panss_g_g13_willensschwae)
v4_panss_g13<-factor(v4_panss_g13, ordered=T)

descT(v4_panss_g13)
##                1    2   3  4   5   6   <NA>     
## [1,] No. cases 405  21  46 9   2   1   1059 1543
## [2,] Percent   26.2 1.4 3  0.6 0.1 0.1 68.6 100

G14 Poor impulse control (ordinal [1,2,3,4,5,6,7], v4_panss_g14)

v4_panss_g14<-c(v4_clin$v4_panss_g_g14_mang_impulsk,v4_con$v4_panss_g_g14_mang_impulsk)
v4_panss_g14<-factor(v4_panss_g14, ordered=T)

descT(v4_panss_g14)
##                1    2   3   4   6   <NA>     
## [1,] No. cases 413  24  40  3   2   1061 1543
## [2,] Percent   26.8 1.6 2.6 0.2 0.1 68.8 100

G15 Preoccupation (ordinal [1,2,3,4,5,6,7], v4_panss_g15)

v4_panss_g15<-c(v4_clin$v4_panss_g_g15_selbstbezog,v4_con$v4_panss_g_g15_selbstbezog)
v4_panss_g15<-factor(v4_panss_g15, ordered=T)

descT(v4_panss_g15)
##                1    2   3   4   5   <NA>     
## [1,] No. cases 423  25  28  4   2   1061 1543
## [2,] Percent   27.4 1.6 1.8 0.3 0.1 68.8 100

G16 Active social avoidance (ordinal [1,2,3,4,5,6,7], v4_panss_g16)

v4_panss_g16<-c(v4_clin$v4_panss_g_g16_aktsoz_vermeid,v4_con$v4_panss_g_g16_aktsoz_vermeid)
v4_panss_g16<-factor(v4_panss_g16, ordered=T)

descT(v4_panss_g16)
##                1    2   3   4   5   6   <NA>     
## [1,] No. cases 360  32  59  19  12  1   1060 1543
## [2,] Percent   23.3 2.1 3.8 1.2 0.8 0.1 68.7 100

PANSS General Psychopathology sum score (continuous [16-112], v4_panss_sum_gen)

v4_panss_sum_gen<-as.numeric.factor(v4_panss_g1)+
                  as.numeric.factor(v4_panss_g2)+
                  as.numeric.factor(v4_panss_g3)+
                  as.numeric.factor(v4_panss_g4)+
                  as.numeric.factor(v4_panss_g5)+
                  as.numeric.factor(v4_panss_g6)+
                  as.numeric.factor(v4_panss_g7)+
                  as.numeric.factor(v4_panss_g8)+
                  as.numeric.factor(v4_panss_g9)+
                  as.numeric.factor(v4_panss_g10)+
                  as.numeric.factor(v4_panss_g11)+
                  as.numeric.factor(v4_panss_g12)+
                  as.numeric.factor(v4_panss_g13)+
                  as.numeric.factor(v4_panss_g14)+
                  as.numeric.factor(v4_panss_g15)+
                  as.numeric.factor(v4_panss_g16)

summary(v4_panss_sum_gen)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    16.0    18.0    21.0    23.3    27.0    50.0    1083

Create PANSS Total score (continuous [30-210], v4_panss_sum_tot)

v4_panss_sum_tot<-v4_panss_sum_pos+v4_panss_sum_neg+v4_panss_sum_gen
summary(v4_panss_sum_tot)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   30.00   34.00   40.00   44.82   51.00  100.00    1087

Create dataset

v4_symp_panss<-data.frame(v4_panss_p1,v4_panss_p2,v4_panss_p3,v4_panss_p4,v4_panss_p5,v4_panss_p6,v4_panss_p7,
                          v4_panss_n1,v4_panss_n2,v4_panss_n3,v4_panss_n4,v4_panss_n5,v4_panss_n6,v4_panss_n7,
                          v4_panss_g1,v4_panss_g2,v4_panss_g3,v4_panss_g4,v4_panss_g5,v4_panss_g6,v4_panss_g7,
                          v4_panss_g8,v4_panss_g9,v4_panss_g10,v4_panss_g11,v4_panss_g12,v4_panss_g13,v4_panss_g14,
                          v4_panss_g15,v4_panss_g16,v4_panss_sum_pos,v4_panss_sum_neg,v4_panss_sum_gen,
                          v4_panss_sum_tot)

IDS-C30

For more information on the scale, please see Visit 1

Item 1 Sleep onset insomnia (ordinal [0,1,2,3], v4_idsc_itm1)

v4_idsc_itm1<-c(v4_clin$v4_ids_c_s1_ids1_einschlafschw,v4_con$v4_ids_c_s1_ids1_einschlafschw)
v4_idsc_itm1<-factor(v4_idsc_itm1, ordered=T)

descT(v4_idsc_itm1)
##                0    1   2   3   <NA>     
## [1,] No. cases 343  56  48  36  1060 1543
## [2,] Percent   22.2 3.6 3.1 2.3 68.7 100

Item 2 Mid-nocturnal insomnia (ordinal [0,1,2,3], v4_idsc_itm2)

v4_idsc_itm2<-c(v4_clin$v4_ids_c_s1_ids2_naechtl_aufw,v4_con$v4_ids_c_s1_ids2_naechtl_aufw)
v4_idsc_itm2<-factor(v4_idsc_itm2, ordered=T)

descT(v4_idsc_itm2)
##                0    1   2   3   <NA>     
## [1,] No. cases 298  72  74  39  1060 1543
## [2,] Percent   19.3 4.7 4.8 2.5 68.7 100

Item 3 Early morning insomnia (ordinal [0,1,2,3], v4_idsc_itm3)

v4_idsc_itm3<-c(v4_clin$v4_ids_c_s1_ids3_frueh_aufw,v4_con$v4_ids_c_s1_ids3_frueh_aufw)
v4_idsc_itm3<-factor(v4_idsc_itm3, ordered=T)

descT(v4_idsc_itm3)
##                0    1   2  3   <NA>     
## [1,] No. cases 388  38  31 25  1061 1543
## [2,] Percent   25.1 2.5 2  1.6 68.8 100

Item 4 Hypersomnia (ordinal [0,1,2,3], v4_idsc_itm4)

v4_idsc_itm4<-c(v4_clin$v4_ids_c_s1_ids4_hypersomnie,v4_con$v4_ids_c_s1_ids4_hypersomnie)
v4_idsc_itm4<-factor(v4_idsc_itm4, ordered=T)

descT(v4_idsc_itm4)
##                0    1   2   3  <NA>     
## [1,] No. cases 287  134 48  15 1059 1543
## [2,] Percent   18.6 8.7 3.1 1  68.6 100

Item 5 Mood (sad) (ordinal [0,1,2,3], v4_idsc_itm5)

v4_idsc_itm5<-c(v4_clin$v4_ids_c_s1_ids5_stimmung_trgk,v4_con$v4_ids_c_s1_ids5_stimmung_trgk)
v4_idsc_itm5<-factor(v4_idsc_itm5, ordered=T)

descT(v4_idsc_itm5)
##                0    1   2   3   <NA>     
## [1,] No. cases 296  121 50  17  1059 1543
## [2,] Percent   19.2 7.8 3.2 1.1 68.6 100

Item 6 Mood (irritable) (ordinal [0,1,2,3], v4_idsc_itm6)

v4_idsc_itm6<-c(v4_clin$v4_ids_c_s1_ids6_stimmung_grzt,v4_con$v4_ids_c_s1_ids6_stimmung_grzt)
v4_idsc_itm6<-factor(v4_idsc_itm6, ordered=T)

descT(v4_idsc_itm6)
##                0    1   2   3   <NA>     
## [1,] No. cases 361  85  28  8   1061 1543
## [2,] Percent   23.4 5.5 1.8 0.5 68.8 100

Item 7 Mood (anxious) (ordinal [0,1,2,3], v4_idsc_itm7)

v4_idsc_itm7<-c(v4_clin$v4_ids_c_s1_ids7_stimmung_agst,v4_con$v4_ids_c_s1_ids7_stimmung_agst)
v4_idsc_itm7<-factor(v4_idsc_itm7, ordered=T)

descT(v4_idsc_itm7)
##                0    1   2   3   <NA>     
## [1,] No. cases 313  112 38  19  1061 1543
## [2,] Percent   20.3 7.3 2.5 1.2 68.8 100

Item 8 Reactivity of mood (ordinal [0,1,2,3], v4_idsc_itm8)

v4_idsc_itm8<-c(v4_clin$v4_ids_c_s1_ids8_reakt_stimmung,v4_con$v4_ids_c_s1_ids8_reakt_stimmung)
v4_idsc_itm8<-factor(v4_idsc_itm8, ordered=T)

descT(v4_idsc_itm8)
##                0    1   2   3   <NA>     
## [1,] No. cases 388  65  24  6   1060 1543
## [2,] Percent   25.1 4.2 1.6 0.4 68.7 100

Item 9 Mood Variation (ordinal [0,1,2,3], v4_idsc_itm9)

v4_idsc_itm9<-c(v4_clin$v4_ids_c_s1_ids9_stimmungsschw,v4_con$v4_ids_c_s1_ids9_stimmungsschw)
v4_idsc_itm9<-factor(v4_idsc_itm9, ordered=T)

descT(v4_idsc_itm9)
##                0    1   2   3  <NA>     
## [1,] No. cases 377  37  21  46 1062 1543
## [2,] Percent   24.4 2.4 1.4 3  68.8 100

Item 9A (categorical [M, A, N], v4_idsc_itm9a)
Additional information if the answer on item 9 was 1,2 or 3: “When was the mood usually worse?” (“M”-morning, “A”-afternoon, “N”-night).

v4_idsc_itm9a_pre<-c(v4_clin$v4_ids_c_s1_ids9a_stimmungsschw,v4_con$v4_ids_c_s1_ids9a_stimmungsschw)

v4_idsc_itm9a<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==1, "M", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==2, "A", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9a_pre==3, "N", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9a))
v4_idsc_itm9a<-factor(v4_idsc_itm9a, ordered=F)

descT(v4_idsc_itm9a)
##                -999 A   M   N   <NA>     
## [1,] No. cases 377  6   64  14  1082 1543
## [2,] Percent   24.4 0.4 4.1 0.9 70.1 100

Item 9B (dichotomous, v4_idsc_itm9b) Additional information if the answer on item 9 was 1,2 or 3: “Is mood variation attributed to environment by the patient?”.

v4_idsc_itm9b_pre<-c(v4_clin$v4_ids_c_s1_ids9b_stimmungsschw,v4_con$v4_ids_c_s1_ids9b_stimmungsschw)

v4_idsc_itm9b<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm9b<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9b_pre==0, "N", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9b))
v4_idsc_itm9b<-ifelse(v4_idsc_itm9!=0 & v4_idsc_itm9b_pre==1, "Y", ifelse(v4_idsc_itm9==0, -999, v4_idsc_itm9b))
v4_idsc_itm9b<-factor(v4_idsc_itm9b, ordered=F)

descT(v4_idsc_itm9b)
##                -999 N   Y   <NA>     
## [1,] No. cases 377  26  36  1104 1543
## [2,] Percent   24.4 1.7 2.3 71.5 100

Item 10 Quality of mood (ordinal [0,1,2,3], v4_idsc_itm10)

v4_idsc_itm10<-c(v4_clin$v4_ids_c_s1_ids10_quali_stimmung,v4_con$v4_ids_c_s1_ids10_quali_stimmung)
v4_idsc_itm10<-factor(v4_idsc_itm10, ordered=T)

descT(v4_idsc_itm10)
##                0    1   2   3   <NA>     
## [1,] No. cases 419  32  14  9   1069 1543
## [2,] Percent   27.2 2.1 0.9 0.6 69.3 100

Items 11-14 Appetite and weight
Please not that item 11 assesses decreased appetite and item 13 assesses weight loss during the past two weeks. Item 12 assesses increased appetite and item 14 weight gain during the past two weeks.

The interviewer is supposed to rate either items 11 and 13 or items 12 and 14.

Item 11 (ordinal [0,1,2,3], v4_idsc_itm11)

v4_idsc_app_verm<-c(v4_clin$v4_ids_c_s2_ids11_appetit_verm,v4_con$v4_ids_c_s2_ids11_appetit_verm)
v4_idsc_app_gest<-c(v4_clin$v4_ids_c_s2_ids12_appetit_steig,v4_con$v4_ids_c_s2_ids12_appetit_steig)

v4_idsc_itm11<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm11<-ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==F, -999,                
                  ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==T,          
                         v4_idsc_app_verm, 
                      ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &    
                     (v4_idsc_app_verm>v4_idsc_app_gest), v4_idsc_app_verm,                                            ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &                                                         (v4_idsc_app_gest>=v4_idsc_app_verm),-999,v4_idsc_itm11)))))

#Important: do not code as factor, see after calculation of sum score!
descT(v4_idsc_itm11)
##                -999 0    1   2   3   <NA>     
## [1,] No. cases 161  268  38  13  3   1060 1543
## [2,] Percent   10.4 17.4 2.5 0.8 0.2 68.7 100

Item 12 (ordinal [0,1,2,3], v4_idsc_itm12)

v4_idsc_itm12<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm12<-ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==T, NA, 
                  ifelse(is.na(v4_idsc_app_verm)==T & is.na(v4_idsc_app_gest)==F,    
                         v4_idsc_app_gest,                
                  ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==T,          
                         -999, 
                      ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &    
                     (v4_idsc_app_verm>v4_idsc_app_gest), -999,                                            
                     ifelse(is.na(v4_idsc_app_verm)==F & is.na(v4_idsc_app_gest)==F &                                                         (v4_idsc_app_gest>=v4_idsc_app_verm),
                            v4_idsc_app_gest,v4_idsc_itm12)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v4_idsc_itm12)
##                -999 0  1   2   3  <NA>     
## [1,] No. cases 322  77 51  17  16 1060 1543
## [2,] Percent   20.9 5  3.3 1.1 1  68.7 100

Item 13 (ordinal [0,1,2,3], v4_idsc_itm13)

v4_idsc_gew_abn<-c(v4_clin$v4_ids_c_s2_ids13_gewichtsabn,v4_con$v4_ids_c_s2_ids13_gewichtsabn)
v4_idsc_gew_zun<-c(v4_clin$v4_ids_c_s2_ids14_gewichtszun,v4_con$v4_ids_c_s2_ids14_gewichtszun)

v4_idsc_itm13<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm13<-ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==F, -999,                
                  ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==T,          
                         v4_idsc_gew_abn, 
                      ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &    
                     (v4_idsc_gew_abn>v4_idsc_gew_zun), v4_idsc_gew_abn,                                            ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F & (v4_idsc_gew_zun >= v4_idsc_gew_abn),-999,v4_idsc_itm13)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v4_idsc_itm13)
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 175  247 28  20  13  1060 1543
## [2,] Percent   11.3 16  1.8 1.3 0.8 68.7 100

Item 14 (ordinal [0,1,2,3], v4_idsc_itm14)

v4_idsc_itm14<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_idsc_itm14<-ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==T, NA, 
                  ifelse(is.na(v4_idsc_gew_abn)==T & is.na(v4_idsc_gew_zun)==F,    
                         v4_idsc_gew_zun,                
                  ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==T,          
                         -999, 
                      ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &    
                     (v4_idsc_gew_abn>v4_idsc_gew_zun), -999,                                            
                     ifelse(is.na(v4_idsc_gew_abn)==F & is.na(v4_idsc_gew_zun)==F &                                                         (v4_idsc_gew_zun>=v4_idsc_gew_abn),
                            v4_idsc_gew_zun,v4_idsc_itm14)))))
#Important: do not code as factor, see after calculation of sum score!

descT(v4_idsc_itm14)
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 308  99  37  22  17  1060 1543
## [2,] Percent   20   6.4 2.4 1.4 1.1 68.7 100

Item 15 Concentration/decision making (ordinal [0,1,2,3], v4_idsc_itm15)

v4_idsc_itm15<-c(v4_clin$v4_ids_c_s2_ids15_konz_entscheid,v4_con$v4_ids_c_s2_ids15_konz_entscheid)
v4_idsc_itm15<-factor(v4_idsc_itm15, ordered=T)

descT(v4_idsc_itm15)
##                0    1   2  3   <NA>     
## [1,] No. cases 236  149 77 19  1062 1543
## [2,] Percent   15.3 9.7 5  1.2 68.8 100

Item 16 Outlook (self) (ordinal [0,1,2,3], v4_idsc_itm16)

v4_idsc_itm16<-c(v4_clin$v4_ids_c_s2_ids16_selbstbild,v4_con$v4_ids_c_s2_ids16_selbstbild)
v4_idsc_itm16<-factor(v4_idsc_itm16, ordered=T)

descT(v4_idsc_itm16)
##                0    1   2  3   <NA>     
## [1,] No. cases 347  90  16 28  1062 1543
## [2,] Percent   22.5 5.8 1  1.8 68.8 100

Item 17 Outlook (future) (ordinal [0,1,2,3], v4_idsc_itm17)

v4_idsc_itm17<-c(v4_clin$v4_ids_c_s2_ids17_zukunftssicht,v4_con$v4_ids_c_s2_ids17_zukunftssicht)
v4_idsc_itm17<-factor(v4_idsc_itm17, ordered=T)

descT(v4_idsc_itm17)
##                0    1   2   3   <NA>     
## [1,] No. cases 299  141 37  5   1061 1543
## [2,] Percent   19.4 9.1 2.4 0.3 68.8 100

Item 18 Suicidal ideation (ordinal [0,1,2,3], v4_idsc_itm18)

v4_idsc_itm18<-c(v4_clin$v4_ids_c_s2_ids18_selbstmordged,v4_con$v4_ids_c_s2_ids18_selbstmordged)
v4_idsc_itm18<-factor(v4_idsc_itm18, ordered=T)

descT(v4_idsc_itm18)
##                0    1   2   <NA>     
## [1,] No. cases 434  29  20  1060 1543
## [2,] Percent   28.1 1.9 1.3 68.7 100

Item 19 Involvement (ordinal [0,1,2,3], v4_idsc_itm19)

v4_idsc_itm19<-c(v4_clin$v4_ids_c_s2_ids19_interess_aktiv,v4_con$v4_ids_c_s2_ids19_interess_aktiv)
v4_idsc_itm19<-factor(v4_idsc_itm19, ordered=T)

descT(v4_idsc_itm19)
##                0    1   2   3   <NA>     
## [1,] No. cases 369  74  22  17  1061 1543
## [2,] Percent   23.9 4.8 1.4 1.1 68.8 100

Item 20 Energy/fatigability (ordinal [0,1,2,3], v4_idsc_itm20)

v4_idsc_itm20<-c(v4_clin$v4_ids_c_s2_ids20_energ_ermued,v4_con$v4_ids_c_s2_ids20_energ_ermued)
v4_idsc_itm20<-factor(v4_idsc_itm20, ordered=T)

descT(v4_idsc_itm20)
##                0    1   2   3   <NA>     
## [1,] No. cases 302  101 71  9   1060 1543
## [2,] Percent   19.6 6.5 4.6 0.6 68.7 100

Item 21 Pleasure/enjoyment (exclude sexual activities) (ordinal [0,1,2,3], v4_idsc_itm21)

v4_idsc_itm21<-c(v4_clin$v4_ids_c_s3_ids21_vergn_genuss,v4_con$v4_ids_c_s3_ids21_vergn_genuss)
v4_idsc_itm21<-factor(v4_idsc_itm21, ordered=T)

descT(v4_idsc_itm21)
##                0    1   2   3   <NA>     
## [1,] No. cases 384  63  24  11  1061 1543
## [2,] Percent   24.9 4.1 1.6 0.7 68.8 100

Item 22 Sexual interest (ordinal [0,1,2,3], v4_idsc_itm22)

v4_idsc_itm22<-c(v4_clin$v4_ids_c_s3_ids22_sex_interesse,v4_con$v4_ids_c_s3_ids22_sex_interesse)
v4_idsc_itm22<-factor(v4_idsc_itm22, ordered=T)

descT(v4_idsc_itm22)
##                0    1   2   3   <NA>     
## [1,] No. cases 341  35  57  44  1066 1543
## [2,] Percent   22.1 2.3 3.7 2.9 69.1 100

Item 23 Psychomotor slowing (ordinal [0,1,2,3], v4_idsc_itm23)

v4_idsc_itm23<-c(v4_clin$v4_ids_c_s3_ids23_psymo_hemm,v4_con$v4_ids_c_s3_ids23_psymo_hemm)
v4_idsc_itm23<-factor(v4_idsc_itm23, ordered=T)

descT(v4_idsc_itm23)
##                0    1   2   3   <NA>     
## [1,] No. cases 358  99  21  4   1061 1543
## [2,] Percent   23.2 6.4 1.4 0.3 68.8 100

Item 24 Psychomotor agitation (ordinal [0,1,2,3], v4_idsc_itm24)

v4_idsc_itm24<-c(v4_clin$v4_ids_c_s3_ids24_psymo_agitht,v4_con$v4_ids_c_s3_ids24_psymo_agitht)
v4_idsc_itm24<-factor(v4_idsc_itm24, ordered=T)

descT(v4_idsc_itm24)
##                0    1   2   3   <NA>     
## [1,] No. cases 382  76  18  2   1065 1543
## [2,] Percent   24.8 4.9 1.2 0.1 69   100

Item 25 Somatic complaints (ordinal [0,1,2,3], v4_idsc_itm25)

v4_idsc_itm25<-c(v4_clin$v4_ids_c_s3_ids25_som_beschw,v4_con$v4_ids_c_s3_ids25_som_beschw)
v4_idsc_itm25<-factor(v4_idsc_itm25, ordered=T)

descT(v4_idsc_itm25)
##                0    1   2   3   <NA>     
## [1,] No. cases 326  117 26  12  1062 1543
## [2,] Percent   21.1 7.6 1.7 0.8 68.8 100

Item 26 Sympathetic arousal (ordinal [0,1,2,3], v4_idsc_itm26)

v4_idsc_itm26<-c(v4_clin$v4_ids_c_s3_ids26_veg_erreg,v4_con$v4_ids_c_s3_ids26_veg_erreg)
v4_idsc_itm26<-factor(v4_idsc_itm26, ordered=T)

descT(v4_idsc_itm26)
##                0    1   2   3   <NA>     
## [1,] No. cases 352  104 17  8   1062 1543
## [2,] Percent   22.8 6.7 1.1 0.5 68.8 100

Item 27 Panic/phobic symptoms (ordinal [0,1,2,3], v4_idsc_itm27)

v4_idsc_itm27<-c(v4_clin$v4_ids_c_s3_ids27_panik_phob,v4_con$v4_ids_c_s3_ids27_panik_phob)
v4_idsc_itm27<-factor(v4_idsc_itm27, ordered=T)

descT(v4_idsc_itm27)
##                0    1   2  3   <NA>     
## [1,] No. cases 420  41  15 7   1060 1543
## [2,] Percent   27.2 2.7 1  0.5 68.7 100

Item 28 Gastrointestinal (ordinal [0,1,2,3], v4_idsc_itm28)

v4_idsc_itm28<-c(v4_clin$v4_ids_c_s3_ids28_verdauung,v4_con$v4_ids_c_s3_ids28_verdauung)
v4_idsc_itm28<-factor(v4_idsc_itm28, ordered=T)

descT(v4_idsc_itm28)
##                0    1   2   3   <NA>     
## [1,] No. cases 408  44  23  8   1060 1543
## [2,] Percent   26.4 2.9 1.5 0.5 68.7 100

Item 29 Interpersonal sensitivity (ordinal [0,1,2,3], v4_idsc_itm29)

v4_idsc_itm29<-c(v4_clin$v4_ids_c_s3_ids29_pers_bezieh,v4_con$v4_ids_c_s3_ids29_pers_bezieh)
v4_idsc_itm29<-factor(v4_idsc_itm29, ordered=T)

descT(v4_idsc_itm29)
##                0    1   2   3   <NA>     
## [1,] No. cases 382  55  30  12  1064 1543
## [2,] Percent   24.8 3.6 1.9 0.8 69   100

