This dataset and the accompagnying codebook was created using R version 3.4.4 on an Rstudio Server.
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:
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.
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.
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:
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.
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.
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.
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:
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.
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 .
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.
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 |
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.
library("sjmisc") #neccessary for row_sum function
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]}
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)}
## [1] 1223
## [1] 320
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 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)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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.
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)
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
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
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
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
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)
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$v1_con_cntr_brth))
v1_cntr_brth<-as.factor(c(v1_clin_cntr_brth,v1_con_cntr_brth))
descT(v1_cntr_brth)
## Afghanistan Ägypten anderes Land Argentinien Äthiopien
## [1,] No. cases 1 1 1 1 2
## [2,] Percent 0.1 0.1 0.1 0.1 0.1
## Australien Belarus (Weißrussland) Bosnien und Herzegowina Deutschland
## [1,] 2 1 1 1233
## [2,] 0.1 0.1 0.1 79.9
## Eritrea Estland Finnland Frankreich Irak Iran, Islamische Republik
## [1,] 2 1 1 2 1 3
## [2,] 0.1 0.1 0.1 0.1 0.1 0.2
## Italien Kasachstan Kirgisistan Kroatien Marokko
## [1,] 1 9 2 3 1
## [2,] 0.1 0.6 0.1 0.2 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Niederlande Nigeria
## [1,] 2 1 1 1
## [2,] 0.1 0.1 0.1 0.1
## Österreich Pakistan Polen Rumänien Russische Föderation Senegal
## [1,] 134 1 25 10 11 1
## [2,] 8.7 0.1 1.6 0.6 0.7 0.1
## Serbien Slowakei Slowenien Sri Lanka Tadschikistan Thailand
## [1,] 7 1 1 1 1 1
## [2,] 0.5 0.1 0.1 0.1 0.1 0.1
## Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 3 8 1 2 1
## [2,] 0.2 0.5 0.1 0.1 0.1
## Vereinigte Staaten von Amerika
## [1,] 1
## [2,] 0.1
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 2 57 1543
## [2,] 0.1 3.7 100
v1_clin_cntr_brth_m<-v1_clin$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$v1_demo2_gebortm==1,
"Deutschland",as.character(v1_con_cntr_brth_m))
v1_cntr_brth_m<-as.factor(c(v1_clin_cntr_brth_m,v1_con_cntr_brth_m))
descT(v1_cntr_brth_m)