Item 30 Leaden paralysis/physical energy (ordinal [0,1,2,3], v4_idsc_itm30)

v4_idsc_itm30<-c(v4_clin$v4_ids_c_s3_ids30_schwgf_k_energ,v4_con$v4_ids_c_s3_ids30_schwgf_k_energ)
v4_idsc_itm30<-factor(v4_idsc_itm30, ordered=T)

descT(v4_idsc_itm30)
##                0    1   2   3   <NA>     
## [1,] No. cases 383  63  26  10  1061 1543
## [2,] Percent   24.8 4.1 1.7 0.6 68.8 100

Create IDS-C30 total score (continuous [0-84], v4_idsc_sum) Please note that calculation of the sum score involves selecting either item 11 or item 12 and selecting either item 13 or item 14. If both items are coded, the higher one is taken, according to the official rating instructions.

v4_idsc_sum<-as.numeric.factor(v4_idsc_itm1)+
             as.numeric.factor(v4_idsc_itm2)+
             as.numeric.factor(v4_idsc_itm3)+
             as.numeric.factor(v4_idsc_itm4)+
             as.numeric.factor(v4_idsc_itm5)+
             as.numeric.factor(v4_idsc_itm6)+
             as.numeric.factor(v4_idsc_itm7)+
             as.numeric.factor(v4_idsc_itm8)+
             as.numeric.factor(v4_idsc_itm9)+
             as.numeric.factor(v4_idsc_itm10)+
  
 ifelse(is.na(v4_idsc_itm11)==T & is.na(v4_idsc_itm12)==T, NA, 
        ifelse((v4_idsc_itm11==-999 & v4_idsc_itm12!=-999), v4_idsc_itm12,                
              ifelse((v4_idsc_itm11!=-999 & v4_idsc_itm12==-999),v4_idsc_itm11, NA)))+
  
   ifelse(is.na(v4_idsc_itm13)==T & is.na(v4_idsc_itm14)==T, NA, 
        ifelse((v4_idsc_itm13==-999 & v4_idsc_itm14!=-999), v4_idsc_itm14,                
              ifelse((v4_idsc_itm13!=-999 & v4_idsc_itm14==-999),v4_idsc_itm13, NA)))+
                                                  
             as.numeric.factor(v4_idsc_itm15)+
             as.numeric.factor(v4_idsc_itm16)+
             as.numeric.factor(v4_idsc_itm17)+
             as.numeric.factor(v4_idsc_itm18)+
             as.numeric.factor(v4_idsc_itm19)+
             as.numeric.factor(v4_idsc_itm20)+
             as.numeric.factor(v4_idsc_itm21)+
             as.numeric.factor(v4_idsc_itm22)+
             as.numeric.factor(v4_idsc_itm23)+
             as.numeric.factor(v4_idsc_itm24)+
             as.numeric.factor(v4_idsc_itm25)+
             as.numeric.factor(v4_idsc_itm26)+
             as.numeric.factor(v4_idsc_itm27)+
             as.numeric.factor(v4_idsc_itm28)+
             as.numeric.factor(v4_idsc_itm29)+
             as.numeric.factor(v4_idsc_itm30)

summary(v4_idsc_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0     4.0     8.0    11.3    16.0    55.0    1107

Code itm 11, 12, 13 and 14 as factors (omitted before due to ifelse condition)

v4_idsc_itm11<-factor(v4_idsc_itm11,ordered=T)
v4_idsc_itm12<-factor(v4_idsc_itm12,ordered=T)
v4_idsc_itm13<-factor(v4_idsc_itm13,ordered=T)
v4_idsc_itm14<-factor(v4_idsc_itm14,ordered=T)

Create dataset

v4_symp_ids_c<-data.frame(v4_idsc_itm1,v4_idsc_itm2,v4_idsc_itm3,v4_idsc_itm4,v4_idsc_itm5,v4_idsc_itm6,v4_idsc_itm7,
                          v4_idsc_itm8,v4_idsc_itm9,v4_idsc_itm9a,v4_idsc_itm9b,v4_idsc_itm10,v4_idsc_itm11,v4_idsc_itm12,
                          v4_idsc_itm13,v4_idsc_itm14,v4_idsc_itm15,v4_idsc_itm16,v4_idsc_itm17,v4_idsc_itm18,v4_idsc_itm19,
                          v4_idsc_itm20,v4_idsc_itm21,v4_idsc_itm22,v4_idsc_itm23,v4_idsc_itm24,v4_idsc_itm25,v4_idsc_itm26,
                          v4_idsc_itm27,v4_idsc_itm28,v4_idsc_itm29,v4_idsc_itm30,v4_idsc_sum)

YMRS

For more information on the scale, please see Visit 1

Item 1 Elevated mood (ordinal [0,1,2,3,4], v4_ymrs_itm1)

v4_ymrs_itm1<-c(v4_clin$v4_ymrs_ymrs1_gehob_stimm,v4_con$v4_ymrs_ymrs1_gehob_stimm)
v4_ymrs_itm1<-factor(v4_ymrs_itm1, ordered=T)

descT(v4_ymrs_itm1)
##                0    1   2   <NA>     
## [1,] No. cases 422  48  11  1062 1543
## [2,] Percent   27.3 3.1 0.7 68.8 100

Item 2 Increased motor activity or energy (ordinal [0,1,2,3,4], v4_ymrs_itm2)

v4_ymrs_itm2<-c(v4_clin$v4_ymrs_ymrs2_gest_aktiv,v4_con$v4_ymrs_ymrs2_gest_aktiv)
v4_ymrs_itm2<-factor(v4_ymrs_itm2, ordered=T)

descT(v4_ymrs_itm2)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 427  34  14  3   1   1064 1543
## [2,] Percent   27.7 2.2 0.9 0.2 0.1 69   100

Item 3 Sexual interest (ordinal [0,1,2,3,4], v4_ymrs_itm3)

v4_ymrs_itm3<-c(v4_clin$v4_ymrs_ymrs3_sex_interesse,v4_con$v4_ymrs_ymrs3_sex_interesse)
v4_ymrs_itm3<-factor(v4_ymrs_itm3, ordered=T)

descT(v4_ymrs_itm3)
##                0    1   2   3   <NA>     
## [1,] No. cases 457  10  10  1   1065 1543
## [2,] Percent   29.6 0.6 0.6 0.1 69   100

Item 4 Sleep (ordinal [0,1,2,3,4], v4_ymrs_itm4)

v4_ymrs_itm4<-c(v4_clin$v4_ymrs_ymrs4_schlaf,v4_con$v4_ymrs_ymrs4_schlaf)
v4_ymrs_itm4<-factor(v4_ymrs_itm4, ordered=T)

descT(v4_ymrs_itm4)
##                0    1   2   3   <NA>     
## [1,] No. cases 443  18  12  8   1062 1543
## [2,] Percent   28.7 1.2 0.8 0.5 68.8 100

Item 5 Irritability (ordinal [0,2,4,6,8], v4_ymrs_itm5)

v4_ymrs_itm5<-c(v4_clin$v4_ymrs_ymrs5_reizbarkeit,v4_con$v4_ymrs_ymrs5_reizbarkeit)
v4_ymrs_itm5<-factor(v4_ymrs_itm5, ordered=T)

descT(v4_ymrs_itm5)
##                0    2  4   6   <NA>     
## [1,] No. cases 395  77 8   1   1062 1543
## [2,] Percent   25.6 5  0.5 0.1 68.8 100

Item 6 Speech: rate & amount (ordinal [0,2,4,6,8], v4_ymrs_itm6)

v4_ymrs_itm6<-c(v4_clin$v4_ymrs_ymrs6_sprechweise,v4_con$v4_ymrs_ymrs6_sprechweise)
v4_ymrs_itm6<-factor(v4_ymrs_itm6, ordered=T)

descT(v4_ymrs_itm6)
##                0    2   4   6   8   <NA>     
## [1,] No. cases 410  39  28  3   1   1062 1543
## [2,] Percent   26.6 2.5 1.8 0.2 0.1 68.8 100

Item 7 Language: thought disorder (ordinal [0,1,2,3,4], v4_ymrs_itm7)

v4_ymrs_itm7<-c(v4_clin$v4_ymrs_ymrs7_sprachstoer,v4_con$v4_ymrs_ymrs7_sprachstoer)
v4_ymrs_itm7<-factor(v4_ymrs_itm7, ordered=T)

descT(v4_ymrs_itm7)
##                0   1   2   <NA>     
## [1,] No. cases 432 41  8   1062 1543
## [2,] Percent   28  2.7 0.5 68.8 100

Item 8 Content (ordinal [0,2,4,6,8], v4_ymrs_itm8)

v4_ymrs_itm8<-c(v4_clin$v4_ymrs_ymrs8_inhalte,v4_con$v4_ymrs_ymrs8_inhalte)
v4_ymrs_itm8<-factor(v4_ymrs_itm8, ordered=T)

descT(v4_ymrs_itm8)
##                0    2   4   6   8   <NA>     
## [1,] No. cases 456  8   1   7   9   1062 1543
## [2,] Percent   29.6 0.5 0.1 0.5 0.6 68.8 100

Item 9 Disruptive or aggressive behavior (ordinal [0,2,4,6,8], v4_ymrs_itm9)

v4_ymrs_itm9<-c(v4_clin$v4_ymrs_ymrs9_exp_aggr_verh,v4_con$v4_ymrs_ymrs9_exp_aggr_verh)
v4_ymrs_itm9<-factor(v4_ymrs_itm9, ordered=T)

descT(v4_ymrs_itm9)
##                0    2   6   <NA>     
## [1,] No. cases 465  14  1   1063 1543
## [2,] Percent   30.1 0.9 0.1 68.9 100

Item 10 Appearance (ordinal [0,1,2,3,4], v4_ymrs_itm10)

v4_ymrs_itm10<-c(v4_clin$v4_ymrs_ymrs10_erscheinung,v4_con$v4_ymrs_ymrs10_erscheinung)
v4_ymrs_itm10<-factor(v4_ymrs_itm10, ordered=T)

descT(v4_ymrs_itm10)
##                0    1  2   3   <NA>     
## [1,] No. cases 419  46 12  2   1064 1543
## [2,] Percent   27.2 3  0.8 0.1 69   100

Item 11 Insight (ordinal [0,1,2,3,4], v4_ymrs_itm11)

v4_ymrs_itm11<-c(v4_clin$v4_ymrs_ymrs11_krkh_einsicht,v4_con$v4_ymrs_ymrs11_krkh_einsicht)
v4_ymrs_itm11<-factor(v4_ymrs_itm11, ordered=T)

descT(v4_ymrs_itm11)
##                0    1   2   3   4   <NA>     
## [1,] No. cases 459  11  6   2   2   1063 1543
## [2,] Percent   29.7 0.7 0.4 0.1 0.1 68.9 100

Create YMRS total score (continuous [0-60], v4_ymrs_sum)

v4_ymrs_sum<-(as.numeric.factor(v4_ymrs_itm1)+
        as.numeric.factor(v4_ymrs_itm2)+
        as.numeric.factor(v4_ymrs_itm3)+
        as.numeric.factor(v4_ymrs_itm4)+
        as.numeric.factor(v4_ymrs_itm5)+
        as.numeric.factor(v4_ymrs_itm6)+
        as.numeric.factor(v4_ymrs_itm7)+
        as.numeric.factor(v4_ymrs_itm8)+
        as.numeric.factor(v4_ymrs_itm9)+
        as.numeric.factor(v4_ymrs_itm10)+
        as.numeric.factor(v4_ymrs_itm11))

summary(v4_ymrs_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   2.074   2.000  24.000    1069

Create dataset

v4_symp_ymrs<-data.frame(v4_ymrs_itm1,
                         v4_ymrs_itm2,
                         v4_ymrs_itm3,
                         v4_ymrs_itm4,
                         v4_ymrs_itm5,
                         v4_ymrs_itm6,
                         v4_ymrs_itm7,
                         v4_ymrs_itm8,
                         v4_ymrs_itm9,
                         v4_ymrs_itm10,
                         v4_ymrs_itm11,
                         v4_ymrs_sum)

CGI

Please see Visit 1 for more details and explicit rating instructions.

Illness everity (ordinal [1,2,3,4,5,6,7], v4_cgi_s)

v4_cgi_s<-c(v4_clin$v4_cgi1_cgi1_schweregrad,rep(-999,dim(v4_con)[1]))

v4_cgi_s[v4_cgi_s==0]<- -999
v4_cgi_s<-factor(v4_cgi_s, ordered=T)

descT(v4_cgi_s)
##                -999 1   2   3   4   5   6   7   <NA>     
## [1,] No. cases 321  11  48  155 125 108 29  1   745  1543
## [2,] Percent   20.8 0.7 3.1 10  8.1 7   1.9 0.1 48.3 100

Change since last study visit (ordinal [1,2,3,4,5,6,7], v4_cgi_c)

Here, the interviewer is supposed to comprehensively assess change in illness state since the last study visit. A patient should be rated 0 if not conclusively assessable, so here zero is repaced with “-999” and the remaining seven gradations are on an ordinal scale. These range from “very much improved”-1 to “very much worse”-7.

v4_cgi_c<-c(v4_clin$v4_cgi1_cgi2_gesamt_urteil,rep(-999,dim(v4_con)[1]))

v4_cgi_c[v4_cgi_c==0]<- -999
v4_cgi_c<-factor(v4_cgi_c, ordered=T)

descT(v4_cgi_c)
##                -999 1   2   3   4    5  6  7   <NA>     
## [1,] No. cases 341  6   55  95  187  77 16 2   764  1543
## [2,] Percent   22.1 0.4 3.6 6.2 12.1 5  1  0.1 49.5 100

GAF (continuous [1-100], v4_gaf)

Please see Visit 1 for more details and explicit rating instructions.

v4_gaf<-c(v4_clin$v4_gaf_gaf_code,v4_con$v4_gaf_gaf_code)
v4_gaf[v4_gaf==0]<- -999

summary(v4_gaf[v4_gaf>0])
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   14.00   51.00   60.00   61.69   71.00   99.00    1056
boxplot(v4_gaf[v4_gaf>0 & v1_stat=="CLINICAL"], v4_gaf[v4_gaf>0 & v1_stat=="CONTROL"],ylab="GAF score",ylim=c(0,100),names=c("Clinical","Control"))

Create dataset

v4_ill_sev<-data.frame(v4_cgi_s,v4_cgi_c,v4_gaf)

Visit 4: Neuropsychology (cognitive tests)

There are no differences compared to the test battery assessed in Visit 2 or Visit 3.

General comments on the testing (character, v4_nrpsy_com) If there were no comments, this item was coded -999.

Language proficiency of the participant (ordinal [“mother tongue”,“good”,“sufficient”,“not sufficient”], v4_nrpsy_lng)

v4_nrpsy_lng<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_nrpsy_lng<-ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==0, "mother tongue", 
                     ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==1, "good", 
                            ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==2, "sufficient", 
                              ifelse(c(v4_clin$v4_npu1_np_sprach,v4_con$v4_npu_folge_np_sprach)==3, "not sufficient",v4_nrpsy_lng))))
                            
v4_nrpsy_lng<-factor(v4_nrpsy_lng, ordered=T, levels=c("mother tongue","good",
"sufficient","not sufficient"))

descT(v4_nrpsy_lng)
##                mother tongue good sufficient not sufficient <NA>     
## [1,] No. cases 495           22   4          0              1022 1543
## [2,] Percent   32.1          1.4  0.3        0              66.2 100

Motivation of the participant (ordinal [“poor”,“average”,“good”], v4_nrpsy_mtv)

v4_nrpsy_mtv_pre<-c(v4_clin$v4_npu1_np_mot,v4_con$v4_npu_folge_np_mot)

v4_nrpsy_mtv<-ifelse(v4_nrpsy_mtv_pre==0, "poor", 
                  ifelse(v4_nrpsy_mtv_pre==1, "average", 
                      ifelse(v4_nrpsy_mtv_pre==2, "good", NA)))

v4_nrpsy_mtv<-factor(v4_nrpsy_mtv, ordered=T, levels=c("poor","average","good"))

descT(v4_nrpsy_mtv)
##                poor average good <NA>     
## [1,] No. cases 13   51      451  1028 1543
## [2,] Percent   0.8  3.3     29.2 66.6 100

VLMT

For a description of the test and the variables, see Visit 2.

Re-coding of incomplete VLMT Tests To be able to use the maximum number of tests available, we have now also included the data of incomplete tests (see variable “VLMT_introcheck”). Our expert team has checked every incompletete test and assessed the scores that are usable. Here, we set certain subscores of the VLMT to the appropriate scores.

## [1] 198
## [1] 908
## [1] 246
## [1] 908
## [1] 894
## [1] 912
## [1] 78
## [1] 908
## [1] 909
## [1] 910
## [1] 911
## [1] 912
## [1] 957
##    [1]    1    2    3    4    5    6    7    8    9   10   11   12   13
##   [14]   14   15   16   17   18   19   20   21   22   23   24   25   26
##   [27]   27   28   29   30   31   32   33   34   35   36   37   38   39
##   [40]   40   41   42   43   44   45   46   47   48   49   50   51   52
##   [53]   53   54   55   56   57   58   59   60   61   62   63   64   65
##   [66]   66   67   68   69   70   71   72   73   74   75   76   77   78
##   [79]   79   80   81   82   83   84   85   86   87   88   89   90   91
##   [92]   92   93   94   95   96   97   98   99  100  101  102  103  104
##  [105]  105  106  107  108  109  110  111  112  113  114  115  116  117
##  [118]  118  119  120  121  122  123  124  125  126  127  128  129  130
##  [131]  131  132  133  134  135  136  137  138  139  140  141  142  143
##  [144]  144  145  146  147  148  149  150  151  152  153  154  155  156
##  [157]  157  158  159  160  161  162  163  164  165  166  167  168  169
##  [170]  170  171  172  173  174  175  176  177  178  179  180  181  182
##  [183]  183  184  185  186  187  188  189  190  191  192  193  194  195
##  [196]  196  197  198  199  200  201  202  203  204  205  206  207  208
##  [209]  209  210  211  212  213  214  215  216  217  218  219  220  221
##  [222]  222  223  224  225  226  227  228  229  230  231  232  233  234
##  [235]  235  236  237  238  239  240  241  242  243  244  245  246  247
##  [248]  248  249  250  251  252  253  254  255  256  257  258  259  260
##  [261]  261  262  263  264  265  266  267  268  269  270  271  272  273
##  [274]  274  275  276  277  278  279  280  281  282  283  284  285  286
##  [287]  287  288  289  290  291  292  293  294  295  296  297  298  299
##  [300]  300  301  302  303  304  305  306  307  308  309  310  311  312
##  [313]  313  314  315  316  317  318  319  320  321  322  323  324  325
##  [326]  326  327  328  329  330  331  332  333  334  335  336  337  338
##  [339]  339  340  341  342  343  344  345  346  347  348  349  350  351
##  [352]  352  353  354  355  356  357  358  359  360  361  362  363  364
##  [365]  365  366  367  368  369  370  371  372  373  374  375  376  377
##  [378]  378  379  380  381  382  383  384  385  386  387  388  389  390
##  [391]  391  392  393  394  395  396  397  398  399  400  401  402  403
##  [404]  404  405  406  407  408  409  410  411  412  413  414  415  416
##  [417]  417  418  419  420  421  422  423  424  425  426  427  428  429
##  [430]  430  431  432  433  434  435  436  437  438  439  440  441  442
##  [443]  443  444  445  446  447  448  449  450  451  452  453  454  455
##  [456]  456  457  458  459  460  461  462  463  464  465  466  467  468
##  [469]  469  470  471  472  473  474  475  476  477  478  479  480  481
##  [482]  482  483  484  485  486  487  488  489  490  491  492  493  494
##  [495]  495  496  497  498  499  500  501  502  503  504  505  506  507
##  [508]  508  509  510  511  512  513  514  515  516  517  518  519  520
##  [521]  521  522  523  524  525  526  527  528  529  530  531  532  533
##  [534]  534  535  536  537  538  539  540  541  542  543  544  545  546
##  [547]  547  548  549  550  551  552  553  554  555  556  557  558  559
##  [560]  560  561  562  563  564  565  566  567  568  569  570  571  572
##  [573]  573  574  575  576  577  578  579  580  581  582  583  584  585
##  [586]  586  587  588  589  590  591  592  593  594  595  596  597  598
##  [599]  599  600  601  602  603  604  605  606  607  608  609  610  611
##  [612]  612  613  614  615  616  617  618  619  620  621  622  623  624
##  [625]  625  626  627  628  629  630  631  632  633  634  635  636  637
##  [638]  638  639  640  641  642  643  644  645  646  647  648  649  650
##  [651]  651  652  653  654  655  656  657  658  659  660  661  662  663
##  [664]  664  665  666  667  668  669  670  671  672  673  674  675  676
##  [677]  677  678  679  680  681  682  683  684  685  686  687  688  689
##  [690]  690  691  692  693  694  695  696  697  698  699  700  701  702
##  [703]  703  704  705  706  707  708  709  710  711  712  713  714  715
##  [716]  716  717  718  719  720  721  722  723  724  725  726  727  728
##  [729]  729  730  731  732  733  734  735  736  737  738  739  740  741
##  [742]  742  743  744  745  746  747  748  749  750  751  752  753  754
##  [755]  755  756  757  758  759  760  761  762  763  764  765  766  767
##  [768]  768  769  770  771  772  773  774  775  776  777  778  779  780
##  [781]  781  782  783  784  785  786  787  788  789  790  791  792  793
##  [794]  794  795  796  797  798  799  800  801  802  803  804  805  806
##  [807]  807  808  809  810  811  812  813  814  815  816  817  818  819
##  [820]  820  821  822  823  824  825  826  827  828  829  830  831  832
##  [833]  833  834  835  836  837  838  839  840  841  842  843  844  845
##  [846]  846  847  848  849  850  851  852  853  854  855  856  857  858
##  [859]  859  860  861  862  863  864  865  866  867  868  869  870  871
##  [872]  872  873  874  875  876  877  878  879  880  881  882  883  884
##  [885]  885  886  887  888  889  890  891  892  893  894  895  896  897
##  [898]  898  899  900  901  902  903  904  905  906  907  908  909  910
##  [911]  911  912  913  914  915  916  917  918  919  920  921  922  923
##  [924]  924  925  926  927  928  929  930  931  932  933  934  935  936
##  [937]  937  938  939  940  941  942  943  944  945  946  947  948  949
##  [950]  950  951  952  953  954  955  956  957  958  959  960  961  962
##  [963]  963  964  965  966  967  968  969  970  971  972  973  974  975
##  [976]  976  977  978  979  980  981  982  983  984  985  986  987  988
##  [989]  989  990  991  992  993  994  995  996  997  998  999 1000 1001
## [1002] 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014
## [1015] 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027
## [1028] 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## [1041] 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053
## [1054] 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066
## [1067] 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079
## [1080] 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092
## [1093] 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105
## [1106] 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118
## [1119] 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131
## [1132] 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144
## [1145] 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157
## [1158] 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170
## [1171] 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183
## [1184] 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196
## [1197] 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209
## [1210] 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222
## [1223] 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235
## [1236] 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## [1249] 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261
## [1262] 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274
## [1275] 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287
## [1288] 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300
## [1301] 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313
## [1314] 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326
## [1327] 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339
## [1340] 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352
## [1353] 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365
## [1366] 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378
## [1379] 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391
## [1392] 1392 1393
## [1] 908
## [1] 909
## [1] 910
## [1] 911
## [1] 912
## [1] 745
## [1] 911
## [1] 456
## [1] 912
## [1] 248
## [1] 908
## [1] 691
## [1] 910
## [1] 254
## [1] 908
## [1] 663
## [1] 910

VLMT_introcheck (categorical [0, 1, 9], v4_nrpsy_vlmt_check)

v4_nrpsy_vlmt_check<-c(v4_clin$v4_vlmt_vlmt_introcheck1,v4_con$v4_npu_folge_np_vlmt)
descT(v4_nrpsy_vlmt_check)
##                0   1    9   <NA>     
## [1,] No. cases 42  483  18  1000 1543
## [2,] Percent   2.7 31.3 1.2 64.8 100

Sum of correctly recalled words across all five presentations of list 1 (continuous [number of words], v4_nrpsy_vlmt_corr)

v4_nrpsy_vlmt_corr<-c(v4_clin$v4_vlmt_vlmt3_sw_a5d,v4_con$v4_npu_folge_np_vlmt_gl)
summary(v4_nrpsy_vlmt_corr)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    9.00   39.00   49.00   48.32   58.00   76.00    1046

Loss of recalled words (compared to recall after last presentation of list 1) from list 1 after distraction (presentation and recall of list 2) (continuous [number of words], v4_nrpsy_vlmt_lss_d)

v4_nrpsy_vlmt_lss_d<-c(v4_clin$v4_vlmt_vlmt5_aw_ilsd6,v4_con$v4_npu_folge_np_vlmt_vni)
summary(v4_nrpsy_vlmt_lss_d)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -3.000   0.000   2.000   1.886   3.000   8.000    1052

Loss of recalled words (compared to recall after last presentation of list 1) after time interval (25-30 min.) (continuous [number of words], v4_nrpsy_vlmt_lss_t)

v4_nrpsy_vlmt_lss_t<-c(v4_clin$v4_vlmt_vlmt6_aw_vwd7,v4_con$v4_npu_folge_np_vlmt_vnzv)
summary(v4_nrpsy_vlmt_lss_t)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  -3.000   1.000   2.000   2.101   3.000  14.000    1057

Recognition performance (corrected for falsely recognized words) (continuous [number of words], v4_nrpsy_vlmt_rec)

v4_nrpsy_vlmt_rec<-c(v4_clin$v4_vlmt_vlmt8_kwl,v4_con$v4_npu_folge_np_vlmt_kw)
  summary(v4_nrpsy_vlmt_rec)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   -8.00    9.00   13.00   11.05   14.00   15.00    1059

TMT

For a description of the test, see Visit 1.

TMT Part A, time (continuous [seconds], v4_nrpsy_tmt_A_rt)

v4_nrpsy_tmt_A_rt<-c(v4_clin$v4_npu1_tmt_001,v4_con$v4_npu_folge_np_tmt_001)
summary(v4_nrpsy_tmt_A_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   10.00   21.00   28.00   32.08   39.00  151.00    1024

TMT Part A, errors (continuous [number of errors], v4_nrpsy_tmt_A_err) We did not impose any cut-off value to errors (see Visit 1).

v4_nrpsy_tmt_A_err<-c(v4_clin$v4_npu1_tmt_af_001,v4_con$v4_npu_folge_np_tmtfehler_001)
summary(v4_nrpsy_tmt_A_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.0904  0.0000  3.0000    1023

TMT Part B, time (continuous [seconds], v4_nrpsy_tmt_B_rt) As recommended by Strauss (2006), paricipants with a time >300s were set to 300s. We checked for values <10s, but there were none present.

v4_nrpsy_tmt_B_rt<-c(v4_clin$v4_npu1_tmt_002,v4_con$v4_npu_folge_tmt_002)
v4_nrpsy_tmt_B_rt[v4_nrpsy_tmt_B_rt>300]<-300

summary(v4_nrpsy_tmt_B_rt)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   24.00   50.50   68.00   77.39   90.00  300.00    1052

TMT Part B, errors (continuous [number of errors], v4_nrpsy_tmt_B_err)

v4_nrpsy_tmt_B_err<-c(v4_clin$v4_npu1_tmt_af_002,v4_con$v4_npu_folge_tmt_af_002)
summary(v4_nrpsy_tmt_B_err)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.5295  1.0000  6.0000    1052

Verbal digit span

For a description of the test, see Visit 1.

Forward (continuous [number of items], v4_nrpsy_dgt_sp_frw)

v4_nrpsy_dgt_sp_frw<-c(v4_clin$v4_npu1_zns_001,v4_con$v4_npu_folge_np_wie_001)
summary(v4_nrpsy_dgt_sp_frw)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00    8.00    9.00    9.47   11.00   15.00    1035

Backward (continuous [number of items], v4_nrpsy_dgt_sp_bck)

## [1] 33
## [1] 926
v4_nrpsy_dgt_sp_bck<-c(v4_clin$v4_npu1_zns_002,v4_con$v4_npu_folge_np_wie_002)
summary(v4_nrpsy_dgt_sp_bck)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   5.000   6.000   6.357   8.000  14.000    1036

DST (continuous [number of correct symbols], v4_nrpsy_dg_sym)

For a description of the test, see Visit 1.

v4_introcheck3<-c(v4_clin$v4_npu1_np_introcheck3,v4_con$v4_npu_folge_np_hawier)
v4_nrpsy_dg_sym_pre<-c(v4_clin$v4_npu1_zst_001,v4_con$v4_npu_folge_np_hawier_001)

v4_nrpsy_dg_sym<-ifelse(v4_introcheck3==1, v4_nrpsy_dg_sym_pre, 
                           ifelse(v4_introcheck3==9,-999,
                                  ifelse(v4_introcheck3==0,NA,NA)))

summary(subset(v4_nrpsy_dg_sym,v4_nrpsy_dg_sym>=0))
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   13.00   49.00   64.00   63.86   78.00  132.00

Create dataset

v4_nrpsy<-data.frame(v4_nrpsy_com,
                     v4_nrpsy_lng,
                     v4_nrpsy_mtv,
                     v4_nrpsy_vlmt_check,
                     v4_nrpsy_vlmt_corr,
                     v4_nrpsy_vlmt_lss_d,
                     v4_nrpsy_vlmt_lss_t,
                     v4_nrpsy_vlmt_rec,
                     v4_nrpsy_tmt_A_rt,
                     v4_nrpsy_tmt_A_err,
                     v4_nrpsy_tmt_B_rt,
                     v4_nrpsy_tmt_B_err,
                     v4_nrpsy_dgt_sp_frw,
                     v4_nrpsy_dgt_sp_bck,
                     v4_nrpsy_dg_sym)

Visit 4: Questionnaires (patient rates her/himself)

Participants were asked to fill out questionnaires on the following topics: current medication adherence (compliance), current depressive symptoms (BDI-II), current manic symptoms (ASRM and MSS), life events in the past six months, and current quality of life (WHOQOL-BREF). Additionally, interviews of clinical participants included questions on whether experienced life events during the past six months are attributed to the development of an illness episode (if any occured in between Visit 3 and 4) and medication adherence (compliance). Control participants additionally completed the Short Form Health Survey (SF-12). As in Visit 1, 2 and 3, all questionnaires are checked on whether they were filled out correctly or not and only those correctly filled-out are included in this dataset.

SF-12

For explanation, please refer to the section in Visit 1

“How satisfied are you currently with your overall life” (ordinal [1,2,3,4,5,6,7,8,9,10], v4_sf12_itm0) Answering alternatives are the following: “Very dissatisfied”-1 to “Completely satisfied”-10.

v4_sf12_recode(v4_con$v4_sf12_sf_allgemein,"v4_sf12_itm0")
##                -999 1   3   4   5   6   7   8   9   10  <NA>     
## [1,] No. cases 1223 1   1   1   2   2   8   17  20  11  257  1543
## [2,] Percent   79.3 0.1 0.1 0.1 0.1 0.1 0.5 1.1 1.3 0.7 16.7 100

“In general, would you say your health is…” (ordinal [1,2,3,4,5], v4_sf12_itm1) Answering alternatives are the following: “Excellent”-1, “Very Good”-2, “Good”-3, “Fair”-4, “Poor”-5.

v4_sf12_recode(v4_con$v4_sf12_sf1,"v4_sf12_itm1")
##                -999 1   2   3   4   <NA>     
## [1,] No. cases 1223 12  20  29  3   256  1543
## [2,] Percent   79.3 0.8 1.3 1.9 0.2 16.6 100

“The following questions are about activities you might do during a typical day. Does YOUR HEALTH NOW LIMIT YOU in these activities? If so, how much?”

“MODERATE ACTIVITIES, such as moving a table, pushing a vacuum cleaner, bowling, or playing golf” (ordinal [1,2,3], v4_sf12_itm2)

Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v4_sf12_recode(v4_con$v4_sf12_sf2,"v4_sf12_itm2")
##                -999 2   3   <NA>     
## [1,] No. cases 1223 5   59  256  1543
## [2,] Percent   79.3 0.3 3.8 16.6 100

“Climbing SEVERAL flights of stairs” (ordinal [1,2,3], v4_sf12_itm3) Answering alternatives are the following: “Yes, Limited A Lot”-1, “Yes, Limited A Little”-2, “No, Not Limited At All”-3.

v4_sf12_recode(v4_con$v4_sf12_sf3,"v4_sf12_itm3")
##                -999 2   3   <NA>     
## [1,] No. cases 1223 6   58  256  1543
## [2,] Percent   79.3 0.4 3.8 16.6 100

During the PAST 4 WEEKS have you had any of the following problems with your work or other regular activities AS A RESULT OF YOUR PHYSICAL HEALTH?

“ACCOMPLISHED LESS than you would like” (dichotomous [1,2], v4_sf12_itm4) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf4,"v4_sf12_itm4")
##                -999 1   2   <NA>     
## [1,] No. cases 1223 8   54  258  1543
## [2,] Percent   79.3 0.5 3.5 16.7 100

“Didn’t do work or other activities as carefully as usual” (dichotomous [1,2], v4_sf12_itm5) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf5,"v4_sf12_itm5")
##                -999 1   2   <NA>     
## [1,] No. cases 1223 2   59  259  1543
## [2,] Percent   79.3 0.1 3.8 16.8 100

During the PAST 4 WEEKS, were you limited in the kind of work you do or other regular activities AS A RESULT OF ANY EMOTIONAL PROBLEMS (such as feeling depressed or anxious)?

“ACCOMPLISHED LESS than you would like:” (dichotomous [1,2], v4_sf12_itm6) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf6,"v4_sf12_itm6")
##                -999 1   2   <NA>     
## [1,] No. cases 1223 8   54  258  1543
## [2,] Percent   79.3 0.5 3.5 16.7 100

“Didn’t do work or other activities as CAREFULLY as usual” (dichotomous [1,2], v4_sf12_itm7) Answering alternatives are the following: “Yes”-1, “No”-2.

v4_sf12_recode(v4_con$v4_sf12_sf7,"v4_sf12_itm7")
##                -999 1   2   <NA>     
## [1,] No. cases 1223 3   59  258  1543
## [2,] Percent   79.3 0.2 3.8 16.7 100

“During the PAST 4 WEEKS, how much did PAIN interfere with your normal work (including both work outside the home and housework)?” (ordinal [1,2,3], v4_sf12_itm8) Answering alternatives are the following: “Not At All”-1, “A Little Bit”-2, “Moderately”-3, “Quite A Bit”-4, “Extremely”-5.

v4_sf12_recode(v4_con$v4_sf12_st8,"v4_sf12_itm8")
##                -999 1   2  3   4   5   <NA>     
## [1,] No. cases 1223 33  15 10  3   1   258  1543
## [2,] Percent   79.3 2.1 1  0.6 0.2 0.1 16.7 100

The next three questions are about how you feel and how things have been DURING THE PAST 4 WEEKS. For each question, please give the one answer that comes closest to the way you have been feeling. How much of the time during the PAST 4 WEEKS

Answering alternatives are the following: “All of the Time”-1, “Most of the Time”-2, “A Good Bit of the Time”-3, “Some of the Time”-4, “A Little of the Time”-5, “None of the Time”-6.

“Have you felt calm and peaceful?” (ordinal [1,2,3,4,5,6], v4_sf12_itm9)

v4_sf12_recode(v4_con$v4_sf12_st9,"v4_sf12_itm9")
##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 1223 11  35  12  2   2   258  1543
## [2,] Percent   79.3 0.7 2.3 0.8 0.1 0.1 16.7 100

“Did you have a lot of energy?” (ordinal [1,2,3,4,5,6], v4_sf12_itm10)

v4_sf12_recode(v4_con$v4_sf12_st10,"v4_sf12_itm10")
##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 1223 2   34  11  13  2   258  1543
## [2,] Percent   79.3 0.1 2.2 0.7 0.8 0.1 16.7 100

“Have you felt downhearted and blue?” (ordinal [1,2,3,4,5,6], v4_sf12_itm11)

v4_sf12_recode(v4_con$v4_sf12_st11,"v4_sf12_itm11")
##                -999 1   2   3   4   5   6   <NA>     
## [1,] No. cases 1223 1   1   2   5   28  25  258  1543
## [2,] Percent   79.3 0.1 0.1 0.1 0.3 1.8 1.6 16.7 100

“During the PAST 4 WEEKS, how much of the time has your PHYSICAL HEALTH OR EMOTIONAL PROBLEMS interfered with your social activities (like visiting with friends, relatives, etc.)?” (ordinal [0,1,2,3], v4_sf12_itm12) Answering alternatives are the following: “All of the Time”-1 to “None of the Time”-5.

ATTENTION: there appears to be something wrong with the coding of this item, i.e. the export of the item and the display in the GUI of the database are inconsistent. NOT CONTAINED IN DATASET FOR NOW

v4_sf12_recode(v4_con$v4_sf12_st12,"v4_sf12_itm12")

Create dataset

v4_sf12<-data.frame(v4_sf12_itm0,
                    v4_sf12_itm1,
                    v4_sf12_itm2,
                    v4_sf12_itm3,
                    v4_sf12_itm4,
                    v4_sf12_itm5,
                    v4_sf12_itm6,
                    v4_sf12_itm7,
                    v4_sf12_itm8,
                    v4_sf12_itm9,
                    v4_sf12_itm10,
                    v4_sf12_itm11)

#INCLUDE v4_sf12_itm12 when issues are settled

Religious beliefs

For a description of the questionnaire, see Visit 1. Controls all have “-999”, as here the questionaire was introduced from the start of data collection.

Religion Christianity (dichotomous, v4_rel_chr)

v4_rel_chris<-c(v4_clin$v4_religion_christ,rep(-999,dim(v4_con)[1]))
v4_rel_chr<-ifelse(v4_rel_chris==1, "Y",ifelse(v4_rel_chris==0,"N",ifelse(v4_rel_chris==-999,"-999",NA)))
descT(v4_rel_chr) 
##                -999 N   Y    <NA>     
## [1,] No. cases 320  13  274  936  1543
## [2,] Percent   20.7 0.8 17.8 60.7 100

Religion Islam (dichotomous, v4_rel_isl)

v4_rel_islam<-c(v4_clin$v4_religion_islam_jn,rep(-999,dim(v4_con)[1]))
v4_rel_isl<-ifelse(v4_rel_islam==1, "Y",ifelse(v4_rel_islam==0,"N",ifelse(v4_rel_islam==-999,"-999",NA)))
descT(v4_rel_isl) 
##                -999 N   Y   <NA>     
## [1,] No. cases 320  57  6   1160 1543
## [2,] Percent   20.7 3.7 0.4 75.2 100

Other religion (categorical,[v4_rel_oth])

v4_rel_var<-c(v4_clin$v4_religion_religion,rep(-999,dim(v4_con)[1]))
v4_rel_oth<-ifelse(v4_rel_var==1, "Judaism",
                   ifelse(v4_rel_var==2, "Hinduism",
                          ifelse(v4_rel_var==3, "Buddhism",
                                 ifelse(v4_rel_var==4, "Other",
                                        ifelse(v4_rel_var==5, "No denomination",
                                              ifelse(v4_rel_var==-999, "-999", NA))))))
descT(v4_rel_oth) 
##                -999 Buddhism Hinduism Judaism No denomination Other <NA>
## [1,] No. cases 320  6        1        1       89              7     1119
## [2,] Percent   20.7 0.4      0.1      0.1     5.8             0.5   72.5
##          
## [1,] 1543
## [2,] 100

“How actively do you practice your belief?” (ordinal [1,2,3,4,5], v4_rel_act) This is an ordinal item with the following answer possibilities and the assigned gadation: “not at all”-1,“little active”-2,“moderately active”-3,“rather active”-4,“very actively”-5.

v4_rel_act<-c(v4_clin$v4_religion_religion_aktiv,rep(-999,dim(v4_con)[1]))
descT(v4_rel_act)
##                -999 1   2   3   4   5   <NA>     
## [1,] No. cases 320  105 133 98  56  20  811  1543
## [2,] Percent   20.7 6.8 8.6 6.4 3.6 1.3 52.6 100

Create dataset

v4_rlgn<-data.frame(v4_rel_chr,v4_rel_isl,v4_rel_oth,v4_rel_act)

Medication adherence (compliance)

For a description of the questionnaire, see Visit 1.

Past seven days (ordinal [1,2,3,4,5,6], v4_med_pst_wk)

v4_med_chk<-c(v4_clin$v4_compl_verwer_fragebogen,rep(1,dim(v4_con)[1]))
v4_med_pst_wk_pre<-c(v4_clin$v4_compl_psychopharm_7_tag,rep(-999,dim(v4_con)[1]))
  
v4_med_pst_wk<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_med_pst_wk<-ifelse((is.na(v4_med_chk) | v4_med_chk!=2), 
                      v4_med_pst_wk_pre, v4_med_pst_wk)

descT(v4_med_pst_wk)
##                -999 1    2   3  4   6   <NA>     
## [1,] No. cases 320  395  38  16 1   4   769  1543
## [2,] Percent   20.7 25.6 2.5 1  0.1 0.3 49.8 100

Past six months (ordinal [1,2,3,4,5,6], v4_med_pst_sx_mths)

v4_med_pre<-c(v4_clin$v4_compl_psychopharm_6_mon,rep(-999,dim(v4_con)[1]))

v4_med_pst_sx_mths<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])
v4_med_pst_sx_mths<-ifelse((is.na(v4_med_chk) | v4_med_chk!=2),
                           v4_med_pre, v4_med_pst_sx_mths)

descT(v4_med_pst_sx_mths)
##                -999 1    2   3   4   6   <NA>     
## [1,] No. cases 320  358  55  32  7   2   769  1543
## [2,] Percent   20.7 23.2 3.6 2.1 0.5 0.1 49.8 100

Create dataset

v4_med_adh<-data.frame(v4_med_pst_wk,v4_med_pst_sx_mths)

BDI-II

For explanation, please refer to the section in Visit 1

1. Sadness (ordinal [0,1,2,3], v4_bdi2_itm1)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi1_traurigkeit,v4_con$v4_bdi2_s1_bdi1,"v4_bdi2_itm1")
##                0   1   2   3   <NA>     
## [1,] No. cases 371 134 10  7   1021 1543
## [2,] Percent   24  8.7 0.6 0.5 66.2 100

2. Pessimism (ordinal [0,1,2,3], v4_bdi2_itm2)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi2_pessimismus,v4_con$v4_bdi2_s1_bdi2,"v4_bdi2_itm2")
##                0    1  2   3   <NA>     
## [1,] No. cases 379  92 42  8   1022 1543
## [2,] Percent   24.6 6  2.7 0.5 66.2 100

3. Past failure (ordinal [0,1,2,3], v4_bdi2_itm3)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi3_versagensgef,v4_con$v4_bdi2_s1_bdi3,"v4_bdi2_itm3")
##                0   1   2   3   <NA>     
## [1,] No. cases 340 108 65  8   1022 1543
## [2,] Percent   22  7   4.2 0.5 66.2 100

4. Loss of pleasure (ordinal [0,1,2,3], v4_bdi2_itm4)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi4_verlust_freude,v4_con$v4_bdi2_s1_bdi4,"v4_bdi2_itm4")
##                0    1   2   3   <NA>     
## [1,] No. cases 315  155 30  19  1024 1543
## [2,] Percent   20.4 10  1.9 1.2 66.4 100

5. Guilty feelings (ordinal [0,1,2,3], v4_bdi2_itm5)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi5_schuldgef,v4_con$v4_bdi2_s1_bdi5,"v4_bdi2_itm5")
##                0    1   2   3   <NA>     
## [1,] No. cases 376  123 14  6   1024 1543
## [2,] Percent   24.4 8   0.9 0.4 66.4 100

6. Punishment feelings (ordinal [0,1,2,3], v4_bdi2_itm6)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi6_bestrafungsgef,v4_con$v4_bdi2_s1_bdi6,"v4_bdi2_itm6")
##                0    1   2   3   <NA>     
## [1,] No. cases 426  70  2   24  1021 1543
## [2,] Percent   27.6 4.5 0.1 1.6 66.2 100

7. Self-dislike (ordinal [0,1,2,3], v4_bdi2_itm7)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi7_selbstablehnung,v4_con$v4_bdi2_s1_bdi7,"v4_bdi2_itm7")
##                0    1   2  3   <NA>     
## [1,] No. cases 403  80  31 8   1021 1543
## [2,] Percent   26.1 5.2 2  0.5 66.2 100

8. Self-criticalness (ordinal [0,1,2,3], v4_bdi2_itm8)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi8_selbstvorwuerfe,v4_con$v4_bdi2_s1_bdi8,"v4_bdi2_itm8")
##                0    1   2   3   <NA>     
## [1,] No. cases 350  132 30  11  1020 1543
## [2,] Percent   22.7 8.6 1.9 0.7 66.1 100

9. Suicidal thoughts or wishes (ordinal [0,1,2,3], v4_bdi2_itm9)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi9_selbstmordged,v4_con$v4_bdi2_s1_bdi9,"v4_bdi2_itm9")
##                0    1   2   3   <NA>     
## [1,] No. cases 433  83  4   3   1020 1543
## [2,] Percent   28.1 5.4 0.3 0.2 66.1 100

10. Crying (ordinal [0,1,2,3], v4_bdi2_itm10)

v4_bdi2_recode(v4_clin$v4_bdi2_s1_bdi10_weinen,v4_con$v4_bdi2_s1_bdi10,"v4_bdi2_itm10")
##                0    1   2   3   <NA>     
## [1,] No. cases 411  56  9   41  1026 1543
## [2,] Percent   26.6 3.6 0.6 2.7 66.5 100

11. Agitation (ordinal [0,1,2,3], v4_bdi2_itm11)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi11_unruhe,v4_con$v4_bdi2_s2_bdi11,"v4_bdi2_itm11")
##                0    1   2   3   <NA>     
## [1,] No. cases 392  104 13  8   1026 1543
## [2,] Percent   25.4 6.7 0.8 0.5 66.5 100

12. Loss of interest (ordinal [0,1,2,3], v4_bdi2_itm12)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi12_interessverl,v4_con$v4_bdi2_s2_bdi12,"v4_bdi2_itm12")
##                0    1   2   3   <NA>     
## [1,] No. cases 353  115 29  21  1025 1543
## [2,] Percent   22.9 7.5 1.9 1.4 66.4 100

13. Indecisiveness (ordinal [0,1,2,3], v4_bdi2_itm13)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi13_entschlussunf,v4_con$v4_bdi2_s2_bdi13,"v4_bdi2_itm13")
##                0    1   2   3   <NA>     
## [1,] No. cases 331  132 34  19  1027 1543
## [2,] Percent   21.5 8.6 2.2 1.2 66.6 100

14. Worthlessness (ordinal [0,1,2,3], v4_bdi2_itm14)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi14_wertlosigkeit,v4_con$v4_bdi2_s2_bdi14,"v4_bdi2_itm14")
##                0    1   2   3   <NA>     
## [1,] No. cases 391  82  37  6   1027 1543
## [2,] Percent   25.3 5.3 2.4 0.4 66.6 100

15. Loss of energy (ordinal [0,1,2,3], v4_bdi2_itm15)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi15_energieverlust,v4_con$v4_bdi2_s2_bdi15,"v4_bdi2_itm15")
##                0    1    2   3   <NA>     
## [1,] No. cases 289  165  54  7   1028 1543
## [2,] Percent   18.7 10.7 3.5 0.5 66.6 100