## Afghanistan Algerien anderes Land Argentinien Äthiopien
## [1,] No. cases 1 1 2 1 2
## [2,] Percent 0.1 0.1 0.1 0.1 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Brasilien
## [1,] 2 1 5 1
## [2,] 0.1 0.1 0.3 0.1
## Bulgarien Chile Dänemark Deutschland Eritrea Estland Finnland
## [1,] 2 2 1 1047 2 2 1
## [2,] 0.1 0.1 0.1 67.9 0.1 0.1 0.1
## Frankreich Griechenland Indien Indonesien Irak
## [1,] 2 1 1 1 1
## [2,] 0.1 0.1 0.1 0.1 0.1
## Iran, Islamische Republik Irland Israel Italien Japan Kasachstan
## [1,] 5 1 1 7 1 10
## [2,] 0.3 0.1 0.1 0.5 0.1 0.6
## Kirgisistan Korea, Republik (Südkorea) Kroatien
## [1,] 1 1 10
## [2,] 0.1 0.1 0.6
## Libysch-Arabische Dschamahirija (Libyen) Luxemburg Marokko
## [1,] 1 3 1
## [2,] 0.1 0.2 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3 1 2 1
## [2,] 0.2 0.1 0.1 0.1
## Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 136 1 95 14 25
## [2,] 8.8 0.1 6.2 0.9 1.6
## Schweiz (Confoederatio Helvetica) Senegal Serbien Singapur Slowakei
## [1,] 2 1 13 1 3
## [2,] 0.1 0.1 0.8 0.1 0.2
## Slowenien Spanien Sri Lanka Thailand Tschechische Republik Türkei
## [1,] 5 3 3 1 24 19
## [2,] 0.3 0.2 0.2 0.1 1.6 1.2
## Ukraine Ungarn Usbekistan
## [1,] 10 11 1
## [2,] 0.6 0.7 0.1
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 2 45 1543
## [2,] 0.1 2.9 100
v1_clin_cntr_brth_f<-v1_clin$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$v1_demo2_gebortv==1,
"Deutschland",as.character(v1_con_cntr_brth_f))
v1_cntr_brth_f<-as.factor(c(v1_clin_cntr_brth_f,v1_con_cntr_brth_f))
descT(v1_cntr_brth_f)
## Afghanistan Ägypten anderes Land Argentinien Armenien
## [1,] No. cases 1 1 4 3 1
## [2,] Percent 0.1 0.1 0.3 0.2 0.1
## Äthiopien Australien Belarus (Weißrussland) Belgien
## [1,] 1 2 1 2
## [2,] 0.1 0.1 0.1 0.1
## Bosnien und Herzegowina Bulgarien Chile Deutschland
## [1,] 3 3 3 1008
## [2,] 0.2 0.2 0.2 65.3
## Dominikanische Republik Eritrea Estland Frankreich Griechenland
## [1,] 1 3 2 5 1
## [2,] 0.1 0.2 0.1 0.3 0.1
## Indien Indonesien Irak Iran, Islamische Republik Israel Italien Japan
## [1,] 1 1 1 5 1 9 2
## [2,] 0.1 0.1 0.1 0.3 0.1 0.6 0.1
## Jemen Kasachstan Korea, Republik (Südkorea) Kroatien Libanon
## [1,] 1 9 1 5 1
## [2,] 0.1 0.6 0.1 0.3 0.1
## Luxemburg Marokko Mazedonien, ehem. jugoslawische Republik Mongolei
## [1,] 1 2 3 1
## [2,] 0.1 0.1 0.2 0.1
## Namibia Nigeria Norwegen Österreich Pakistan
## [1,] 2 1 1 143 2
## [2,] 0.1 0.1 0.1 9.3 0.1
## Palästinensische Autonomiegebiete Polen Rumänien Russische Föderation
## [1,] 1 111 13 32
## [2,] 0.1 7.2 0.8 2.1
## Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Tadschikistan
## [1,] 1 9 2 3 1 2 1
## [2,] 0.1 0.6 0.1 0.2 0.1 0.1 0.1
## Thailand Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 1 22 23 8 6 1
## [2,] 0.1 1.4 1.5 0.5 0.4 0.1
## Vereinigte Staaten von Amerika
## [1,] 5
## [2,] 0.3
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 4 59 1543
## [2,] 0.3 3.8 100
v1_clin_cntr_brth_gmm<-v1_clin$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$v1_demo2_gebortgmm==1,
"Deutschland",as.character(v1_con_cntr_brth_gmm))
v1_cntr_brth_gmm<-as.factor(c(v1_clin_cntr_brth_gmm,v1_con_cntr_brth_gmm))
descT(v1_cntr_brth_gmm)