16. Changes in sleeping pattern (ordinal [0,1,2,3], v4_bdi2_itm16) Here, there are seven answer alternatives: “I have not experienced changes in sleeping patterns”, “I sleep somewhat less than usual”,“I sleep somewhat more than usual”, “I sleep a lot less than usual”, “I sleep a lot more than usual”, “I sleep most of the day”, I wake up 1-2 hours early and can’t get back to sleep“. There is a thus a distinction between sleeping more and sleeping less. We have coded the questionaire so that sleep difficulties (sleeping more or slepping less) receive the same points. The distinction between whether somebody slept more or less is therefore lost.

v4_itm_bdi2_chk<-c(v4_clin$v4_bdi2_s1_verwer_fragebogen,v4_con$v4_bdi2_s1_bdi_korrekt)
v4_itm_bdi2_itm16_clin_con<-c(v4_clin$v4_bdi2_s2_bdi16_schlafgewohn,v4_con$v4_bdi2_s2_bdi16)

v4_bdi2_itm16<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])

v4_bdi2_itm16<-ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & v4_itm_bdi2_itm16_clin_con==0, 0,
                ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                           (v4_itm_bdi2_itm16_clin_con==1 | v4_itm_bdi2_itm16_clin_con==100), 1, 
                 ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm16_clin_con==2 | v4_itm_bdi2_itm16_clin_con==200), 2, 
                  ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm16_clin_con==3 | v4_itm_bdi2_itm16_clin_con==300), 3, v4_bdi2_itm16))))  

v4_bdi2_itm16<-factor(v4_bdi2_itm16,ordered=T)
descT(v4_bdi2_itm16)
##                0    1    2   3   <NA>     
## [1,] No. cases 291  158  40  29  1025 1543
## [2,] Percent   18.9 10.2 2.6 1.9 66.4 100

17. Irritability (ordinal [0,1,2,3], v4_bdi2_itm17)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi17_reizbarkeit,v4_con$v4_bdi2_s2_bdi17,"v4_bdi2_itm17")
##                0    1   2   3   <NA>     
## [1,] No. cases 425  81  8   3   1026 1543
## [2,] Percent   27.5 5.2 0.5 0.2 66.5 100

18. Change in appetite (ordinal [0,1,2,3], v4_bdi2_itm18)
As above (item 16), there are several answer alternatives: “I have not experienced any change in my appetite”, “My appetite is somewhat less than usual”, “My appetite is somewhat more than usual”, “My appetite is much less than before”, “My appetite is much more than before”, “I have no appetite at all”, “I crave food all the time”. More explicity, there is a distinction between more and less appetite. We have coded the questionaire so that changes in appetite receive the same points. The distinction between whether somebody had more or less appetite is therefore lost.

v4_itm_bdi2_itm18_clin_con<-c(v4_clin$v4_bdi2_s2_bdi18_appetit,v4_con$v4_bdi2_s2_bdi18)
v4_bdi2_itm18<-rep(NA,dim(v4_clin)[1]+dim(v4_con)[1])

v4_bdi2_itm18<-ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & v4_itm_bdi2_itm18_clin_con==0, 0,
                ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                           (v4_itm_bdi2_itm18_clin_con==1 | v4_itm_bdi2_itm18_clin_con==100), 1, 
                 ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm18_clin_con==2 | v4_itm_bdi2_itm18_clin_con==200), 2, 
                  ifelse((is.na(v4_itm_bdi2_chk) | v4_itm_bdi2_chk!=2) & 
                               (v4_itm_bdi2_itm18_clin_con==3 | v4_itm_bdi2_itm18_clin_con==300), 3, v4_bdi2_itm18))))  

v4_bdi2_itm18<-factor(v4_bdi2_itm18,ordered=T)
descT(v4_bdi2_itm18)
##                0    1   2   3  <NA>     
## [1,] No. cases 341  136 24  15 1027 1543
## [2,] Percent   22.1 8.8 1.6 1  66.6 100

19. Concentration difficulty (ordinal [0,1,2,3], v4_bdi2_itm19)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi19_konzschw,v4_con$v4_bdi2_s2_bdi19,"v4_bdi2_itm19")
##                0    1   2   3   <NA>     
## [1,] No. cases 294  152 65  6   1026 1543
## [2,] Percent   19.1 9.9 4.2 0.4 66.5 100

20. Tiredness or fatigue (ordinal [0,1,2,3], v4_bdi2_itm20)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi20_ermued_ersch,v4_con$v4_bdi2_s2_bdi20,"v4_bdi2_itm20")
##                0    1    2   3   <NA>     
## [1,] No. cases 292  178  36  10  1027 1543
## [2,] Percent   18.9 11.5 2.3 0.6 66.6 100

21. Loss of interest in sex (ordinal [0,1,2,3], v4_bdi2_itm21)

v4_bdi2_recode(v4_clin$v4_bdi2_s2_bdi21_sex_interess,v4_con$v4_bdi2_s2_bdi21,"v4_bdi2_itm21")
##                0   1   2   3  <NA>     
## [1,] No. cases 340 88  40  46 1029 1543
## [2,] Percent   22  5.7 2.6 3  66.7 100

BDI-II sum score calculation (continuous [0-63], v4_bdi2_sum)

v4_bdi2_sum<-as.numeric.factor(v4_bdi2_itm1)+
              as.numeric.factor(v4_bdi2_itm2)+
              as.numeric.factor(v4_bdi2_itm3)+
              as.numeric.factor(v4_bdi2_itm4)+
              as.numeric.factor(v4_bdi2_itm5)+
              as.numeric.factor(v4_bdi2_itm6)+
              as.numeric.factor(v4_bdi2_itm7)+
              as.numeric.factor(v4_bdi2_itm8)+
              as.numeric.factor(v4_bdi2_itm9)+
              as.numeric.factor(v4_bdi2_itm10)+
              as.numeric.factor(v4_bdi2_itm11)+
              as.numeric.factor(v4_bdi2_itm12)+
              as.numeric.factor(v4_bdi2_itm13)+
              as.numeric.factor(v4_bdi2_itm14)+
              as.numeric.factor(v4_bdi2_itm15)+
              as.numeric.factor(v4_bdi2_itm16)+
              as.numeric.factor(v4_bdi2_itm17)+
              as.numeric.factor(v4_bdi2_itm18)+
              as.numeric.factor(v4_bdi2_itm19)+
              as.numeric.factor(v4_bdi2_itm20)+
              as.numeric.factor(v4_bdi2_itm21)

summary(v4_bdi2_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   1.000   5.000   8.866  13.000  47.000    1050

Create dataset

v4_bdi2<-data.frame(v4_bdi2_itm1,v4_bdi2_itm2,v4_bdi2_itm3,v4_bdi2_itm4,v4_bdi2_itm5,
                    v4_bdi2_itm6,v4_bdi2_itm7,v4_bdi2_itm8,v4_bdi2_itm9,v4_bdi2_itm10,
                    v4_bdi2_itm11,v4_bdi2_itm12,v4_bdi2_itm13,v4_bdi2_itm14,
                    v4_bdi2_itm15,v4_bdi2_itm16,v4_bdi2_itm17,v4_bdi2_itm18,
                    v4_bdi2_itm19,v4_bdi2_itm20,v4_bdi2_itm21, v4_bdi2_sum)

ASRM

For explanation, please refer to the section in Visit 1

1. Positive Mood (ordinal [0,1,2,3,4], v4_asrm_itm1)

v4_asrm_recode(v4_clin$v4_asrm_asrm1_gluecklich,v4_con$v4_asrm_asrm1,"v4_asrm_itm1")
##                0    1   2   3   4   <NA>     
## [1,] No. cases 397  86  22  11  4   1023 1543
## [2,] Percent   25.7 5.6 1.4 0.7 0.3 66.3 100

2 Self-Confidence (ordinal [0,1,2,3,4], v4_asrm_itm2)

v4_asrm_recode(v4_clin$v4_asrm_asrm2_selbstbewusst,v4_con$v4_asrm_asrm2,"v4_asrm_itm2")
##                0    1   2   3   4   <NA>     
## [1,] No. cases 397  100 17  3   2   1024 1543
## [2,] Percent   25.7 6.5 1.1 0.2 0.1 66.4 100

3. Sleep (ordinal [0,1,2,3,4], v4_asrm_itm3)

v4_asrm_recode(v4_clin$v4_asrm_asrm3_schlaf,v4_con$v4_asrm_asrm3,"v4_asrm_itm3")
##                0    1   2   3   4   <NA>     
## [1,] No. cases 430  72  12  3   3   1023 1543
## [2,] Percent   27.9 4.7 0.8 0.2 0.2 66.3 100

4. Speech (ordinal [0,1,2,3,4], v4_asrm_itm4)

v4_asrm_recode(v4_clin$v4_asrm_asrm4_reden,v4_con$v4_asrm_asrm4,"v4_asrm_itm4")
##                0    1   2   3   4   <NA>     
## [1,] No. cases 420  86  10  2   1   1024 1543
## [2,] Percent   27.2 5.6 0.6 0.1 0.1 66.4 100

5. Activity Level (ordinal [0,1,2,3,4], v4_asrm_itm5)

v4_asrm_recode(v4_clin$v4_asrm_asrm5_aktiv,v4_con$v4_asrm_asrm5,"v4_asrm_itm5")
##                0    1   2  3   4   <NA>     
## [1,] No. cases 393  105 16 3   3   1023 1543
## [2,] Percent   25.5 6.8 1  0.2 0.2 66.3 100

Create ASRM sum score (continuous [0-20],v4_asrm_sum)

v4_asrm_sum<-as.numeric.factor(v4_asrm_itm1)+
            as.numeric.factor(v4_asrm_itm2)+
            as.numeric.factor(v4_asrm_itm3)+
            as.numeric.factor(v4_asrm_itm4)+
            as.numeric.factor(v4_asrm_itm5)

summary(v4_asrm_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   1.386   2.000  13.000    1027

Create dataset

v4_asrm<-data.frame(v4_asrm_itm1,v4_asrm_itm2,v4_asrm_itm3,v4_asrm_itm4,v4_asrm_itm5,v4_asrm_sum)

MSS

For explanation, please refer to the section in Visit 1

1. “I had more energy” (dichotomous, v4_mss_itm1)

v4_mss_recode(v4_clin$v4_mss_s1_mss1_energie,v4_con$v4_mss_s1_mss1,"v4_mss_itm1")
##                N    Y   <NA>     
## [1,] No. cases 412  102 1029 1543
## [2,] Percent   26.7 6.6 66.7 100

2. “I had trouble sitting still” (dichotomous, v4_mss_itm2)

v4_mss_recode(v4_clin$v4_mss_s1_mss2_ruhig_sitzen,v4_con$v4_mss_s1_mss2,"v4_mss_itm2")
##                N    Y   <NA>     
## [1,] No. cases 444  69  1030 1543
## [2,] Percent   28.8 4.5 66.8 100

3. “I drove faster” (dichotomous, v4_mss_itm3)

v4_mss_recode(v4_clin$v4_mss_s1_mss3_auto_fahren,v4_con$v4_mss_s1_mss3,"v4_mss_itm3")
##                N   Y   <NA>     
## [1,] No. cases 479 11  1053 1543
## [2,] Percent   31  0.7 68.2 100

4. “I drank more alcoholic beverages” (dichotomous, v4_mss_itm4)

v4_mss_recode(v4_clin$v4_mss_s1_mss4_alkohol,v4_con$v4_mss_s1_mss4,"v4_mss_itm4")
##                N    Y   <NA>     
## [1,] No. cases 477  33  1033 1543
## [2,] Percent   30.9 2.1 66.9 100

5. “I changed clothes several times a day” (dichotomous, v4_mss_itm5)

v4_mss_recode(v4_clin$v4_mss_s1_mss5_umziehen, v4_con$v4_mss_s1_mss5,"v4_mss_itm5")
##                N    Y   <NA>     
## [1,] No. cases 473  38  1032 1543
## [2,] Percent   30.7 2.5 66.9 100

6. “I wore brighter clothes/make-up” (dichotomous, v4_mss_itm6)

v4_mss_recode(v4_clin$v4_mss_s1_mss6_bunter,v4_con$v4_mss_s1_mss6,"v4_mss_itm6")
##                N   Y   <NA>     
## [1,] No. cases 494 19  1030 1543
## [2,] Percent   32  1.2 66.8 100

7. “I played music louder” (dichotomous, v4_mss_itm7)

v4_mss_recode(v4_clin$v4_mss_s1_mss7_musik_lauter,v4_con$v4_mss_s1_mss7,"v4_mss_itm7")
##                N    Y   <NA>     
## [1,] No. cases 443  71  1029 1543
## [2,] Percent   28.7 4.6 66.7 100

8. “I ate faster than usual” (dichotomous, v4_mss_itm8)

v4_mss_recode(v4_clin$v4_mss_s1_mss8_hastiger_essen,v4_con$v4_mss_s1_mss8,"v4_mss_itm8")
##                N    Y   <NA>     
## [1,] No. cases 459  54  1030 1543
## [2,] Percent   29.7 3.5 66.8 100

9. “I ate more than usual” (dichotomous, v4_mss_itm9)

v4_mss_recode(v4_clin$v4_mss_s1_mss9_mehr_essen,v4_con$v4_mss_s1_mss9,"v4_mss_itm9")
##                N    Y  <NA>     
## [1,] No. cases 420  93 1030 1543
## [2,] Percent   27.2 6  66.8 100

10. “I slept fewer hours than usual” (dichotomous, v4_mss_itm10)

v4_mss_recode(v4_clin$v4_mss_s1_mss10_weniger_schlaf,v4_con$v4_mss_s1_mss10,"v4_mss_itm10")
##                N   Y   <NA>     
## [1,] No. cases 447 66  1030 1543
## [2,] Percent   29  4.3 66.8 100

11. “I started things that I didn’t finish” (dichotomous, v4_mss_itm11)

v4_mss_recode(v4_clin$v4_mss_s1_mss11_unbeendet,v4_con$v4_mss_s1_mss11,"v4_mss_itm11")
##                N    Y   <NA>     
## [1,] No. cases 410  103 1030 1543
## [2,] Percent   26.6 6.7 66.8 100

12. “I gave away my own possessions” (dichotomous, v4_mss_itm12)

v4_mss_recode(v4_clin$v4_mss_s1_mss12_weggeben,v4_con$v4_mss_s1_mss12,"v4_mss_itm12")
##                N    Y   <NA>     
## [1,] No. cases 472  42  1029 1543
## [2,] Percent   30.6 2.7 66.7 100

13. “I bought gifts for people” (dichotomous, v4_mss_itm13)

v4_mss_recode(v4_clin$v4_mss_s1_mss13_geschenke,v4_con$v4_mss_s1_mss13,"v4_mss_itm13")
##                N    Y   <NA>     
## [1,] No. cases 472  41  1030 1543
## [2,] Percent   30.6 2.7 66.8 100

14. “I spent money more freely” (dichotomous, v4_mss_itm14)

v4_mss_recode(v4_clin$v4_mss_s1_mss14_mehr_geld,v4_con$v4_mss_s1_mss14,"v4_mss_itm14")
##                N    Y   <NA>     
## [1,] No. cases 415  98  1030 1543
## [2,] Percent   26.9 6.4 66.8 100

15. “I accumulated debts” (dichotomous, v4_mss_itm15)

v4_mss_recode(v4_clin$v4_mss_s1_mss15_schulden,v4_con$v4_mss_s1_mss15,"v4_mss_itm15")
##                N    Y   <NA>     
## [1,] No. cases 486  27  1030 1543
## [2,] Percent   31.5 1.7 66.8 100

16. “I made unwise business decisions” (dichotomous, v4_mss_itm16)

v4_mss_recode(v4_clin$v4_mss_s1_mss16_unkluge_entsch,v4_con$v4_mss_s1_mss16,"v4_mss_itm16")
##                N    Y   <NA>     
## [1,] No. cases 492  21  1030 1543
## [2,] Percent   31.9 1.4 66.8 100

17. “I partied more” (dichotomous, v4_mss_itm17)

v4_mss_recode(v4_clin$v4_mss_s1_mss17_parties,v4_con$v4_mss_s1_mss17,"v4_mss_itm17")
##                N   Y   <NA>     
## [1,] No. cases 493 21  1029 1543
## [2,] Percent   32  1.4 66.7 100

18. “I enjoyed flirting” (dichotomous, v4_mss_itm18)

v4_mss_recode(v4_clin$v4_mss_s1_mss18_flirten,v4_con$v4_mss_s1_mss18,"v4_mss_itm18")
##                N    Y   <NA>     
## [1,] No. cases 489  22  1032 1543
## [2,] Percent   31.7 1.4 66.9 100

19. “I masturbated more often” (dichotomous, v4_mss_itm19)

v4_mss_recode(v4_clin$v4_mss_s2_mss19_selbstbefried,v4_con$v4_mss_s2_mss19,"v4_mss_itm19")
##                N    Y   <NA>     
## [1,] No. cases 480  24  1039 1543
## [2,] Percent   31.1 1.6 67.3 100

20. “I was more interested in sex than usual” (dichotomous, v4_mss_itm20)

v4_mss_recode(v4_clin$v4_mss_s2_mss20_sex_interess,v4_con$v4_mss_s2_mss20,"v4_mss_itm20")
##                N    Y   <NA>     
## [1,] No. cases 459  44  1040 1543
## [2,] Percent   29.7 2.9 67.4 100

21. “I had sex with people that I usually wouldn’t have sex with” (dichotomous, v4_mss_itm21)

v4_mss_recode(v4_clin$v4_mss_s2_mss21_sexpartner,v4_con$v4_mss_s2_mss21,"v4_mss_itm21")
##                N    Y   <NA>     
## [1,] No. cases 500  4   1039 1543
## [2,] Percent   32.4 0.3 67.3 100

22. “I spent more time on the phone” (dichotomous, v4_mss_itm22)

v4_mss_recode(v4_clin$v4_mss_s2_mss22_mehr_telefon,v4_con$v4_mss_s2_mss22,"v4_mss_itm22")
##                N    Y   <NA>     
## [1,] No. cases 429  75  1039 1543
## [2,] Percent   27.8 4.9 67.3 100

23. “I spoke louder than usual” (dichotomous, v4_mss_itm23)

v4_mss_recode(v4_clin$v4_mss_s2_mss23_sprache_lauter,v4_con$v4_mss_s2_mss23,"v4_mss_itm23")
##                N    Y   <NA>     
## [1,] No. cases 461  41  1041 1543
## [2,] Percent   29.9 2.7 67.5 100

24. “I spoke so fast that people said they couldn’t understand me” (dichotomous, v4_mss_itm24)

v4_mss_recode(v4_clin$v4_mss_s2_mss24_spr_schneller,v4_con$v4_mss_s2_mss24,"v4_mss_itm24")
##                N    Y   <NA>     
## [1,] No. cases 476  30  1037 1543
## [2,] Percent   30.8 1.9 67.2 100

25. “1 enjoyed punning or rhyming” (dichotomous, v4_mss_itm25)

v4_mss_recode(v4_clin$v4_mss_s2_mss25_witze,v4_con$v4_mss_s2_mss25,"v4_mss_itm25")
##                N    Y   <NA>     
## [1,] No. cases 457  48  1038 1543
## [2,] Percent   29.6 3.1 67.3 100

26. “I butted into conversations” (dichotomous, v4_mss_itm26)

v4_mss_recode(v4_clin$v4_mss_s2_mss26_einmischen,v4_con$v4_mss_s2_mss26,"v4_mss_itm26")
##                N    Y   <NA>     
## [1,] No. cases 484  22  1037 1543
## [2,] Percent   31.4 1.4 67.2 100

27. “I spoke on and on and couldn’t be interrupted” (dichotomous, v4_mss_itm27)

v4_mss_recode(v4_clin$v4_mss_s2_mss27_red_pausenlos,v4_con$v4_mss_s2_mss27,"v4_mss_itm27")
##                N    Y  <NA>     
## [1,] No. cases 490  16 1037 1543
## [2,] Percent   31.8 1  67.2 100

28. “I enjoyed being the centre of attention” (dichotomous, v4_mss_itm28)

v4_mss_recode(v4_clin$v4_mss_s2_mss28_mittelpunkt,v4_con$v4_mss_s2_mss28,"v4_mss_itm28")
##                N   Y   <NA>     
## [1,] No. cases 479 26  1038 1543
## [2,] Percent   31  1.7 67.3 100

29. “I liked to joke and laugh” (dichotomous, v4_mss_itm29)

v4_mss_recode(v4_clin$v4_mss_s2_mss29_herumalbern,v4_con$v4_mss_s2_mss29,"v4_mss_itm29")
##                N   Y   <NA>     
## [1,] No. cases 448 58  1037 1543
## [2,] Percent   29  3.8 67.2 100

30. “People found me entertaining” (dichotomous, v4_mss_itm30)

v4_mss_recode(v4_clin$v4_mss_s2_mss30_unterhaltsamer,v4_con$v4_mss_s2_mss30,"v4_mss_itm30")
##                N    Y   <NA>     
## [1,] No. cases 471  33  1039 1543
## [2,] Percent   30.5 2.1 67.3 100

31. “I felt as if I was on top of the world” (dichotomous, v4_mss_itm31)

v4_mss_recode(v4_clin$v4_mss_s2_mss31_obenauf,v4_con$v4_mss_s2_mss31,"v4_mss_itm31")
##                N    Y   <NA>     
## [1,] No. cases 470  34  1039 1543
## [2,] Percent   30.5 2.2 67.3 100

32. “I was more cheerful than my usual self” (dichotomous, v4_mss_itm32)

v4_mss_recode(v4_clin$v4_mss_s2_mss32_froehlicher,v4_con$v4_mss_s2_mss32,"v4_mss_itm32")
##                N    Y   <NA>     
## [1,] No. cases 426  78  1039 1543
## [2,] Percent   27.6 5.1 67.3 100

33. “Other people got on my nerves” (dichotomous, v4_mss_itm33)

v4_mss_recode(v4_clin$v4_mss_s2_mss33_ungeduldiger,v4_con$v4_mss_s2_mss33,"v4_mss_itm33")
##                N    Y   <NA>     
## [1,] No. cases 400  105 1038 1543
## [2,] Percent   25.9 6.8 67.3 100

34. “I was getting into arguments” (dichotomous, v4_mss_itm34)

v4_mss_recode(v4_clin$v4_mss_s2_mss34_streiten,v4_con$v4_mss_s2_mss34,"v4_mss_itm34")
##                N    Y   <NA>     
## [1,] No. cases 472  34  1037 1543
## [2,] Percent   30.6 2.2 67.2 100

35. “I had so many ideas that I couldn’t get around to doing them all” (dichotomous, v4_mss_itm35)

v4_mss_recode(v4_clin$v4_mss_s2_mss35_ideen,v4_con$v4_mss_s2_mss35,"v4_mss_itm35")
##                N    Y   <NA>     
## [1,] No. cases 437  68  1038 1543
## [2,] Percent   28.3 4.4 67.3 100

36. “My thoughts raced through my mind” (dichotomous, v4_mss_itm36)

v4_mss_recode(v4_clin$v4_mss_s2_mss36_gedanken,v4_con$v4_mss_s2_mss36,"v4_mss_itm36")
##                N    Y   <NA>     
## [1,] No. cases 389  114 1040 1543
## [2,] Percent   25.2 7.4 67.4 100

37. “I couldn’t concentrate on a single topic for longer than a minute” (dichotomous, v4_mss_itm37)

v4_mss_recode(v4_clin$v4_mss_s2_mss37_konzentration,v4_con$v4_mss_s2_mss37,"v4_mss_itm37")
##                N    Y   <NA>     
## [1,] No. cases 426  79  1038 1543
## [2,] Percent   27.6 5.1 67.3 100

38. “I thought I was an especially important person” (dichotomous, v4_mss_itm38)

v4_mss_recode(v4_clin$v4_mss_s2_mss38_etw_besonderes,v4_con$v4_mss_s2_mss38,"v4_mss_itm38")
##                N    Y   <NA>     
## [1,] No. cases 469  35  1039 1543
## [2,] Percent   30.4 2.3 67.3 100

39. “I thought I could change the world” (dichotomous, v4_mss_itm39)

v4_mss_recode(v4_clin$v4_mss_s2_mss39_welt_veraender,v4_con$v4_mss_s2_mss39,"v4_mss_itm39")
##                N    Y   <NA>     
## [1,] No. cases 485  21  1037 1543
## [2,] Percent   31.4 1.4 67.2 100

40. “I thought I was right most of the time” (dichotomous, v4_mss_itm40)

v4_mss_recode(v4_clin$v4_mss_s2_mss40_recht_haben,v4_con$v4_mss_s2_mss40,"v4_mss_itm40")
##                N    Y   <NA>     
## [1,] No. cases 481  22  1040 1543
## [2,] Percent   31.2 1.4 67.4 100

41. “I thought I was superior to others” (dichotomous, v4_mss_itm41)

v4_mss_recode(v4_clin$v4_mss_s3_mss41_ueberlegen,v4_con$v4_mss_s3_mss41,"v4_mss_itm41")
##                N    Y   <NA>     
## [1,] No. cases 489  20  1034 1543
## [2,] Percent   31.7 1.3 67   100

42. “I wanted to take on jobs that I was not trained to handle” (dichotomous, v4_mss_itm42)

v4_mss_recode(v4_clin$v4_mss_s3_mss42_uebermut,v4_con$v4_mss_s3_mss42,"v4_mss_itm42")
##                N    Y   <NA>     
## [1,] No. cases 481  28  1034 1543
## [2,] Percent   31.2 1.8 67   100

43. “I thought I knew what other people were thinking” (dichotomous, v4_mss_itm43)

v4_mss_recode(v4_clin$v4_mss_s3_mss43_ged_lesen_akt,v4_con$v4_mss_s3_mss43,"v4_mss_itm43")
##                N    Y   <NA>     
## [1,] No. cases 475  34  1034 1543
## [2,] Percent   30.8 2.2 67   100

44. “I thought other people knew what I was thinking” (dichotomous, v4_mss_itm44)

v4_mss_recode(v4_clin$v4_mss_s3_mss44_ged_lesen_pas,v4_con$v4_mss_s3_mss44,"v4_mss_itm44")
##                N   Y   <NA>     
## [1,] No. cases 479 30  1034 1543
## [2,] Percent   31  1.9 67   100

45. “I thought someone wanted to harm me” (dichotomous, v4_mss_itm45)

v4_mss_recode(v4_clin$v4_mss_s3_mss45_etw_antun,v4_con$v4_mss_s3_mss45,"v4_mss_itm45")
##                N    Y   <NA>     
## [1,] No. cases 484  25  1034 1543
## [2,] Percent   31.4 1.6 67   100

46. “I heard voices when people weren’t there” (dichotomous, v4_mss_itm46)

v4_mss_recode(v4_clin$v4_mss_s3_mss46_stimmen,v4_con$v4_mss_s3_mss46,"v4_mss_itm46")
##                N    Y   <NA>     
## [1,] No. cases 468  41  1034 1543
## [2,] Percent   30.3 2.7 67   100

47. “I had false beliefs concerning who I was” (dichotomous, v4_mss_itm47)

v4_mss_recode(v4_clin$v4_mss_s3_mss47_jmd_anders,v4_con$v4_mss_s3_mss47,"v4_mss_itm47")
##                N   Y   <NA>     
## [1,] No. cases 494 14  1035 1543
## [2,] Percent   32  0.9 67.1 100

48. “I knew I was getting ill” (dichotomous, v4_mss_itm48)

v4_mss_recode(v4_clin$v4_mss_s3_mss48_krank_einsicht,v4_con$v4_mss_s3_mss48,"v4_mss_itm48")
##                N    Y   <NA>     
## [1,] No. cases 440  65  1038 1543
## [2,] Percent   28.5 4.2 67.3 100

Create MSS sum score (continuous [0-48],v4_mss_sum)

v4_mss_sum<-ifelse(v4_mss_itm1=="Y",1,0)+
            ifelse(v4_mss_itm2=="Y",1,0)+
            ifelse(v4_mss_itm3=="Y",1,0)+
            ifelse(v4_mss_itm4=="Y",1,0)+
            ifelse(v4_mss_itm5=="Y",1,0)+
            ifelse(v4_mss_itm6=="Y",1,0)+
            ifelse(v4_mss_itm7=="Y",1,0)+
            ifelse(v4_mss_itm8=="Y",1,0)+
            ifelse(v4_mss_itm9=="Y",1,0)+
            ifelse(v4_mss_itm10=="Y",1,0)+
            ifelse(v4_mss_itm11=="Y",1,0)+
            ifelse(v4_mss_itm12=="Y",1,0)+
            ifelse(v4_mss_itm13=="Y",1,0)+
            ifelse(v4_mss_itm14=="Y",1,0)+
            ifelse(v4_mss_itm15=="Y",1,0)+
            ifelse(v4_mss_itm16=="Y",1,0)+
            ifelse(v4_mss_itm17=="Y",1,0)+
            ifelse(v4_mss_itm18=="Y",1,0)+
            ifelse(v4_mss_itm19=="Y",1,0)+
            ifelse(v4_mss_itm20=="Y",1,0)+
            ifelse(v4_mss_itm21=="Y",1,0)+
            ifelse(v4_mss_itm22=="Y",1,0)+
            ifelse(v4_mss_itm23=="Y",1,0)+
            ifelse(v4_mss_itm24=="Y",1,0)+
            ifelse(v4_mss_itm25=="Y",1,0)+
            ifelse(v4_mss_itm26=="Y",1,0)+
            ifelse(v4_mss_itm27=="Y",1,0)+
            ifelse(v4_mss_itm28=="Y",1,0)+
            ifelse(v4_mss_itm29=="Y",1,0)+
            ifelse(v4_mss_itm30=="Y",1,0)+
            ifelse(v4_mss_itm31=="Y",1,0)+
            ifelse(v4_mss_itm32=="Y",1,0)+
            ifelse(v4_mss_itm33=="Y",1,0)+
            ifelse(v4_mss_itm34=="Y",1,0)+
            ifelse(v4_mss_itm35=="Y",1,0)+
            ifelse(v4_mss_itm36=="Y",1,0)+
            ifelse(v4_mss_itm37=="Y",1,0)+
            ifelse(v4_mss_itm38=="Y",1,0)+
            ifelse(v4_mss_itm39=="Y",1,0)+
            ifelse(v4_mss_itm40=="Y",1,0)+
            ifelse(v4_mss_itm41=="Y",1,0)+
            ifelse(v4_mss_itm42=="Y",1,0)+
            ifelse(v4_mss_itm43=="Y",1,0)+
            ifelse(v4_mss_itm44=="Y",1,0)+
            ifelse(v4_mss_itm45=="Y",1,0)+
            ifelse(v4_mss_itm46=="Y",1,0)+
            ifelse(v4_mss_itm47=="Y",1,0)+
            ifelse(v4_mss_itm48=="Y",1,0)

summary(v4_mss_sum)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   2.000   4.007   6.000  36.000    1092

Create dataset

v4_mss<-data.frame(v4_mss_itm1,v4_mss_itm2,v4_mss_itm3,v4_mss_itm4,v4_mss_itm5,v4_mss_itm6,
                   v4_mss_itm7,v4_mss_itm8,v4_mss_itm9,v4_mss_itm10,v4_mss_itm11,
                   v4_mss_itm12,v4_mss_itm13,v4_mss_itm14,v4_mss_itm15,v4_mss_itm16,
                   v4_mss_itm17,v4_mss_itm18,v4_mss_itm19,v4_mss_itm20,v4_mss_itm21,
                   v4_mss_itm22,v4_mss_itm23,v4_mss_itm24,v4_mss_itm25,v4_mss_itm26,
                   v4_mss_itm27,v4_mss_itm28,v4_mss_itm29,v4_mss_itm30,v4_mss_itm31,
                   v4_mss_itm32,v4_mss_itm33,v4_mss_itm34,v4_mss_itm35,v4_mss_itm36,
                   v4_mss_itm37,v4_mss_itm38,v4_mss_itm39,v4_mss_itm40,v4_mss_itm41,
                   v4_mss_itm42,v4_mss_itm43,v4_mss_itm44,v4_mss_itm45,v4_mss_itm46,
                   v4_mss_itm47,v4_mss_itm48, v4_mss_sum)

LEQ

For explanation, please refer to the section in Visit 1

Health

1. “Major personal illness or injury”

1A Nature (dichotomous [“good”,“bad”], v4_leq_A_1A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq1a_schw_krankh,v4_con$v4_leq_a_leq1a,"v4_leq_A_1A")
##                -999 bad good <NA>     
## [1,] No. cases 337  120 18   1068 1543
## [2,] Percent   21.8 7.8 1.2  69.2 100

1B Impact (ordinal [0,1,2,3], v4_leq_A_1B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq1e_schw_krankh,v4_con$v4_leq_a_leq1e,"v4_leq_A_1B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 336  8   32  48  51  1068 1543
## [2,] Percent   21.8 0.5 2.1 3.1 3.3 69.2 100

2. “Major change in eating habits”

2A Nature (dichotomous [“good”,“bad”], v4_leq_A_2A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq2a_ernaehrung,v4_con$v4_leq_a_leq2a,"v4_leq_A_2A")
##                -999 bad good <NA>     
## [1,] No. cases 353  49  73   1068 1543
## [2,] Percent   22.9 3.2 4.7  69.2 100

2B Impact (ordinal [0,1,2,3], v4_leq_A_2B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq2e_ernaehrung,v4_con$v4_leq_a_leq2e,"v4_leq_A_2B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 353  8   31 53  30  1068 1543
## [2,] Percent   22.9 0.5 2  3.4 1.9 69.2 100

3. “Major change in sleeping habits”

3A Nature (dichotomous [“good”,“bad”], v4_leq_A_3A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq3a_schlaf,v4_con$v4_leq_a_leq3a,"v4_leq_A_3A")
##                -999 bad good <NA>     
## [1,] No. cases 353  74  48   1068 1543
## [2,] Percent   22.9 4.8 3.1  69.2 100

3B Impact (ordinal [0,1,2,3], v4_leq_A_3B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq3e_schlaf,v4_con$v4_leq_a_leq3e,"v4_leq_A_3B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 353  12  35  37  38  1068 1543
## [2,] Percent   22.9 0.8 2.3 2.4 2.5 69.2 100

4. “Major change in usual type and/or amount of recreation”

4A Nature (dichotomous [“good”,“bad”], v4_leq_A_4A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq4a_freizeit,v4_con$v4_leq_a_leq4a,"v4_leq_A_4A")
##                -999 bad good <NA>     
## [1,] No. cases 347  45  83   1068 1543
## [2,] Percent   22.5 2.9 5.4  69.2 100

4B Impact (ordinal [0,1,2,3], v4_leq_A_4B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq4e_freizeit,v4_con$v4_leq_a_leq4e,"v4_leq_A_4B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 347  6   27  56  39  1068 1543
## [2,] Percent   22.5 0.4 1.7 3.6 2.5 69.2 100

5. “Major dental work”

5A Nature (dichotomous [“good”,“bad”], v4_leq_A_5A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq5a_zahnarzt,v4_con$v4_leq_a_leq5a,"v4_leq_A_5A")
##                -999 bad good <NA>     
## [1,] No. cases 392  41  42   1068 1543
## [2,] Percent   25.4 2.7 2.7  69.2 100

5B Impact (ordinal [0,1,2,3], v4_leq_A_5B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq5e_zahnarzt,v4_con$v4_leq_a_leq5e,"v4_leq_A_5B")
##                -999 0   1  2   3   <NA>     
## [1,] No. cases 390  19  31 18  17  1068 1543
## [2,] Percent   25.3 1.2 2  1.2 1.1 69.2 100

6. “(Female) Pregnancy”

6A Nature (dichotomous [“good”,“bad”], v4_leq_A_6A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq6a_schwanger,v4_con$v4_leq_a_leq6a,"v4_leq_A_6A")
##                -999 good <NA>     
## [1,] No. cases 472  3    1068 1543
## [2,] Percent   30.6 0.2  69.2 100

6B Impact (ordinal [0,1,2,3], v4_leq_A_6B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq6e_schwanger,v4_con$v4_leq_a_leq6e,"v4_leq_A_6B")
##                -999 0   3   <NA>     
## [1,] No. cases 471  1   3   1068 1543
## [2,] Percent   30.5 0.1 0.2 69.2 100

7. “(Female) Miscarriage or abortion”

7A Nature (dichotomous [“good”,“bad”], v4_leq_A_7A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq7a_fehlg_abtr,v4_con$v4_leq_a_leq7a,"v4_leq_A_7A")
##                -999 <NA>     
## [1,] No. cases 475  1068 1543
## [2,] Percent   30.8 69.2 100

7B Impact (ordinal [0,1,2,3], v4_leq_A_7B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq7e_fehlg_abtr,v4_con$v4_leq_a_leq7e,"v4_leq_A_7B")
##                -999 0   <NA>     
## [1,] No. cases 474  1   1068 1543
## [2,] Percent   30.7 0.1 69.2 100

8. “(Female) Started menopause”

8A Nature (dichotomous [“good”,“bad”], v4_leq_A_8A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq8a_wechseljahre,v4_con$v4_leq_a_leq8a,"v4_leq_A_8A")
##                -999 bad good <NA>     
## [1,] No. cases 460  11  4    1068 1543
## [2,] Percent   29.8 0.7 0.3  69.2 100

8B Impact (ordinal [0,1,2,3], v4_leq_A_8B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq8e_wechseljahre,v4_con$v4_leq_a_leq8e,"v4_leq_A_8B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 459  4   3   4   5   1068 1543
## [2,] Percent   29.7 0.3 0.2 0.3 0.3 69.2 100

9. “Major difficulties with birth control pills or devices”

9A Nature (dichotomous [“good”,“bad”], v4_leq_A_9A)

v4_leq_a_recode(v4_clin$v4_leq_a_leq9a_verhuetung,v4_con$v4_leq_a_leq9a,"v4_leq_A_9A")
##                -999 good <NA>     
## [1,] No. cases 471  4    1068 1543
## [2,] Percent   30.5 0.3  69.2 100

9B Impact (ordinal [0,1,2,3], v4_leq_A_9B)

v4_leq_b_recode(v4_clin$v4_leq_a_leq9e_verhuetung,v4_con$v4_leq_a_leq9e,"v4_leq_A_9B")
##                -999 0   1   3   <NA>     
## [1,] No. cases 470  2   2   1   1068 1543
## [2,] Percent   30.5 0.1 0.1 0.1 69.2 100

Create dataset

v4_leq_A<-data.frame(v4_leq_A_1A,v4_leq_A_1B,v4_leq_A_2A,v4_leq_A_2B,v4_leq_A_3A,
                     v4_leq_A_3B,v4_leq_A_4A,v4_leq_A_4B,v4_leq_A_5A,v4_leq_A_5B,
                     v4_leq_A_6A,v4_leq_A_6B,v4_leq_A_7A,v4_leq_A_7B,v4_leq_A_8A,
                     v4_leq_A_8B,v4_leq_A_9A,v4_leq_A_9B)

Work

10. “Difficulty finding a job”

10A Nature (dichotomous [“good”,“bad”], v4_leq_B_10A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq10a_arbeitssuche,v4_con$v4_leq_b_leq10a,"v4_leq_B_10A")
##                -999 bad good <NA>     
## [1,] No. cases 429  38  8    1068 1543
## [2,] Percent   27.8 2.5 0.5  69.2 100

10B Impact (ordinal [0,1,2,3], v4_leq_B_10B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq10e_arbeitssuche,v4_con$v4_leq_b_leq10e,"v4_leq_B_10B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 428  3   11  14  19  1068 1543
## [2,] Percent   27.7 0.2 0.7 0.9 1.2 69.2 100

11. “Beginning work outside the home”

11A Nature (dichotomous [“good”,“bad”], v4_leq_B_11A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq11a_arbeit_aussen,v4_con$v4_leq_b_leq11a,"v4_leq_B_11A")
##                -999 bad good <NA>     
## [1,] No. cases 432  8   35   1068 1543
## [2,] Percent   28   0.5 2.3  69.2 100

11B Impact (ordinal [0,1,2,3], v4_leq_B_11B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq11e_arbeit_aussen,v4_con$v4_leq_b_leq11e,"v4_leq_B_11B")
##                -999 0   1   2  3  <NA>     
## [1,] No. cases 430  4   9   16 16 1068 1543
## [2,] Percent   27.9 0.3 0.6 1  1  69.2 100

12. “Changing to a new type of work” 12A Nature (dichotomous [“good”,“bad”], v4_leq_B_12A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq12a_arbeitswechs,v4_con$v4_leq_b_leq12a,"v4_leq_B_12A")
##                -999 bad good <NA>     
## [1,] No. cases 423  8   44   1068 1543
## [2,] Percent   27.4 0.5 2.9  69.2 100

12B Impact (ordinal [0,1,2,3], v4_leq_B_12B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq12e_arbeitswechs,v4_con$v4_leq_b_leq12e,"v4_leq_B_12B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 421  3   12  16 23  1068 1543
## [2,] Percent   27.3 0.2 0.8 1  1.5 69.2 100

13. “Changing your work hours or conditions”

13A Nature (dichotomous [“good”,“bad”], v4_leq_B_13A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq13a_veraend_arb,v4_con$v4_leq_b_leq13a,"v4_leq_B_13A")
##                -999 bad good <NA>     
## [1,] No. cases 404  17  54   1068 1543
## [2,] Percent   26.2 1.1 3.5  69.2 100

13B Impact (ordinal [0,1,2,3], v4_leq_B_13B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq13e_veraend_arb,v4_con$v4_leq_b_leq13e,"v4_leq_B_13B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 403  3   21  26  22  1068 1543
## [2,] Percent   26.1 0.2 1.4 1.7 1.4 69.2 100

14. “Change in your responsibilities at work” 14A Nature (dichotomous [“good”,“bad”], v4_leq_B_14A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq14a_veraend_ba,v4_con$v4_leq_b_leq14a,"v4_leq_B_14A")
##                -999 bad good <NA>     
## [1,] No. cases 409  15  51   1068 1543
## [2,] Percent   26.5 1   3.3  69.2 100

14B Impact (ordinal [0,1,2,3], v4_leq_B_14B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq14e_veraend_ba,v4_con$v4_leq_b_leq14e,"v4_leq_B_14B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 408  4   12  32  19  1068 1543
## [2,] Percent   26.4 0.3 0.8 2.1 1.2 69.2 100

15. “Troubles at work with your employer or co-worker”

15A Nature (dichotomous [“good”,“bad”], v4_leq_B_15A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq15a_schw_arbeit,v4_con$v4_leq_b_leq15a,"v4_leq_B_15A")
##                -999 bad good <NA>     
## [1,] No. cases 432  35  8    1068 1543
## [2,] Percent   28   2.3 0.5  69.2 100

15B Impact (ordinal [0,1,2,3], v4_leq_B_15B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq15e_schw_arbeit,v4_con$v4_leq_b_leq15e,"v4_leq_B_15B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 431  5   13  17  9   1068 1543
## [2,] Percent   27.9 0.3 0.8 1.1 0.6 69.2 100

16. “Major business readjustment”

16A Nature (dichotomous [“good”,“bad”], v4_leq_B_16A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq16a_betr_reorg,v4_con$v4_leq_b_leq16a,"v4_leq_B_16A")
##                -999 bad good <NA>     
## [1,] No. cases 459  9   7    1068 1543
## [2,] Percent   29.7 0.6 0.5  69.2 100

16B Impact (ordinal [0,1,2,3], v4_leq_B_16B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq16e_betr_reorg,v4_con$v4_leq_b_leq16e,"v4_leq_B_16B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 458  1   6   3   7   1068 1543
## [2,] Percent   29.7 0.1 0.4 0.2 0.5 69.2 100

17. “Being fired or laid off from work”

17A Nature (dichotomous [“good”,“bad”], v4_leq_B_17A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq17a_kuendigung,v4_con$v4_leq_b_leq17a,"v4_leq_B_17A")
##                -999 bad good <NA>     
## [1,] No. cases 464  7   4    1068 1543
## [2,] Percent   30.1 0.5 0.3  69.2 100

17B Impact (ordinal [0,1,2,3], v4_leq_B_17B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq17e_kuendigung,v4_con$v4_leq_b_leq17e,"v4_leq_B_17B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 463  2   3   1   6   1068 1543
## [2,] Percent   30   0.1 0.2 0.1 0.4 69.2 100

18. “Retirement from work”

18A Nature (dichotomous [“good”,“bad”], v4_leq_B_18A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq18a_ende_beruf,v4_con$v4_leq_b_leq18a,"v4_leq_B_18A")
##                -999 bad good <NA>     
## [1,] No. cases 464  3   8    1068 1543
## [2,] Percent   30.1 0.2 0.5  69.2 100

18B Impact (ordinal [0,1,2,3], v4_leq_B_18B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq18e_ende_beruf,v4_con$v4_leq_b_leq18e,"v4_leq_B_18B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 463  1   3   1   7   1068 1543
## [2,] Percent   30   0.1 0.2 0.1 0.5 69.2 100

19. “Taking courses by mail or studying at home to help you in your work”

19A Nature (dichotomous [“good”,“bad”], v4_leq_B_19A)

v4_leq_a_recode(v4_clin$v4_leq_b_leq19a_fortbildung,v4_con$v4_leq_b_leq19a,"v4_leq_B_19A")
##                -999 bad good <NA>     
## [1,] No. cases 455  3   17   1068 1543
## [2,] Percent   29.5 0.2 1.1  69.2 100

19B Impact (ordinal [0,1,2,3], v4_leq_B_19B)

v4_leq_b_recode(v4_clin$v4_leq_b_leq19e_fortbildung,v4_con$v4_leq_b_leq19e,"v4_leq_B_19B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 454  1   8   7   5   1068 1543
## [2,] Percent   29.4 0.1 0.5 0.5 0.3 69.2 100
v4_leq_B<-data.frame(v4_leq_B_10A,v4_leq_B_10B,v4_leq_B_11A,v4_leq_B_11B,v4_leq_B_12A,
                     v4_leq_B_12B,v4_leq_B_13A,v4_leq_B_13B,v4_leq_B_14A,v4_leq_B_14B,
                     v4_leq_B_15A,v4_leq_B_15B,v4_leq_B_16A,v4_leq_B_16B,v4_leq_B_17A,
                     v4_leq_B_17B,v4_leq_B_18A,v4_leq_B_18B,v4_leq_B_19A,v4_leq_B_19B)

School

20. “Beginning or ceasing school, college, or training program”

20A Nature (dichotomous [“good”,“bad”], v4_leq_C_20A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq20a_beginn_ende,v4_con$v4_leq_c_d_leq20a,"v4_leq_C_20A")
##                -999 bad good <NA>     
## [1,] No. cases 450  1   24   1068 1543
## [2,] Percent   29.2 0.1 1.6  69.2 100

20B Impact (ordinal [0,1,2,3], v4_leq_C_20B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq20e_beginn_ende,v4_con$v4_leq_c_d_leq20e,"v4_leq_C_20B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 449  1   5   7   13  1068 1543
## [2,] Percent   29.1 0.1 0.3 0.5 0.8 69.2 100

21. “Change of school, college, or training program”

21A Nature (dichotomous [“good”,“bad”], v4_leq_C_21A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq21a_schulwechsel,v4_con$v4_leq_c_d_leq21a,"v4_leq_C_21A")
##                -999 bad good <NA>     
## [1,] No. cases 465  2   8    1068 1543
## [2,] Percent   30.1 0.1 0.5  69.2 100

21B Impact (ordinal [0,1,2,3], v4_leq_C_21B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq21e_schulwechsel,v4_con$v4_leq_c_d_leq21e,"v4_leq_C_21B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 464  1   2   2   6   1068 1543
## [2,] Percent   30.1 0.1 0.1 0.1 0.4 69.2 100

22. “Change in career goal or academic major”