## Afghanistan anderes Land Argentinien Äthiopien
## [1,] No. cases 1 2 1 1
## [2,] Percent 0.1 0.1 0.1 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien
## [1,] 2 1 6 1
## [2,] 0.1 0.1 0.4 0.1
## Chile China, Volksrepublik Dänemark Deutschland Eritrea Estland
## [1,] 2 1 2 867 2 2
## [2,] 0.1 0.1 0.1 56.2 0.1 0.1
## Finnland Frankreich Georgien Griechenland Indien Indonesien Irak
## [1,] 2 1 1 3 2 1 1
## [2,] 0.1 0.1 0.1 0.2 0.1 0.1 0.1
## Iran, Islamische Republik Israel Italien Japan Kasachstan Kolumbien
## [1,] 5 2 7 1 5 1
## [2,] 0.3 0.1 0.5 0.1 0.3 0.1
## Korea, Republik (Südkorea) Kroatien Litauen Luxemburg Marokko
## [1,] 1 10 2 3 1
## [2,] 0.1 0.6 0.1 0.2 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3 1 1 3
## [2,] 0.2 0.1 0.1 0.2
## Österreich Pakistan Polen Rumänien Russische Föderation Schweden
## [1,] 127 1 124 10 23 1
## [2,] 8.2 0.1 8 0.6 1.5 0.1
## Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei Slowenien
## [1,] 3 1 13 2 6
## [2,] 0.2 0.1 0.8 0.1 0.4
## Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 4 3 1 32 17 15
## [2,] 0.3 0.2 0.1 2.1 1.1 1
## Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 13 1 1
## [2,] 0.8 0.1 0.1
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 2 195 1543
## [2,] 0.1 12.6 100
v1_clin_cntr_brth_gfm<-v1_clin$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$v1_demo2_gebortgmv==1,
"Deutschland",as.character(v1_con_cntr_brth_gfm))
v1_cntr_brth_gfm<-as.factor(c(v1_clin_cntr_brth_gfm,v1_con_cntr_brth_gfm))
descT(v1_cntr_brth_gfm)
## Afghanistan anderes Land Argentinien Aserbaidschan
## [1,] No. cases 1 2 1 1
## [2,] Percent 0.1 0.1 0.1 0.1
## Äthiopien Belarus (Weißrussland) Belgien Bosnien und Herzegowina
## [1,] 1 3 1 6
## [2,] 0.1 0.2 0.1 0.4
## Bulgarien Chile China, Volksrepublik Dänemark Deutschland Eritrea
## [1,] 1 2 2 1 798 2
## [2,] 0.1 0.1 0.1 0.1 51.7 0.1
## Estland Finnland Frankreich Georgien Griechenland Indien Indonesien
## [1,] 2 1 3 1 4 2 1
## [2,] 0.1 0.1 0.2 0.1 0.3 0.1 0.1
## Irak Iran, Islamische Republik Israel Italien Japan Kasachstan
## [1,] 1 5 2 6 1 5
## [2,] 0.1 0.3 0.1 0.4 0.1 0.3
## Korea, Demokratische Volksrepublik (Nordkorea)
## [1,] 1
## [2,] 0.1
## Korea, Republik (Südkorea) Kroatien Luxemburg Marokko
## [1,] 1 10 3 1
## [2,] 0.1 0.6 0.2 0.1
## Mazedonien, ehem. jugoslawische Republik Mongolei Namibia Niederlande
## [1,] 3 1 2 4
## [2,] 0.2 0.1 0.1 0.3
## Österreich Pakistan Polen Rumänien Russische Föderation
## [1,] 123 1 114 9 27
## [2,] 8 0.1 7.4 0.6 1.7
## Schweiz (Confoederatio Helvetica) Senegal Serbien Slowakei Slowenien
## [1,] 2 1 12 3 5
## [2,] 0.1 0.1 0.8 0.2 0.3
## Spanien Sri Lanka Thailand Tschechische Republik Türkei Ukraine
## [1,] 4 3 1 23 18 8
## [2,] 0.3 0.2 0.1 1.5 1.2 0.5
## Ungarn Usbekistan Vereinigte Staaten von Amerika
## [1,] 14 1 1
## [2,] 0.9 0.1 0.1
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 1 290 1543
## [2,] 0.1 18.8 100
v1_clin_cntr_brth_gmf<-v1_clin$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$v1_demo2_gebortgvm==1,
"Deutschland",as.character(v1_con_cntr_brth_gmf))
v1_cntr_brth_gmf<-as.factor(c(v1_clin_cntr_brth_gmf,v1_con_cntr_brth_gmf))
descT(v1_cntr_brth_gmf)