A Nature (dichotomous [“good”,“bad”], v4_leq_C_22A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq22a_aend_karriere,v4_con$v4_leq_c_d_leq22a,"v4_leq_C_22A")
##                -999 bad good <NA>     
## [1,] No. cases 464  2   9    1068 1543
## [2,] Percent   30.1 0.1 0.6  69.2 100

B Impact (ordinal [0,1,2,3], v4_leq_C_22B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq22e_aend_karriere,v4_con$v4_leq_c_d_leq22e,"v4_leq_C_22B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 463  1   4   3   4   1068 1543
## [2,] Percent   30   0.1 0.3 0.2 0.3 69.2 100

23. “Problem in school, college, or training program”

23A Nature (dichotomous [“good”,“bad”], v4_leq_C_23A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq23a_schulprob,v4_con$v4_leq_c_d_leq23a,"v4_leq_C_23A")
##                -999 bad good <NA>     
## [1,] No. cases 465  8   2    1068 1543
## [2,] Percent   30.1 0.5 0.1  69.2 100

23B Impact (ordinal [0,1,2,3], v4_leq_C_23B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq23e_schulprob,v4_con$v4_leq_c_d_leq23e,"v4_leq_C_23B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 464  1   3   2   5   1068 1543
## [2,] Percent   30.1 0.1 0.2 0.1 0.3 69.2 100

Create dataset

v4_leq_C<-data.frame(v4_leq_C_20A,v4_leq_C_20B,v4_leq_C_21A,v4_leq_C_21B,v4_leq_C_22A,v4_leq_C_22B,v4_leq_C_23A,v4_leq_C_23B)

Residence

24. “Difficulty finding housing”

24A Nature (dichotomous [“good”,“bad”], v4_leq_D_24A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq24a_schw_wsuche,v4_con$v4_leq_c_d_leq24a,"v4_leq_D_24A")
##                -999 bad good <NA>     
## [1,] No. cases 452  18  5    1068 1543
## [2,] Percent   29.3 1.2 0.3  69.2 100

24B Impact (ordinal [0,1,2,3], v4_leq_D_24B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq24e_schw_wsuche,v4_con$v4_leq_c_d_leq24e,"v4_leq_D_24B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 450  2   5   7   11  1068 1543
## [2,] Percent   29.2 0.1 0.3 0.5 0.7 69.2 100

25. “Changing residence within the same town or city”

A Nature (dichotomous [“good”,“bad”], v4_leq_D_25A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq25a_umzug_nah,v4_con$v4_leq_c_d_leq25a,"v4_leq_D_25A")
##                -999 bad good <NA>     
## [1,] No. cases 442  3   30   1068 1543
## [2,] Percent   28.6 0.2 1.9  69.2 100

B Impact (ordinal [0,1,2,3], v4_leq_D_25B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq25e_umzug_nah,v4_con$v4_leq_c_d_leq25e,"v4_leq_D_25B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 440  2   5   10  18  1068 1543
## [2,] Percent   28.5 0.1 0.3 0.6 1.2 69.2 100

26. “Moving to a different town, city, state, or country”

26A Nature (dichotomous [“good”,“bad”], v4_leq_D_26A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq26a_umzug_fern,v4_con$v4_leq_c_d_leq26a,"v4_leq_D_26A")
##                -999 bad good <NA>     
## [1,] No. cases 466  2   7    1068 1543
## [2,] Percent   30.2 0.1 0.5  69.2 100

26B Impact (ordinal [0,1,2,3], v4_leq_D_26B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq26e_umzug_fern,v4_con$v4_leq_c_d_leq26e,"v4_leq_D_26B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 465  2   2   6   1068 1543
## [2,] Percent   30.1 0.1 0.1 0.4 69.2 100

27. “Major change in your life conditions (home improvements or a decline in your home or neighborhood)”

27A Nature (dichotomous [“good”,“bad”], v4_leq_D_27A)

v4_leq_a_recode(v4_clin$v4_leq_c_d_leq27a_veraend_lu,v4_con$v4_leq_c_d_leq27a,"v4_leq_D_27A")
##                -999 bad good <NA>     
## [1,] No. cases 418  18  39   1068 1543
## [2,] Percent   27.1 1.2 2.5  69.2 100

27B Impact (ordinal [0,1,2,3], v4_leq_D_27B)

v4_leq_b_recode(v4_clin$v4_leq_c_d_leq27e_veraend_lu,v4_con$v4_leq_c_d_leq27e,"v4_leq_D_27B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 417  1   9   19  29  1068 1543
## [2,] Percent   27   0.1 0.6 1.2 1.9 69.2 100

Create dataset

v4_leq_D<-data.frame(v4_leq_D_24A,v4_leq_D_24B,v4_leq_D_25A,v4_leq_D_25B,v4_leq_D_26A,
                     v4_leq_D_26B,v4_leq_D_27A,v4_leq_D_27B)

Love and marriage

28. “Began a new, close, personal relationship”

28A Nature (dichotomous [“good”,“bad”], v4_leq_E_28A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq28a_neue_bez,v4_con$v4_leq_e_leq28a,"v4_leq_E_28A")
##                -999 bad good <NA>     
## [1,] No. cases 448  1   26   1068 1543
## [2,] Percent   29   0.1 1.7  69.2 100

28B Impact (ordinal [0,1,2,3], v4_leq_E_28B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq28e_neue_bez,v4_con$v4_leq_e_leq28e,"v4_leq_E_28B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 447  1   4   9   14  1068 1543
## [2,] Percent   29   0.1 0.3 0.6 0.9 69.2 100

29. “Became engaged”

29A Nature (dichotomous [“good”,“bad”], v4_leq_E_29A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq29a_verlobung,v4_con$v4_leq_e_leq29a,"v4_leq_E_29A")
##                -999 good <NA>     
## [1,] No. cases 471  4    1068 1543
## [2,] Percent   30.5 0.3  69.2 100

29B Impact (ordinal [0,1,2,3], v4_leq_E_29B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq29e_verlobung,v4_con$v4_leq_e_leq29e,"v4_leq_E_29B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 470  1   2   2   1068 1543
## [2,] Percent   30.5 0.1 0.1 0.1 69.2 100

30. “Girlfriend or boyfriend problems”

30A Nature (dichotomous [“good”,“bad”], v4_leq_E_30A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq30a_prob_partner,v4_con$v4_leq_e_leq30a,"v4_leq_E_30A")
##                -999 bad good <NA>     
## [1,] No. cases 440  32  3    1068 1543
## [2,] Percent   28.5 2.1 0.2  69.2 100

30B Impact (ordinal [0,1,2,3], v4_leq_E_30B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq30e_prob_partner,v4_con$v4_leq_e_leq30e,"v4_leq_E_30B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 439  3   10  9   14  1068 1543
## [2,] Percent   28.5 0.2 0.6 0.6 0.9 69.2 100

31. “Breaking up with a girlfriend or breaking an engagement”

31A Nature (dichotomous [“good”,“bad”], v4_leq_E_31A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq31a_trennung,v4_con$v4_leq_e_leq31a,"v4_leq_E_31A")
##                -999 bad good <NA>     
## [1,] No. cases 460  10  5    1068 1543
## [2,] Percent   29.8 0.6 0.3  69.2 100

31B Impact (ordinal [0,1,2,3], v4_leq_E_31B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq31e_trennung,v4_con$v4_leq_e_leq31e,"v4_leq_E_31B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 459  1   4   2   9   1068 1543
## [2,] Percent   29.7 0.1 0.3 0.1 0.6 69.2 100

32. “(Male) Wife or girlfriend’s pregnancy”

32A Nature (dichotomous [“good”,“bad”], v4_leq_E_32A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq32a_schwanger_p,v4_con$v4_leq_e_leq32a,"v4_leq_E_32A")
##                -999 good <NA>     
## [1,] No. cases 473  2    1068 1543
## [2,] Percent   30.7 0.1  69.2 100

32B Impact (ordinal [0,1,2,3], v4_leq_E_32B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq32e_schwanger_p,v4_con$v4_leq_e_leq32e,"v4_leq_E_32B")
##                -999 2   3   <NA>     
## [1,] No. cases 473  1   1   1068 1543
## [2,] Percent   30.7 0.1 0.1 69.2 100

33. “(Male) Wife or girlfriend having a miscarriage or abortion”

33A Nature (dichotomous [“good”,“bad”], v4_leq_E_33A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq33a_fehlg_abtr_p,v4_con$v4_leq_e_leq33a,"v4_leq_E_33A")
##                -999 <NA>     
## [1,] No. cases 475  1068 1543
## [2,] Percent   30.8 69.2 100

33B Impact (ordinal [0,1,2,3], v4_leq_E_33B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq33e_fehlg_abtr_p,v4_con$v4_leq_e_leq33e,"v4_leq_E_33B")
##                -999 <NA>     
## [1,] No. cases 475  1068 1543
## [2,] Percent   30.8 69.2 100

34. “Getting married (or beginning to live with someone)”

34A Nature (dichotomous [“good”,“bad”], v4_leq_E_34A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq34a_heirat,v4_con$v4_leq_e_leq34a,"v4_leq_E_34A")
##                -999 good <NA>     
## [1,] No. cases 470  5    1068 1543
## [2,] Percent   30.5 0.3  69.2 100

34B Impact (ordinal [0,1,2,3], v4_leq_E_34B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq34e_heirat,v4_con$v4_leq_e_leq34e,"v4_leq_E_34B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 469  1   3   2   1068 1543
## [2,] Percent   30.4 0.1 0.2 0.1 69.2 100

35. “A change in closeness with your partner”

35A Nature (dichotomous [“good”,“bad”], v4_leq_E_35A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq35a_veraend_naehe,v4_con$v4_leq_e_leq35a,"v4_leq_E_35A")
##                -999 bad good <NA>     
## [1,] No. cases 434  13  28   1068 1543
## [2,] Percent   28.1 0.8 1.8  69.2 100

35B Impact (ordinal [0,1,2,3], v4_leq_E_35B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq35e_veraend_naehe,v4_con$v4_leq_e_leq35e,"v4_leq_E_35B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 433  1   8   13  20  1068 1543
## [2,] Percent   28.1 0.1 0.5 0.8 1.3 69.2 100

36. “Infidelity”

36A Nature (dichotomous [“good”,“bad”], v4_leq_E_36A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq36a_untreue,v4_con$v4_leq_e_leq36a,"v4_leq_E_36A")
##                -999 bad good <NA>     
## [1,] No. cases 472  2   1    1068 1543
## [2,] Percent   30.6 0.1 0.1  69.2 100

36B Impact (ordinal [0,1,2,3], v4_leq_E_36B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq36e_untreue,v4_con$v4_leq_e_leq36e,"v4_leq_E_36B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 471  1   1   1   1   1068 1543
## [2,] Percent   30.5 0.1 0.1 0.1 0.1 69.2 100

37. “Trouble with in-laws”

37A Nature (dichotomous [“good”,“bad”], v4_leq_E_37A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq37a_konf_schwiege,v4_con$v4_leq_e_leq37a,"v4_leq_E_37A")
##                -999 bad good <NA>     
## [1,] No. cases 466  8   1    1068 1543
## [2,] Percent   30.2 0.5 0.1  69.2 100

37B Impact (ordinal [0,1,2,3], v4_leq_E_37B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq37e_konf_schwiege,v4_con$v4_leq_e_leq37e,"v4_leq_E_37B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 465  1   3   5   1   1068 1543
## [2,] Percent   30.1 0.1 0.2 0.3 0.1 69.2 100

38. “Separation from spouse or partner due to conflict”

38A Nature (dichotomous [“good”,“bad”], v4_leq_E_38A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq38a_trennung_str,v4_con$v4_leq_e_leq38a,"v4_leq_E_38A")
##                -999 bad good <NA>     
## [1,] No. cases 471  3   1    1068 1543
## [2,] Percent   30.5 0.2 0.1  69.2 100

38B Impact (ordinal [0,1,2,3], v4_leq_E_38B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq38e_trennung_str,v4_con$v4_leq_e_leq38e,"v4_leq_E_38B")
##                -999 0   1   3   <NA>     
## [1,] No. cases 470  1   2   2   1068 1543
## [2,] Percent   30.5 0.1 0.1 0.1 69.2 100

39. “Separation from spouse or partner due to work, travel, etc.”

39A Nature (dichotomous [“good”,“bad”], v4_leq_E_39A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq39a_trennung_ber,v4_con$v4_leq_e_leq39a,"v4_leq_E_39A")
##                -999 bad good <NA>     
## [1,] No. cases 473  1   1    1068 1543
## [2,] Percent   30.7 0.1 0.1  69.2 100

39B Impact (ordinal [0,1,2,3], v4_leq_E_39B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq39e_trennung_ber,v4_con$v4_leq_e_leq39e,"v4_leq_E_39B")
##                -999 0   1   3   <NA>     
## [1,] No. cases 472  1   1   1   1068 1543
## [2,] Percent   30.6 0.1 0.1 0.1 69.2 100

40. “Reconciliation with spouse or partner”

40A Nature (dichotomous [“good”,“bad”], v4_leq_E_40A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq40a_versoehnung,v4_con$v4_leq_e_leq40a,"v4_leq_E_40A")
##                -999 good <NA>     
## [1,] No. cases 466  9    1068 1543
## [2,] Percent   30.2 0.6  69.2 100

40B Impact (ordinal [0,1,2,3], v4_leq_E_40B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq40a_versoehnung,v4_con$v4_leq_e_leq40e,"v4_leq_E_40B")
##                -999 1   3   <NA>     
## [1,] No. cases 466  8   1   1068 1543
## [2,] Percent   30.2 0.5 0.1 69.2 100

41. “Divorce”

41A Nature (dichotomous [“good”,“bad”], v4_leq_E_41A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq41a_scheidung,v4_con$v4_leq_e_leq41a,"v4_leq_E_41A")
##                -999 bad <NA>     
## [1,] No. cases 474  1   1068 1543
## [2,] Percent   30.7 0.1 69.2 100

41B Impact (ordinal [0,1,2,3], v4_leq_E_41B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq41e_scheidung,v4_con$v4_leq_e_leq41e,"v4_leq_E_41B")
##                -999 0   3   <NA>     
## [1,] No. cases 473  1   1   1068 1543
## [2,] Percent   30.7 0.1 0.1 69.2 100

42. “Change in your spouse or partner’s work outside the home (beginning work, ceasing work, changing jobs, retirement, etc.”

42A Nature (dichotomous [“good”,“bad”], v4_leq_E_42A)

v4_leq_a_recode(v4_clin$v4_leq_e_leq42a_veraend_taet,v4_con$v4_leq_e_leq42a,"v4_leq_E_42A")
##                -999 bad good <NA>     
## [1,] No. cases 459  4   12   1068 1543
## [2,] Percent   29.7 0.3 0.8  69.2 100

42B Impact (ordinal [0,1,2,3], v4_leq_E_42B)

v4_leq_b_recode(v4_clin$v4_leq_e_leq42e_veraend_taet,v4_con$v4_leq_e_leq42e,"v4_leq_E_42B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 458  2   3   8   4   1068 1543
## [2,] Percent   29.7 0.1 0.2 0.5 0.3 69.2 100

Create dataset

v4_leq_E<-data.frame(v4_leq_E_28A,v4_leq_E_28B,v4_leq_E_29A,v4_leq_E_29B,v4_leq_E_30A,
                     v4_leq_E_30B,v4_leq_E_31A,v4_leq_E_31B,v4_leq_E_32A,v4_leq_E_32B,
                     v4_leq_E_33A,v4_leq_E_33B,v4_leq_E_34A,v4_leq_E_34B,v4_leq_E_35A,
                     v4_leq_E_35B,v4_leq_E_36A,v4_leq_E_36B,v4_leq_E_37A,v4_leq_E_37B,
                     v4_leq_E_38A,v4_leq_E_38B,v4_leq_E_39A,v4_leq_E_39B,v4_leq_E_40A,
                     v4_leq_E_40B,v4_leq_E_41A,v4_leq_E_41B,v4_leq_E_42A,v4_leq_E_42B)

Family and close friends

43. “Gain of a new family member (through birth, adoption, relative moving in, etc)”

43A Nature (dichotomous [“good”,“bad”], v4_leq_F_43A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq43a_neu_fmitglied,v4_con$v4_leq_f_g_leq43a,"v4_leq_F_43A")
##                -999 bad good <NA>     
## [1,] No. cases 453  2   20   1068 1543
## [2,] Percent   29.4 0.1 1.3  69.2 100

43B Impact (ordinal [0,1,2,3], v4_leq_F_43B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq43e_neu_fmitglied,v4_con$v4_leq_f_g_leq43e,"v4_leq_F_43B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 452  3   5   1   14  1068 1543
## [2,] Percent   29.3 0.2 0.3 0.1 0.9 69.2 100

44. “Child or family member leaving home (due to marriage, to attend college, or for some other reason)”

44A Nature (dichotomous [“good”,“bad”], v4_leq_F_44A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq44a_auszug_fm,v4_con$v4_leq_f_g_leq44a,"v4_leq_F_44A")
##                -999 bad good <NA>     
## [1,] No. cases 467  5   3    1068 1543
## [2,] Percent   30.3 0.3 0.2  69.2 100

44B Impact (ordinal [0,1,2,3], v4_leq_F_44B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq44e_auszug_fm,v4_con$v4_leq_f_g_leq44e,"v4_leq_F_44B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 466  1   1   3   4   1068 1543
## [2,] Percent   30.2 0.1 0.1 0.2 0.3 69.2 100

45. “Major change in the health or behavior of a family member or close friend (illness, accidents, drug or disciplinary problems, etc.)”

45A Nature (dichotomous [“good”,“bad”], v4_leq_F_45A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq45a_gz_verh_fm,v4_con$v4_leq_f_g_leq45a,"v4_leq_F_45A")
##                -999 bad good <NA>     
## [1,] No. cases 411  58  6    1068 1543
## [2,] Percent   26.6 3.8 0.4  69.2 100

45B Impact (ordinal [0,1,2,3], v4_leq_F_45B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq45e_gz_verh_fm,v4_con$v4_leq_f_g_leq45e,"v4_leq_F_45B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 409  6   14  19  27  1068 1543
## [2,] Percent   26.5 0.4 0.9 1.2 1.7 69.2 100

46. “Death of spouse or partner”

46A Nature (dichotomous [“good”,“bad”], v4_leq_F_46A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq46a_tod_partner,v4_con$v4_leq_f_g_leq46a,"v4_leq_F_46A")
##                -999 bad good <NA>     
## [1,] No. cases 473  1   1    1068 1543
## [2,] Percent   30.7 0.1 0.1  69.2 100

46B Impact (ordinal [0,1,2,3], v4_leq_F_46B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq46e_tod_partner,v4_con$v4_leq_f_g_leq46e,"v4_leq_F_46B")
##                -999 0   1   2   <NA>     
## [1,] No. cases 472  1   1   1   1068 1543
## [2,] Percent   30.6 0.1 0.1 0.1 69.2 100

47. “Death of a child”

47A Nature (dichotomous [“good”,“bad”], v4_leq_F_47A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq47a_tod_kind,v4_con$v4_leq_f_g_leq47a,"v4_leq_F_47A")
##                -999 <NA>     
## [1,] No. cases 475  1068 1543
## [2,] Percent   30.8 69.2 100

47B Impact (ordinal [0,1,2,3], v4_leq_F_47B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq47e_tod_kind,v4_con$v4_leq_f_g_leq47e,"v4_leq_F_47B")
##                -999 0   <NA>     
## [1,] No. cases 474  1   1068 1543
## [2,] Percent   30.7 0.1 69.2 100

48. “Death of family member or close friend”

48A Nature (dichotomous [“good”,“bad”], v4_leq_F_48A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq48a_tod_fm_ef,v4_con$v4_leq_f_g_leq48a,"v4_leq_F_48A")
##                -999 bad good <NA>     
## [1,] No. cases 429  43  3    1068 1543
## [2,] Percent   27.8 2.8 0.2  69.2 100

48B Impact (ordinal [0,1,2,3], v4_leq_F_48B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq48e_tod_fm_ef,v4_con$v4_leq_f_g_leq48e,"v4_leq_F_48B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 428  4   10  15 18  1068 1543
## [2,] Percent   27.7 0.3 0.6 1  1.2 69.2 100

49. “Birth of a grandchild”

49A Nature (dichotomous [“good”,“bad”], v4_leq_F_49A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq49a_geb_enkel,v4_con$v4_leq_f_g_leq49a,"v4_leq_F_49A")
##                -999 good <NA>     
## [1,] No. cases 462  13   1068 1543
## [2,] Percent   29.9 0.8  69.2 100

49B Impact (ordinal [0,1,2,3], v4_leq_F_49B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq49e_geb_enkel,v4_con$v4_leq_f_g_leq49e,"v4_leq_F_49B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 461  4   2   3   5   1068 1543
## [2,] Percent   29.9 0.3 0.1 0.2 0.3 69.2 100

50. “Change in marital status of your parents”

50A Nature (dichotomous [“good”,“bad”], v4_leq_F_50A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq50a_fstand_eltern,v4_con$v4_leq_f_g_leq50a,"v4_leq_F_50A")
##                -999 bad good <NA>     
## [1,] No. cases 467  6   2    1068 1543
## [2,] Percent   30.3 0.4 0.1  69.2 100

50B Impact (ordinal [0,1,2,3], v4_leq_F_50B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq50e_fstand_eltern,v4_con$v4_leq_f_g_leq50e,"v4_leq_F_50B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 466  2   3   3   1   1068 1543
## [2,] Percent   30.2 0.1 0.2 0.2 0.1 69.2 100

Create dataset

v4_leq_F<-data.frame(v4_leq_F_43A,v4_leq_F_43B,v4_leq_F_44A,v4_leq_F_44B,v4_leq_F_45A,
                     v4_leq_F_45B,v4_leq_F_46A,v4_leq_F_46B,v4_leq_F_47A,v4_leq_F_47B,
                     v4_leq_F_48A,v4_leq_F_48B,v4_leq_F_49A,v4_leq_F_49B,v4_leq_F_50A,
                     v4_leq_F_50B)

Parenting

51. “Change in child care arrangements”

51A Nature (dichotomous [“good”,“bad”], v4_leq_G_51A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq51a_kindbetr,v4_con$v4_leq_f_g_leq51a,"v4_leq_G_51A")
##                -999 bad good <NA>     
## [1,] No. cases 468  2   5    1068 1543
## [2,] Percent   30.3 0.1 0.3  69.2 100

51B Impact (ordinal [0,1,2,3], v4_leq_G_51B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq51e_kindbetr,v4_con$v4_leq_f_g_leq51e,"v4_leq_G_51B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 467  2   2   3   1   1068 1543
## [2,] Percent   30.3 0.1 0.1 0.2 0.1 69.2 100

52. “Conflicts with spouse or partner about parenting”

52A Nature (dichotomous [“good”,“bad”], v4_leq_G_52A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq52a_konf_eschaft,v4_con$v4_leq_f_g_leq52a,"v4_leq_G_52A")
##                -999 bad good <NA>     
## [1,] No. cases 463  10  2    1068 1543
## [2,] Percent   30   0.6 0.1  69.2 100

52B Impact (ordinal [0,1,2,3], v4_leq_G_52B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq52e_konf_eschaft,v4_con$v4_leq_f_g_leq52e,"v4_leq_G_52B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 462  3   4   5   1   1068 1543
## [2,] Percent   29.9 0.2 0.3 0.3 0.1 69.2 100

53. “Conflicts with child’s grandparents (or other important person) about parenting”

53A Nature (dichotomous [“good”,“bad”], v4_leq_G_53A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq53a_konf_geltern,v4_con$v4_leq_f_g_leq53a,"v4_leq_G_53A")
##                -999 bad <NA>     
## [1,] No. cases 470  5   1068 1543
## [2,] Percent   30.5 0.3 69.2 100

53B Impact (ordinal [0,1,2,3], v4_leq_G_53B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq53e_konf_geltern,v4_con$v4_leq_f_g_leq53e,"v4_leq_G_53B")
##                -999 0   1   3   <NA>     
## [1,] No. cases 469  1   3   2   1068 1543
## [2,] Percent   30.4 0.1 0.2 0.1 69.2 100

54. “Taking on full responsibility for parenting as a single parent”

54A Nature (dichotomous [“good”,“bad”], v4_leq_G_54A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq54a_alleinerz,v4_con$v4_leq_f_g_leq54a,"v4_leq_G_54A")
##                -999 bad good <NA>     
## [1,] No. cases 473  1   1    1068 1543
## [2,] Percent   30.7 0.1 0.1  69.2 100

54B Impact (ordinal [0,1,2,3], v4_leq_G_54B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq54e_alleinerz,v4_con$v4_leq_f_g_leq54e,"v4_leq_G_54B")
##                -999 0   1   <NA>     
## [1,] No. cases 472  1   2   1068 1543
## [2,] Percent   30.6 0.1 0.1 69.2 100

55. “Custody battles with former spouse or partner”

55A Nature (dichotomous [“good”,“bad”], v4_leq_G_55A)

v4_leq_a_recode(v4_clin$v4_leq_f_g_leq55a_sorgerecht,v4_con$v4_leq_f_g_leq55a,"v4_leq_G_55A")
##                -999 bad good <NA>     
## [1,] No. cases 469  5   1    1068 1543
## [2,] Percent   30.4 0.3 0.1  69.2 100

55B Impact (ordinal [0,1,2,3], v4_leq_G_55B)

v4_leq_b_recode(v4_clin$v4_leq_f_g_leq55e_sorgerecht,v4_con$v4_leq_f_g_leq55e,"v4_leq_G_55B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 468  2   1   3   1   1068 1543
## [2,] Percent   30.3 0.1 0.1 0.2 0.1 69.2 100

Create dataset

v4_leq_G<-data.frame(v4_leq_G_51A,v4_leq_G_51B,v4_leq_G_52A,v4_leq_G_52B,v4_leq_G_53A,
                     v4_leq_G_53B,v4_leq_G_54A,v4_leq_G_54B,v4_leq_G_55A,v4_leq_G_55B)

Personal or social

56. “Major personal achievement”

56A Nature (dichotomous [“good”,“bad”], v4_leq_H_56A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq56a_pers_leistung,v4_con$v4_leq_h_leq56a,"v4_leq_H_56A")
##                -999 bad good <NA>     
## [1,] No. cases 392  3   80   1068 1543
## [2,] Percent   25.4 0.2 5.2  69.2 100

56B Impact (ordinal [0,1,2,3], v4_leq_H_56B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq56e_pers_leistung,v4_con$v4_leq_h_leq56e,"v4_leq_H_56B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 391  7   14  33  30  1068 1543
## [2,] Percent   25.3 0.5 0.9 2.1 1.9 69.2 100

57. “Major decision regarding your immediate future”

57A Nature (dichotomous [“good”,“bad”], v4_leq_H_57A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq57a_wicht_entsch,v4_con$v4_leq_h_leq57a,"v4_leq_H_57A")
##                -999 bad good <NA>     
## [1,] No. cases 370  15  90   1068 1543
## [2,] Percent   24   1   5.8  69.2 100

57B Impact (ordinal [0,1,2,3], v4_leq_H_57B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq57e_wicht_entsch,v4_con$v4_leq_h_leq57e,"v4_leq_H_57B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 368  6   8   48  45  1068 1543
## [2,] Percent   23.8 0.4 0.5 3.1 2.9 69.2 100

58. “Change in your personal habits (your dress, life-style, hobbies, etc.)”

58A Nature (dichotomous [“good”,“bad”], v4_leq_H_58A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq58a_pers_gewohn,v4_con$v4_leq_h_leq58a,"v4_leq_H_58A")
##                -999 bad good <NA>     
## [1,] No. cases 404  15  56   1068 1543
## [2,] Percent   26.2 1   3.6  69.2 100

58B Impact (ordinal [0,1,2,3], v4_leq_H_58B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq58e_pers_gewohn,v4_con$v4_leq_h_leq58e,"v4_leq_H_58B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 402  5   17  30  21  1068 1543
## [2,] Percent   26.1 0.3 1.1 1.9 1.4 69.2 100

59. “Change in your religious beliefs”

59A Nature (dichotomous [“good”,“bad”], v4_leq_H_59A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq59a_relig_ueberz,v4_con$v4_leq_h_leq59a,"v4_leq_H_59A")
##                -999 bad good <NA>     
## [1,] No. cases 459  1   15   1068 1543
## [2,] Percent   29.7 0.1 1    69.2 100

59B Impact (ordinal [0,1,2,3], v4_leq_H_59B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq59e_relig_ueberz,v4_con$v4_leq_h_leq59e,"v4_leq_H_59B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 458  4   3   6   4   1068 1543
## [2,] Percent   29.7 0.3 0.2 0.4 0.3 69.2 100

60. “Change in your political beliefs”

60A Nature (dichotomous [“good”,“bad”], v4_leq_H_60A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq60a_pol_ansichten,v4_clin$v4_leq_h_leq60a,"v4_leq_H_60A")
## Warning in (is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con != 2) &
## is.na(leq_con_old_name) == : longer object length is not a multiple of
## shorter object length
## Warning in (is.na(v4_itm_leq_chk_con) | v4_itm_leq_chk_con != 2) &
## is.na(leq_con_old_name): longer object length is not a multiple of shorter
## object length
##                -999 bad good <NA>     
## [1,] No. cases 453  7   15   1068 1543
## [2,] Percent   29.4 0.5 1    69.2 100

60B Impact (ordinal [0,1,2,3], v4_leq_H_60B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq60e_pol_ansichten,v4_con$v4_leq_h_leq60e,"v4_leq_H_60B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 452  5   6   6   6   1068 1543
## [2,] Percent   29.3 0.3 0.4 0.4 0.4 69.2 100

61. “Loss or damage of personal property”

61A Nature (dichotomous [“good”,“bad”], v4_leq_H_61A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq61a_pers_eigent,v4_con$v4_leq_h_leq61a,"v4_leq_H_61A")
##                -999 bad good <NA>     
## [1,] No. cases 448  25  2    1068 1543
## [2,] Percent   29   1.6 0.1  69.2 100

61B Impact (ordinal [0,1,2,3], v4_leq_H_61B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq61e_pers_eigent,v4_con$v4_leq_h_leq61e,"v4_leq_H_61B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 446  5   10  6   8   1068 1543
## [2,] Percent   28.9 0.3 0.6 0.4 0.5 69.2 100

62. “Took a vacation”

62A Nature (dichotomous [“good”,“bad”], v4_leq_H_62A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq62a_erholungsurl,v4_con$v4_leq_h_leq62a,"v4_leq_H_62A")
##                -999 bad good <NA>     
## [1,] No. cases 369  1   105  1068 1543
## [2,] Percent   23.9 0.1 6.8  69.2 100

62B Impact (ordinal [0,1,2,3], v4_leq_H_62B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq62e_erholungsurl,v4_con$v4_leq_h_leq62e,"v4_leq_H_62B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 368  4   20  41  42  1068 1543
## [2,] Percent   23.8 0.3 1.3 2.7 2.7 69.2 100

63. “Took a trip other than a vacation”

63A Nature (dichotomous [“good”,“bad”], v4_leq_H_63A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq63a_reise_andere,v4_con$v4_leq_h_leq63a,"v4_leq_H_63A")
##                -999 bad good <NA>     
## [1,] No. cases 430  2   43   1068 1543
## [2,] Percent   27.9 0.1 2.8  69.2 100

63B Impact (ordinal [0,1,2,3], v4_leq_H_63B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq63e_reise_andere,v4_con$v4_leq_h_leq63e,"v4_leq_H_63B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 429  3   12  14  17  1068 1543
## [2,] Percent   27.8 0.2 0.8 0.9 1.1 69.2 100

64. “Change in family get-togethers”

64A Nature (dichotomous [“good”,“bad”], v4_leq_H_64A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq64a_familientreff,v4_con$v4_leq_h_leq64a,"v4_leq_H_64A")
##                -999 bad good <NA>     
## [1,] No. cases 439  6   30   1068 1543
## [2,] Percent   28.5 0.4 1.9  69.2 100

64B Impact (ordinal [0,1,2,3], v4_leq_H_64B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq64e_familientreff,v4_con$v4_leq_h_leq64e,"v4_leq_H_64B")
##                -999 0   1   2  3   <NA>     
## [1,] No. cases 438  3   9   15 10  1068 1543
## [2,] Percent   28.4 0.2 0.6 1  0.6 69.2 100

65. “Change in your social activities (clubs, movies, visiting)”

65A Nature (dichotomous [“good”,“bad”], v4_leq_H_65A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq65a_ges_unternehm,v4_con$v4_leq_h_leq65a,"v4_leq_H_65A")
##                -999 bad good <NA>     
## [1,] No. cases 417  16  42   1068 1543
## [2,] Percent   27   1   2.7  69.2 100

65B Impact (ordinal [0,1,2,3], v4_leq_H_65B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq65e_ges_unternehm,v4_con$v4_leq_h_leq65e,"v4_leq_H_65B")
##                -999 0   1  2  3   <NA>     
## [1,] No. cases 416  1   16 31 11  1068 1543
## [2,] Percent   27   0.1 1  2  0.7 69.2 100

66. “Made new friends”

66A Nature (dichotomous [“good”,“bad”], v4_leq_H_66A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq66a_neue_freunds,v4_con$v4_leq_h_leq66a,"v4_leq_H_66A")
##                -999 bad good <NA>     
## [1,] No. cases 384  1   90   1068 1543
## [2,] Percent   24.9 0.1 5.8  69.2 100

66B Impact (ordinal [0,1,2,3], v4_leq_H_66B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq66e_neue_freunds,v4_con$v4_leq_h_leq66e,"v4_leq_H_66B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 383  4   27  37  24  1068 1543
## [2,] Percent   24.8 0.3 1.7 2.4 1.6 69.2 100

67. “Broke up with a friend”

67A Nature (dichotomous [“good”,“bad”], v4_leq_H_67A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq67a_ende_freunds,v4_con$v4_leq_h_leq67a,"v4_leq_H_67A")
##                -999 bad good <NA>     
## [1,] No. cases 438  21  16   1068 1543
## [2,] Percent   28.4 1.4 1    69.2 100

67B Impact (ordinal [0,1,2,3], v4_leq_H_67B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq67e_ende_freunds,v4_con$v4_leq_h_leq67e,"v4_leq_H_67B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 437  2   13  13  10  1068 1543
## [2,] Percent   28.3 0.1 0.8 0.8 0.6 69.2 100

68. “Acquired or lost a pet”

68A Nature (dichotomous [“good”,“bad”], v4_leq_H_68A)

v4_leq_a_recode(v4_clin$v4_leq_h_leq68a_haustier,v4_con$v4_leq_h_leq68a,"v4_leq_H_68A")
##                -999 bad good <NA>     
## [1,] No. cases 451  9   15   1068 1543
## [2,] Percent   29.2 0.6 1    69.2 100

68B Impact (ordinal [0,1,2,3], v4_leq_H_68B)

v4_leq_b_recode(v4_clin$v4_leq_h_leq68e_haustier,v4_con$v4_leq_h_leq68e,"v4_leq_H_68B")
##                -999 0   1   2   3  <NA>     
## [1,] No. cases 450  2   2   6   15 1068 1543
## [2,] Percent   29.2 0.1 0.1 0.4 1  69.2 100

Create dataset

v4_leq_H<-data.frame(v4_leq_H_56A,v4_leq_H_56B,v4_leq_H_57A,v4_leq_H_57B,v4_leq_H_58A,
                     v4_leq_H_58B,v4_leq_H_59A,v4_leq_H_59B,v4_leq_H_60A,v4_leq_H_60B,
                     v4_leq_H_61A,v4_leq_H_61B,v4_leq_H_62A,v4_leq_H_62B,v4_leq_H_63A,
                     v4_leq_H_63B,v4_leq_H_64A,v4_leq_H_64B,v4_leq_H_65A,v4_leq_H_65B,
                     v4_leq_H_66A,v4_leq_H_66B,v4_leq_H_67A,v4_leq_H_67B,v4_leq_H_68A,
                     v4_leq_H_68B)

Financial

69. “Major change in finances (increased or decreased income)”

69A Nature (dichotomous [“good”,“bad”], v4_leq_I_69A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq69a_finanz_sit,v4_con$v4_leq_i_j_k_leq69a,"v4_leq_I_69A")
##                -999 bad good <NA>     
## [1,] No. cases 363  52  60   1068 1543
## [2,] Percent   23.5 3.4 3.9  69.2 100

69B Impact (ordinal [0,1,2,3], v4_leq_I_69B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq69e_finanz_sit,v4_con$v4_leq_i_j_k_leq69e,"v4_leq_I_69B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 362  4   24  41  44  1068 1543
## [2,] Percent   23.5 0.3 1.6 2.7 2.9 69.2 100

70. “Took on a moderate purchase, such as TV, car, freezer, etc.”

70A Nature (dichotomous [“good”,“bad”], v4_leq_I_70A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq70a_finanz_verpfl,v4_con$v4_leq_i_j_k_leq70a,"v4_leq_I_70A")
##                -999 bad good <NA>     
## [1,] No. cases 443  14  18   1068 1543
## [2,] Percent   28.7 0.9 1.2  69.2 100

70B Impact (ordinal [0,1,2,3], v4_leq_I_70B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq70e_finanz_verpfl,v4_con$v4_leq_i_j_k_leq70e,"v4_leq_I_70B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 442  4   13  11  5   1068 1543
## [2,] Percent   28.6 0.3 0.8 0.7 0.3 69.2 100

71. “Took on a major purchase or a mortgage loan, such as a home, business, property, etc”

71A Nature (dichotomous [“good”,“bad”], v4_leq_I_71A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq71a_hypothek,v4_con$v4_leq_i_j_k_leq71a,"v4_leq_I_71A")
##                -999 bad good <NA>     
## [1,] No. cases 467  5   3    1068 1543
## [2,] Percent   30.3 0.3 0.2  69.2 100

71B Impact (ordinal [0,1,2,3], v4_leq_I_71B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq71e_hypothek,v4_con$v4_leq_i_j_k_leq71e,"v4_leq_I_71B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 466  2   1   2   4   1068 1543
## [2,] Percent   30.2 0.1 0.1 0.1 0.3 69.2 100

72. “Experienced a foreclosure on a mortgage or loan”

72A Nature (dichotomous [“good”,“bad”], v4_leq_I_72A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq72a_hypoth_kuend,v4_con$v4_leq_i_j_k_leq72a,"v4_leq_I_72A")
##                -999 bad good <NA>     
## [1,] No. cases 469  2   4    1068 1543
## [2,] Percent   30.4 0.1 0.3  69.2 100

72B Impact (ordinal [0,1,2,3], v4_leq_I_72B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq72e_hypoth_kuend,v4_con$v4_leq_i_j_k_leq72e,"v4_leq_I_72B")
##                -999 0   2   3   <NA>     
## [1,] No. cases 468  2   2   3   1068 1543
## [2,] Percent   30.3 0.1 0.1 0.2 69.2 100

73. “Credit rating difficulties”

73A Nature (dichotomous [“good”,“bad”], v4_leq_I_73A)

v4_leq_a_recode(v4_clin$v4_leq_i_j_k_leq73a_kreditwuerdk,v4_con$v4_leq_i_j_k_leq73a,"v4_leq_I_73A")
##                -999 bad good <NA>     
## [1,] No. cases 458  15  2    1068 1543
## [2,] Percent   29.7 1   0.1  69.2 100

73B Impact (ordinal [0,1,2,3], v4_leq_I_73B)

v4_leq_b_recode(v4_clin$v4_leq_i_j_k_leq73e_kreditwuerdk,v4_con$v4_leq_i_j_k_leq73e,"v4_leq_I_73B")
##                -999 0   1   2   3   <NA>     
## [1,] No. cases 457  2   8   5   3   1068 1543
## [2,] Percent   29.6 0.1 0.5 0.3 0.2 69.2 100

Create dataset

v4_leq_I<-data.frame(v4_leq_I_69A,v4_leq_I_69B,v4_leq_I_70A,v4_leq_I_70B,v4_leq_I_71A,
                     v4_leq_I_71B,v4_leq_I_72A,v4_leq_I_72B,v4_leq_I_73A,v4_leq_I_73B)

WHOQOL-BREF

For explanation, please refer to the section in Visit 1

1. “How would you rate your quality of life?” (ordinal [1,2,3,4,5], v4_whoqol_itm1)

v4_quol_recode(v4_clin$v4_whoqol_bref_who1_lebensqualitaet,v4_con$v4_whoqol_bref_who1,"v4_whoqol_itm1",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 9   39  149 239  74  1033 1543
## [2,] Percent   0.6 2.5 9.7 15.5 4.8 66.9 100

2. “How satisfied are you with your health? (ordinal [1,2,3,4,5], v4_whoqol_itm2)”

v4_quol_recode(v4_clin$v4_whoqol_bref_who2_gesundheit,v4_con$v4_whoqol_bref_who2,"v4_whoqol_itm2",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 19  106 126 206  53  1033 1543
## [2,] Percent   1.2 6.9 8.2 13.4 3.4 66.9 100

3. “To what extent do you feel that physical pain prevents you from doing what you need to do?” (ordinal [1,2,3,4,5], v4_whoqol_itm3)

Coding reversed so that higher scores mean less impairment by pain.

v4_quol_recode(v4_clin$v4_whoqol_bref_who3_schmerzen,v4_con$v4_whoqol_bref_who3,"v4_whoqol_itm3",1)
##                1   2   3   4   5    NA's     
## [1,] No. cases 5   35  56  108 299  1040 1543
## [2,] Percent   0.3 2.3 3.6 7   19.4 67.4 100

4. “How much do you need any medical treatment to function in your daily life? (ordinal [1,2,3,4,5], v4_whoqol_itm4)”

Coding reversed so that higher scores mean less dependence on medical treatment.

v4_quol_recode(v4_clin$v4_whoqol_bref_who4_med_behand,v4_con$v4_whoqol_bref_who4,"v4_whoqol_itm4",1)
##                1   2   3   4   5   NA's     
## [1,] No. cases 58  113 83  118 128 1043 1543
## [2,] Percent   3.8 7.3 5.4 7.6 8.3 67.6 100

5. “How much do you enjoy life?” (ordinal [1,2,3,4,5], v4_whoqol_itm5)

v4_quol_recode(v4_clin$v4_whoqol_bref_who5_lebensgenuss,v4_con$v4_whoqol_bref_who5,"v4_whoqol_itm5",0)
##                1  2   3    4    5   NA's     
## [1,] No. cases 16 60  157  213  57  1040 1543
## [2,] Percent   1  3.9 10.2 13.8 3.7 67.4 100

6. “To what extent do ou feel your life to be meaningful?” (ordinal [1,2,3,4,5], v4_whoqol_itm6)

v4_quol_recode(v4_clin$v4_whoqol_bref_who6_lebenssinn,v4_con$v4_whoqol_bref_who6,"v4_whoqol_itm6",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 19  53  115 195  114 1047 1543
## [2,] Percent   1.2 3.4 7.5 12.6 7.4 67.9 100

7. “How well are you able to concentrate?” (ordinal [1,2,3,4,5], v4_whoqol_itm7)

v4_quol_recode(v4_clin$v4_whoqol_bref_who7_konzentration,v4_con$v4_whoqol_bref_who7,"v4_whoqol_itm7",0)
##                1   2   3    4    5   NA's     
## [1,] No. cases 11  79  187  194  35  1037 1543
## [2,] Percent   0.7 5.1 12.1 12.6 2.3 67.2 100

8. “How safe do you feel in your daily life?” (ordinal [1,2,3,4,5], v4_whoqol_itm8)

v4_quol_recode(v4_clin$v4_whoqol_bref_who8_sicherheit,v4_con$v4_whoqol_bref_who8,"v4_whoqol_itm8",0)
##                1   2   3   4    5  NA's     
## [1,] No. cases 12  35  143 238  77 1038 1543
## [2,] Percent   0.8 2.3 9.3 15.4 5  67.3 100

9. “How healthy is your physical environment?” (ordinal [1,2,3,4,5], v4_whoqol_itm9)

v4_quol_recode(v4_clin$v4_whoqol_bref_who9_umweltbed,v4_con$v4_whoqol_bref_who9,"v4_whoqol_itm9",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 7   17  101 265  116 1037 1543
## [2,] Percent   0.5 1.1 6.5 17.2 7.5 67.2 100

10. “Do you have enough energy for everyday life?” (ordinal [1,2,3,4,5], v4_whoqol_itm10)

v4_quol_recode(v4_clin$v4_whoqol_bref_who10_energie,v4_con$v4_whoqol_bref_who10,"v4_whoqol_itm10",0)
##                1  2   3   4    5   NA's     
## [1,] No. cases 15 41  144 205  101 1037 1543
## [2,] Percent   1  2.7 9.3 13.3 6.5 67.2 100

11. “Are you able to accept your bodily appearance?” (ordinal [1,2,3,4,5], v4_whoqol_itm11)

v4_quol_recode(v4_clin$v4_whoqol_bref_who11_aussehen,v4_con$v4_whoqol_bref_who11,"v4_whoqol_itm11",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 12  41  136 203  112 1039 1543
## [2,] Percent   0.8 2.7 8.8 13.2 7.3 67.3 100

12. “Have you enough money to meet your needs?” (ordinal [1,2,3,4,5], v4_whoqol_itm12)

v4_quol_recode(v4_clin$v4_whoqol_bref_who12_genug_geld,v4_con$v4_whoqol_bref_who12,"v4_whoqol_itm12",0)
##                1  2   3   4    5   NA's     
## [1,] No. cases 15 78  133 174  106 1037 1543
## [2,] Percent   1  5.1 8.6 11.3 6.9 67.2 100

13. “How available to you is the information that you need in your day-to-day life?” (ordinal [1,2,3,4,5], v4_whoqol_itm13)

v4_quol_recode(v4_clin$v4_whoqol_bref_who13_infozugang,v4_con$v4_whoqol_bref_who13,"v4_whoqol_itm13",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 4   12  66  206  216 1039 1543
## [2,] Percent   0.3 0.8 4.3 13.4 14  67.3 100

14. “To what extent do you have the opportunity for leisure activities?” (ordinal [1,2,3,4,5], v4_whoqol_itm14)

v4_quol_recode(v4_clin$v4_whoqol_bref_who14_freizeitaktiv,v4_con$v4_whoqol_bref_who14,"v4_whoqol_itm14",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 5   35  131 196  138 1038 1543
## [2,] Percent   0.3 2.3 8.5 12.7 8.9 67.3 100

15. “How well are you able to get around? (ordinal [1,2,3,4,5], v4_whoqol_itm15)”

v4_quol_recode(v4_clin$v4_whoqol_bref_who15_fortbewegung,v4_con$v4_whoqol_bref_who15,"v4_whoqol_itm15",0)
##                1   2   3  4   5    NA's     
## [1,] No. cases 3   28  92 200 182  1038 1543
## [2,] Percent   0.2 1.8 6  13  11.8 67.3 100

16. “How satisfied are you with your sleep?” (ordinal [1,2,3,4,5], v4_whoqol_itm16)

v4_quol_recode(v4_clin$v4_whoqol_bref_who16_schlaf,v4_con$v4_whoqol_bref_who16,"v4_whoqol_itm16",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 28  71  81  250  81  1032 1543
## [2,] Percent   1.8 4.6 5.2 16.2 5.2 66.9 100

17. “How satisfied are you with your ability to perform your daily living activities?” (ordinal [1,2,3,4,5], v4_whoqol_itm17)

v4_quol_recode(v4_clin$v4_whoqol_bref_who17_alltag,v4_con$v4_whoqol_bref_who17,"v4_whoqol_itm17",0)
##                1  2   3   4    5   NA's     
## [1,] No. cases 16 78  96  225  96  1032 1543
## [2,] Percent   1  5.1 6.2 14.6 6.2 66.9 100