## Afghanistan Ägypten anderes Land Argentinien Australien
## [1,] No. cases 1 1 5 1 1
## [2,] Percent 0.1 0.1 0.3 0.1 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien
## [1,] 1 1 3 2
## [2,] 0.1 0.1 0.2 0.1
## Chile Dänemark Deutschland Dominikanische Republik Eritrea Estland
## [1,] 3 1 790 1 3 2
## [2,] 0.2 0.1 51.2 0.1 0.2 0.1
## Frankreich Ghana Griechenland Indien Irak Iran, Islamische Republik
## [1,] 6 1 3 2 1 5
## [2,] 0.4 0.1 0.2 0.1 0.1 0.3
## Israel Italien Japan Jemen Kasachstan Korea, Republik (Südkorea)
## [1,] 1 9 2 1 3 1
## [2,] 0.1 0.6 0.1 0.1 0.2 0.1
## Kroatien Libanon Luxemburg Marokko
## [1,] 4 2 1 1
## [2,] 0.3 0.1 0.1 0.1
## Mazedonien, ehem. jugoslawische Republik Moldawien (Republik Moldau)
## [1,] 3 2
## [2,] 0.2 0.1
## Mongolei Namibia Niederlande Nigeria Norwegen Österreich Pakistan
## [1,] 2 1 1 1 1 135 1
## [2,] 0.1 0.1 0.1 0.1 0.1 8.7 0.1
## Polen Rumänien Russische Föderation Schweiz (Confoederatio Helvetica)
## [1,] 107 11 31 1
## [2,] 6.9 0.7 2 0.1
## Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Südafrika
## [1,] 1 10 2 4 3 2 1
## [2,] 0.1 0.6 0.1 0.3 0.2 0.1 0.1
## Thailand Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 1 28 20 8 8 1
## [2,] 0.1 1.8 1.3 0.5 0.5 0.1
## Vereinigte Staaten von Amerika
## [1,] 4
## [2,] 0.3
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 4 290 1543
## [2,] 0.3 18.8 100
v1_clin_cntr_brth_gff<-v1_clin$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$v1_demo2_gebortgvv==1,
"Deutschland",as.character(v1_con_cntr_brth_gff))
v1_cntr_brth_gff<-as.factor(c(v1_clin_cntr_brth_gff,v1_con_cntr_brth_gff))
descT(v1_cntr_brth_gff)
## Afghanistan Ägypten Algerien anderes Land Australien
## [1,] No. cases 1 1 1 5 1
## [2,] Percent 0.1 0.1 0.1 0.3 0.1
## Belarus (Weißrussland) Belgien Bosnien und Herzegowina Bulgarien
## [1,] 1 2 3 2
## [2,] 0.1 0.1 0.2 0.1
## Chile Deutschland Eritrea Estland Frankreich Ghana Griechenland
## [1,] 4 819 3 1 5 1 1
## [2,] 0.3 53.1 0.2 0.1 0.3 0.1 0.1
## Indien Irak Iran, Islamische Republik Israel Italien Japan Jemen
## [1,] 2 1 6 1 7 2 1
## [2,] 0.1 0.1 0.4 0.1 0.5 0.1 0.1
## Kasachstan Korea, Republik (Südkorea) Kroatien Litauen Luxemburg
## [1,] 3 1 5 2 1
## [2,] 0.2 0.1 0.3 0.1 0.1
## Marokko Mazedonien, ehem. jugoslawische Republik Mongolei Namibia
## [1,] 1 3 1 2
## [2,] 0.1 0.2 0.1 0.1
## Niederlande Nigeria Norwegen Österreich Pakistan
## [1,] 1 1 1 136 1
## [2,] 0.1 0.1 0.1 8.8 0.1
## Palästinensische Autonomiegebiete Polen Rumänien Russische Föderation
## [1,] 2 107 11 31
## [2,] 0.1 6.9 0.7 2
## Senegal Serbien Slowakei Slowenien Spanien Sri Lanka Thailand
## [1,] 1 9 3 5 3 2 1
## [2,] 0.1 0.6 0.2 0.3 0.2 0.1 0.1
## Tschechische Republik Türkei Ukraine Ungarn Usbekistan
## [1,] 25 21 7 8 1
## [2,] 1.6 1.4 0.5 0.5 0.1
## Vereinigte Staaten von Amerika
## [1,] 5
## [2,] 0.3
## Vereinigtes Königreich Großbritannien und Nordirland <NA>
## [1,] 3 268 1543
## [2,] 0.2 17.4 100
Create dataset
v1_eth<-data.frame(v1_cntr_brth,v1_cntr_brth_m,v1_cntr_brth_f,v1_cntr_brth_gmm,
v1_cntr_brth_gfm,v1_cntr_brth_gmf,v1_cntr_brth_gff)
This is an ordinal scale with four levels: “no”-1, “yes, outpatient”-2, “yes, day patient”-3, “yes, inpatient”-4.