18. “How satisfied are you with your capacity for work?” (ordinal [1,2,3,4,5], v4_whoqol_itm18)

v4_quol_recode(v4_clin$v4_whoqol_bref_who18_arbeitsfhgk,v4_con$v4_whoqol_bref_who18,"v4_whoqol_itm18",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 44  97  109 190  68  1035 1543
## [2,] Percent   2.9 6.3 7.1 12.3 4.4 67.1 100

19. “How satisfied are you with yourself?” (ordinal [1,2,3,4,5], v4_whoqol_itm19)

v4_quol_recode(v4_clin$v4_whoqol_bref_who19_selbstzufried,v4_con$v4_whoqol_bref_who19,"v4_whoqol_itm19",0)
##                1   2   3   4    5  NA's     
## [1,] No. cases 18  75  124 230  62 1034 1543
## [2,] Percent   1.2 4.9 8   14.9 4  67   100

20. “How satisfied are you with your personal relationships?” (ordinal [1,2,3,4,5], v4_whoqol_itm20)

v4_quol_recode(v4_clin$v4_whoqol_bref_who20_pers_bezieh,v4_con$v4_whoqol_bref_who20,"v4_whoqol_itm20",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 12  50  115 240  87  1039 1543
## [2,] Percent   0.8 3.2 7.5 15.6 5.6 67.3 100

21. “How satisfied are you with your sex life?” (ordinal [1,2,3,4,5], v4_whoqol_itm21)

v4_quol_recode(v4_clin$v4_whoqol_bref_who21_sexualleben,v4_con$v4_whoqol_bref_who21,"v4_whoqol_itm21",0)
##                1   2   3    4   5  NA's     
## [1,] No. cases 56  81  156  147 61 1042 1543
## [2,] Percent   3.6 5.2 10.1 9.5 4  67.5 100

22. “How satisfied are you with the support you get from your friends?” (ordinal [1,2,3,4,5], v4_whoqol_itm22)

v4_quol_recode(v4_clin$v4_whoqol_bref_who22_freunde,v4_con$v4_whoqol_bref_who22,"v4_whoqol_itm22",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 9   39  125 234  102 1034 1543
## [2,] Percent   0.6 2.5 8.1 15.2 6.6 67   100

23. “How satisfied are you with the conditions of your living place?” (ordinal [1,2,3,4,5], v4_whoqol_itm23)

v4_quol_recode(v4_clin$v4_whoqol_bref_who23_wohnbeding,v4_con$v4_whoqol_bref_who23,"v4_whoqol_itm23",0)
##                1   2   3   4    5   NA's     
## [1,] No. cases 11  43  79  229  147 1034 1543
## [2,] Percent   0.7 2.8 5.1 14.8 9.5 67   100

24. “How satisfied are you with your access to health services?” (ordinal [1,2,3,4,5], v4_whoqol_itm24)

v4_quol_recode(v4_clin$v4_whoqol_bref_who24_gesundhdiens,v4_con$v4_whoqol_bref_who24,"v4_whoqol_itm24",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 11  18  56  265  161  1032 1543
## [2,] Percent   0.7 1.2 3.6 17.2 10.4 66.9 100

25. “How satisfied are you with your mode of transportation?” (ordinal [1,2,3,4,5], v4_whoqol_itm25)

v4_quol_recode(v4_clin$v4_whoqol_bref_who25_transport,v4_con$v4_whoqol_bref_who25,"v4_whoqol_itm25",0)
##                1   2   3   4    5    NA's     
## [1,] No. cases 11  24  54  257  164  1033 1543
## [2,] Percent   0.7 1.6 3.5 16.7 10.6 66.9 100

26. “How often do you have negative feelings, such as blue mood, despair, anxiety, depression?” (ordinal [1,2,3,4,5], v4_whoqol_itm26)

Coding reversed so that higher scores mean symptoms less often.

v4_quol_recode(v4_clin$v4_whoqol_bref_who26_neg_gefuehle,v4_con$v4_whoqol_bref_who26,"v4_whoqol_itm26",1)
##                1  2   3   4    5   NA's     
## [1,] No. cases 16 81  117 211  83  1035 1543
## [2,] Percent   1  5.2 7.6 13.7 5.4 67.1 100

WHOQOL-BREF domain scores

Here, domain scores for Physical Health, Psychological, Social relationships and Environment are calculated from the WHOQOL single items, according to the scoring instructions given in Angermeyer et al. (2000).

Global (continuous [4-20],v4_whoqol_dom_glob)

v4_whoqol_dom_glob_df<-data.frame(as.numeric(v4_whoqol_itm1),as.numeric(v4_whoqol_itm2))

v4_who_glob_no_nas<-rowSums(is.na(v4_whoqol_dom_glob_df))

v4_whoqol_dom_glob<-ifelse((v4_who_glob_no_nas==0) | (v4_who_glob_no_nas==1), 
                            rowMeans(v4_whoqol_dom_glob_df,na.rm=T)*4,NA)

v4_whoqol_dom_glob<-round(v4_whoqol_dom_glob,2)

summary(v4_whoqol_dom_glob)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.00   13.95   16.00   20.00    1031

Physical Health (continuous [4-20],v4_whoqol_dom_phys)

v4_whoqol_dom_phys_df<-data.frame(as.numeric(v4_whoqol_itm3),as.numeric(v4_whoqol_itm10),as.numeric(v4_whoqol_itm16),as.numeric(v4_whoqol_itm15),as.numeric(v4_whoqol_itm17),as.numeric(v4_whoqol_itm4),as.numeric(v4_whoqol_itm18))

v4_who_phys_no_nas<-rowSums(is.na(v4_whoqol_dom_phys_df))

v4_whoqol_dom_phys<-ifelse((v4_who_phys_no_nas==0) | (v4_who_phys_no_nas==1), 
                            rowMeans(v4_whoqol_dom_phys_df,na.rm=T)*4,NA)

v4_whoqol_dom_phys<-round(v4_whoqol_dom_phys,2)

summary(v4_whoqol_dom_phys)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    5.71   12.60   14.86   14.72   16.57   20.00    1037

Psychological (continuous [4-20],v4_whoqol_dom_psy)

v4_whoqol_dom_psy_df<-data.frame(as.numeric(v4_whoqol_itm5),as.numeric(v4_whoqol_itm7),as.numeric(v4_whoqol_itm19),as.numeric(v4_whoqol_itm11),as.numeric(v4_whoqol_itm26),as.numeric(v4_whoqol_itm6))

v4_who_psy_no_nas<-rowSums(is.na(v4_whoqol_dom_psy_df))

v4_whoqol_dom_psy<-ifelse((v4_who_psy_no_nas==0) | (v4_who_psy_no_nas==1), 
                            rowMeans(v4_whoqol_dom_psy_df,na.rm=T)*4,NA)

v4_whoqol_dom_psy<-round(v4_whoqol_dom_psy,2)

summary(v4_whoqol_dom_psy)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.10   16.00   20.00    1039

Social relationships (continuous [4-20],v4_whoqol_dom_soc)

v4_whoqol_dom_soc_df<-data.frame(as.numeric(v4_whoqol_itm20),as.numeric(v4_whoqol_itm22),as.numeric(v4_whoqol_itm21))

v4_who_soc_no_nas<-rowSums(is.na(v4_whoqol_dom_soc_df))

v4_whoqol_dom_soc<-ifelse((v4_who_soc_no_nas==0) | (v4_who_soc_no_nas==1), 
                            rowMeans(v4_whoqol_dom_soc_df,na.rm=T)*4,NA)

v4_whoqol_dom_soc<-round(v4_whoqol_dom_soc,2)

summary(v4_whoqol_dom_soc)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    4.00   12.00   14.67   14.10   16.00   20.00    1035

Environment (continuous [4-20],v4_whoqol_dom_env)

v4_whoqol_dom_env_df<-data.frame(as.numeric(v4_whoqol_itm8),as.numeric(v4_whoqol_itm23),as.numeric(v4_whoqol_itm12),as.numeric(v4_whoqol_itm24),as.numeric(v4_whoqol_itm13),as.numeric(v4_whoqol_itm14),as.numeric(v4_whoqol_itm9),as.numeric(v4_whoqol_itm25))

v4_who_env_no_nas<-rowSums(is.na(v4_whoqol_dom_env_df))

v4_whoqol_dom_env<-ifelse((v4_who_env_no_nas==0) | (v4_who_env_no_nas==1), 
                            rowMeans(v4_whoqol_dom_env_df,na.rm=T)*4,NA)

v4_whoqol_dom_env<-round(v4_whoqol_dom_env,2)

summary(v4_whoqol_dom_env)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    7.00   14.00   15.50   15.61   17.50   20.00    1038

Create dataset

v4_whoqol<-data.frame(v4_whoqol_itm1,v4_whoqol_itm2,v4_whoqol_itm3,v4_whoqol_itm4,
                      v4_whoqol_itm5,v4_whoqol_itm6,v4_whoqol_itm7,v4_whoqol_itm8,
                      v4_whoqol_itm9,v4_whoqol_itm10,v4_whoqol_itm11,v4_whoqol_itm12,
                      v4_whoqol_itm13,v4_whoqol_itm14,v4_whoqol_itm15,v4_whoqol_itm16,
                      v4_whoqol_itm17,v4_whoqol_itm18,v4_whoqol_itm19,v4_whoqol_itm20,
                      v4_whoqol_itm21,v4_whoqol_itm22,v4_whoqol_itm23,v4_whoqol_itm24,
                      v4_whoqol_itm25,v4_whoqol_itm26,v4_whoqol_dom_glob,
                      v4_whoqol_dom_phys,v4_whoqol_dom_psy,v4_whoqol_dom_soc,
                      v4_whoqol_dom_env)

Visit 4: Create dataframe

v4_df<-data.frame(v4_id,
                  v4_rec,
                  v4_clin_ill_ep,
                  v4_con_problems,
                  v4_dem,
                  v4_opcrit,
                  v4_leprcp,
                  v4_suic,
                  v4_med,
                  v4_subst,
                  v4_symp_panss,
                  v4_symp_ids_c,
                  v4_symp_ymrs,
                  v4_ill_sev,
                  v4_nrpsy,
                  v4_sf12,
                  v4_rlgn,
                  v4_med_adh,
                  v4_bdi2,
                  v4_asrm,
                  v4_mss,
                  v4_leq,
                  v4_whoqol)

Merge Visits

ctmp1<-merge(x=v1_df, y=v2_df, by.x="v1_id", by.y="v2_id", all.x=T)
ctmp2<-merge(x=ctmp1, y=v3_df, by.x="v1_id", by.y="v3_id", all.x=T)
phen<-merge(x=ctmp2, y=v4_df, by.x="v1_id", by.y="v4_id",all.x=T)

Analysis IDs

To simplify the process of data analysis and subject selection, we here provide the IDs of individuals that have been included in various biological analyses (e.g. all sample that were whole-genome genotyped have an ID in the column “gwas_id”).

GWAS analysis IDs (character, gwas_id)

1436 individuals contained in this dataset have been genotyped on the Illumina PsychChip (https://www.illumina.com/products/by-type/microarray-kits/infinium-psycharray.html).

IMPORTANT:
1. Some individuals will be removed during QC of genotype data. Therefore, discrepancies with the number of individuals in the genotype dataset may exist. 2. Related individuals remain in the latest genotype dataset. Exclude by yourself if neccessary.

## [1] 1457
#make a dataframe for all analysis ids
ids<-data.frame(v1_id)
ids<-merge(x=ids,y=gwas_id,all.x=T, by.x="v1_id",by.y="id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$gwas_id)==F))
## [1] 1436

SmallRNAome seq analysis IDs (character, v1_smRNAome_id)

The smallRNAomes of a total of 1361 individuals contained in this dataset were sequenced from biomaterial collected AT THE FIRST VISIT. The variable gives the names of the corresponding .fastq files. The dummy variables “v2_smRNAome_id”, v3_smRNAome_id" and “v4_epic_id” are also created below to enable to include data properly in the long format dataset.

## [1] 1361    2
#merge to dataframe ids 
ids<-merge(x=ids,y=v1_smRNAome_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_smRNAome_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_smRNAome_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_smRNAome_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_smRNAome_id)==F))
## [1] 1323

Epigenetic analysis IDs (character, v1_epic_id and v3_epic_id)

In this analysis, 96 biploar individuals were analyzed at wo measurement points (visit 1 and visit 3) using the Illumina EPIC array. The dummy variables “v2_epic_id” and “v4_epic_id” are also created below to enable to include data properly in the long dataset.

## [1] 96  2
#merge to dataframe ids 
ids<-merge(x=ids,y=v1_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_epic_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_epic_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_epic_id)==F))
## [1] 96
length(subset(ids$v1_id,is.na(ids$v3_epic_id)==F))
## [1] 96

RB1CC1 Sanger sequening analysis IDs (character, rb1cc1_id)

In this analysis, the RB1CC1 gene was sequenced in 63 clinical participants.

#merge to dataframe ids 
ids<-merge(x=ids,y=rb1cc1_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$rb1cc1_id)==F))
## [1] 63

Lexogene mRNA NGS analysis IDs (character, v1_lexo_id and v3_lexo_id)

In this analysis, the mRNA transcriptomes of 543 individuals contained in this dataset (539 from visit 1, 4 from visit 3) were sequenced. The dummy variables “v2_lexo_id” and “v4_lexo_id” are also created below to enable to include data properly in the long dataset.

#merge to dataframe ids
ids<-merge(x=ids,y=v1_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_lexo_seq_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_lexo_seq_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_lexo_id)==F))
## [1] 539
length(subset(ids$v1_id,is.na(ids$v3_lexo_id)==F))
## [1] 4

Proteomics analysis IDs (character, v1_prot_id, v2_prot_id, v3_prot_id, v4_prot_id)

In a pilot study, a plasma proteome profiling pipeline was applied to 220 PsyCourse participants. Of these, 74 were from visit 1, 37 from visit 2, 72 from visit 3, 36 from visit 4, and one from an extra study visit between regular visits. This last individual was excluded. We do not have analysis IDs from these individuals, if they are contained in the analysis, the respective field contains a “Y”.

## 
##  1  2  3  4 ZV 
## 74 37 72 36  1
#merge to dataframe ids
ids<-merge(x=ids,y=v1_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_prot_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_prot_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_prot_id)==F))
## [1] 74
length(subset(ids$v1_id,is.na(ids$v2_prot_id)==F))
## [1] 37
length(subset(ids$v1_id,is.na(ids$v3_prot_id)==F))
## [1] 72
length(subset(ids$v1_id,is.na(ids$v4_prot_id)==F))
## [1] 36

Protein profiling analysis IDs (character, v1_ab_prof_id, v2_ab_prof_id, v3_ab_prof_id, v4_ab_prof_id)

In a total of 222 PsyCourse individuals (212 from visit 1, 9 from visit 2, and 1 from visit 3), a selected panel of ~100 serum proteins was determined using a set of 155 antibodies in a high-throughput antibody-based assay. This suspension bead array technology enabled a multiplexed protein profiling of these proteins.

#merge to dataframe ids
ids<-merge(x=ids,y=v1_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_ab_prof_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_ab_prof_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_ab_prof_id)==F))
## [1] 212
length(subset(ids$v1_id,is.na(ids$v2_ab_prof_id)==F))
## [1] 9
length(subset(ids$v1_id,is.na(ids$v3_ab_prof_id)==F))
## [1] 1
length(subset(ids$v1_id,is.na(ids$v4_ab_prof_id)==F))
## [1] 0

Lipidomics profiling analysis IDs (character, v1_lip_id, v2_lip_id, v3_lip_id, v4_lip_id)

Plasma lipid profiles were measured for a total of 1040 PsyCourse individuals, 545 from visit 1, 351 from visit 2, 91 from visit 3, 52 from visit 4, and one from an extra study visit between regular visits. This last individual was excluded. We do not have analysis IDs from these individuals, if they are contained in the analysis, the respective field contains a “Y”. )

#merge to dataframe ids
ids<-merge(x=ids,y=v1_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v2_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v3_lip_id,all.x=T, by="v1_id")
ids<-merge(x=ids,y=v4_lip_id,all.x=T, by="v1_id")

#how many people in the dataset were analyzed?
length(subset(ids$v1_id,is.na(ids$v1_lip_id)==F))
## [1] 545
length(subset(ids$v1_id,is.na(ids$v2_lip_id)==F))
## [1] 351
length(subset(ids$v1_id,is.na(ids$v3_lip_id)==F))
## [1] 91
length(subset(ids$v1_id,is.na(ids$v4_lip_id)==F))
## [1] 52

Merge to get PsyCourse wide format dataset, version 3.1

Save PsyCourse 3.1 wide format dataset

save(psycrs3.1_wd, file="191018_v3.1_psycourse_wd.RData")

Write wide format .csv file

write.table(psycrs3.1_wd,file="191018_v3.1_psycourse_wd.csv", quote=F, row.names=F, col.names=T, sep="\t")

Create long format dataset

To create a long dataset, it has to be determimed which variables are assessd at one, two, three and four visits in the PsyCourse 3.1 dataset. Subsequently, one has to integrate variables that were measured at two or three measurement points with those assessed at four time points.*

For variables that were repeatedy measured at three points in time, dummy first measurement point variables were created, all coded -999, so that these can be treated as repeated measures.

Only four variables were measured at two times, and these are items on religion. These items do not assess change, but were only added at a later measurement point so that people who did not have the chance to complete it at the first measurement point could also be assessed (the questionnaire was introduced some time after the study had started). Below, these variables are collapsed into cross-sectional variables.

Get a list of variables measured one, two, three, or four times. These are identified by counting the variables that are similarly named after the “_" character.

#get variables names measured one time
crs<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==1))
length(crs)
## [1] 217
#get variables names measured two times
lng2<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==2))
length(lng2)
## [1] 4
#get variables names measured three times
lng3<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==3))
length(lng3)
## [1] 147
#get variables names measured four times
lng4<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==4))
length(lng4)
## [1] 409
#Check whether the sum of these variable is equal to the total number of variables in the wide dataset
length(c(crs,lng2,lng2,lng3,lng3,lng3,lng4,lng4,lng4,lng4))==dim(psycrs3.1_wd)[2]
## [1] TRUE

Variable names included in lng3 (measured only three times)

After inspectionThese variables and saw that variables that were asked on follow-up but not the first visit: For each variable name, create a “v1_” variable filled with -999.

#Modify items in lng3 so that each vector element has a "v1_" added in front of it
lng3_new_v1_varnames<-paste("v1",lng3, sep="_")

#Add new variables to psycrs3.1_wd and fill them with -999
psycrs3.1_wd[lng3_new_v1_varnames] <- -999

Variable names included in lng2 (measured only two times): religion variables (see introductory text above)

I had a look and these four variables are the religion variables, the questionnaire of which is asked at visit 1 but also at visit 4.

If a participant anwswered the questionnaire already in v1, use this data, if not and v4 data exisit and is not -999, use v4 data

psycrs3.1_wd$v1_rel_act<-ifelse(is.na(psycrs3.1_wd$v1_rel_act) & 
                                  is.na(psycrs3.1_wd$v4_rel_act)==F & 
                                    psycrs3.1_wd$v4_rel_act!=-999,psycrs3.1_wd$v4_rel_act,psycrs3.1_wd$v1_rel_act)

psycrs3.1_wd$v1_rel_chr<-ifelse(is.na(psycrs3.1_wd$v1_rel_chr) & 
                                  is.na(psycrs3.1_wd$v4_rel_chr)==F & 
                                    psycrs3.1_wd$v4_rel_chr!=-999,psycrs3.1_wd$v4_rel_chr,psycrs3.1_wd$v1_rel_chr)

psycrs3.1_wd$v1_rel_isl<-as.factor(ifelse(is.na(psycrs3.1_wd$v1_rel_isl) & 
                                            is.na(psycrs3.1_wd$v4_rel_isl)==F & 
                                    psycrs3.1_wd$v4_rel_isl!=-999,as.character(psycrs3.1_wd$v4_rel_isl),as.character(psycrs3.1_wd$v1_rel_isl)))

psycrs3.1_wd$v1_rel_oth<-as.factor(ifelse(is.na(psycrs3.1_wd$v1_rel_oth) & is.na(psycrs3.1_wd$v4_rel_oth)==F & 
                                    psycrs3.1_wd$v4_rel_oth!=-999,as.character(psycrs3.1_wd$v4_rel_oth),as.character(psycrs3.1_wd$v1_rel_oth)))

Remove variables v4_rel_act, v4_rel_chr, v4_rel_isl, v4_rel_oth from psycrs3.1_wd

psycrs3.1_wd$v4_rel_act<-NULL
psycrs3.1_wd$v4_rel_chr<-NULL
psycrs3.1_wd$v4_rel_isl<-NULL
psycrs3.1_wd$v4_rel_oth<-NULL

After the modifications done above, determine longitudinally measured (four time points) and cross-sectionally (less than four times) measured variables

#get variables names measured four times
lng4_cor<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==4))
length(lng4_cor)
## [1] 556
#get variables names measured three times
lng3_cor<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==3))
length(lng3_cor)
## [1] 0
#get variables names measured two times
lng2_cor<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==2))
length(lng2_cor)
## [1] 0
#get variables names measured one time
crs_cor<-names(subset(table(substring(names(psycrs3.1_wd),4)),table(substring(names(psycrs3.1_wd),4))==1))
length(crs_cor) 
## [1] 221
#Check whether the sum of these variable is equal to the total number of variables in the wide dataset
length(c(crs_cor,lng2_cor,lng2_cor,lng3_cor,lng3_cor,lng3_cor,lng4_cor,lng4_cor,lng4_cor,lng4_cor))==dim(psycrs3.1_wd)[2]
## [1] TRUE

Make a vector with all names of the longitdinally measured variables

lng4_cor_v1<-paste("v1",lng4_cor,sep="_")
lng4_cor_v2<-paste("v2",lng4_cor,sep="_")
lng4_cor_v3<-paste("v3",lng4_cor,sep="_")
lng4_cor_v4<-paste("v4",lng4_cor,sep="_")

names_lng<-c(lng4_cor_v1,lng4_cor_v2,lng4_cor_v3,lng4_cor_v4)

Format from wide to long

Split psycrs3.1_wd in longitudinal and cross-sectional variables

long<-subset(psycrs3.1_wd,select=names_lng)

#change names of longitudinally measured variables, so that visit info comes at the end
names(long)<-paste(substring(names(long),4),substr(names(long),2,2),sep=".")  

#sort dataframe
long<-long[,sort(names(long))]
dim(long)
## [1] 1543 2224
#create a dataframe with cross-sectionally measured variables
cross<-subset(psycrs3.1_wd,select=!(names(psycrs3.1_wd)%in%names_lng))
dim(cross)
## [1] 1543  221

Reunite long and cross

psycrs3.1_wd2<-cbind(cross,long)
dim(psycrs3.1_wd2) 
## [1] 1543 2445

Reshape dataframme from long to wide

IMPORTANT: column number 222, “visit” contains the time information

psycrs3.1_ln<-reshape(data=psycrs3.1_wd2,
                       direction="long",
                       varying=names(long),
                       timevar="visit",
                       sep=".")

dim(psycrs3.1_ln) 
## [1] 6172  779
#Remove the last column that contains only consective numbers for each time point, and can safely be removed
psycrs3.1_ln<-psycrs3.1_ln[,-779] 

#Is the number of rows four times that of the long dataframe? 
dim(psycrs3.1_ln)[1]==dim(psycrs3.1_wd2)[1]*4 
## [1] TRUE

Save PsyCourse 3.1 long format dataset

save(psycrs3.1_ln, file="191018_v3.1_psycourse_ln.RData")

Write long format .csv file

write.table(psycrs3.1_ln,file="191018_v3.1_psycourse_ln.csv", quote=F, row.names=F, col.names=T, sep="\t")

References

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  1. The ALDA scala should only be assessed in clinical participants with a diagnosis of bipolar disorder↩

  2. Self-reported weight is assessed at each study visit↩

  3. Data not included in the present dataset, but were used to exclude control participants↩

  4. Included during the course of the study, also included in Visit 4 to get information from people that did not fill out this cross-sectional questionnaire in Visit 1↩