v1_clin_cur_psy_trm<-rep(NA,dim(v1_clin)[1])
v1_con_cur_psy_trm<-rep(NA,dim(v1_con)[1])
v1_clin_cur_psy_trm<-ifelse(v1_clin$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$v1_psyaktuel_aktbehand==1,"4",v1_con_cur_psy_trm))))
v1_cur_psy_trm<-ordered(as.factor(c(v1_clin_cur_psy_trm,v1_con_cur_psy_trm)))
descT(v1_cur_psy_trm)
## 1 2 3 4 <NA>
## [1,] No. cases 352 585 73 513 20 1543
## [2,] Percent 22.8 37.9 4.7 33.2 1.3 100
This is an ordinal scale with four levels: “no”-1, “yes, consultation or short treatment”-2, “yes, continuous treatment for six months or numerous short episodes”-3, “yes, continuous treatment for several years or many short episodes”-4. The option “No information” was coded as “-999”.
v1_clin_outpat_psy_trm<-ifelse(v1_clin$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$v1_psyaktuel_jembehand)
v1_outpat_psy_trm<-factor(c(v1_clin_outpat_psy_trm,v1_con_outpat_psy_trm),ordered=T)
summary(v1_outpat_psy_trm)
## -999 1 2 3 4 NA's
## 13 320 152 134 909 15
If controls were never treated for any mental health reason, they are coded as -999.
v1_age_1st_out_trm<-c(v1_clin$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
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
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
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
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
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
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)
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)
#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$v1_Other_psychiatric<-rowSums(v1_clin_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
#get the following variables from v1_con
#1. Medication name ["_med_medi_2"]
#2. Medication category ["_med_kategorie_2"]
#3. Depot name ["_depot_medi_2"]
#4. Depot category ["_depot_kategorie_2"]
#5. Bedarf name ["_bedarf_medi_2"]
#6. Bedarf category ["_bedarf_kategorie_2"]
v1_con_medication_variables_1<-as.data.frame(v1_con[,grep("mnppsd|_med_medi_2|_med_kategorie_2|_depot_medi_2|_depot_kategorie_2|_bedarf_medi_2|_bedarf_kategorie_2",names(v1_con))])
dim(v1_con_medication_variables_1)
## [1] 320 29
#recode the variables that are coded as characters/logicals in the "v1_con_medication_variables_1" as factors
v1_con_medication_variables_1$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$v1_Other_psychiatric<-rowSums(v1_con_medication_categories_1 == "Psychopharmaka nicht zuordenbar", na.rm = TRUE)
Bind v1_clin and v1_con together by rows
v1_drugs<-rbind(v1_clin_med_table,v1_con_med_table)
dim(v1_drugs) #1543 6
## [1] 1543 6
#check if the id column of v1_drugs and v1_id match
table(droplevels(v1_drugs[,1])==v1_id)
##
## TRUE
## 1543
In control participants, this item is coded “-999”, as it was not assessed.
v1_clin_adv<-ifelse(v1_clin$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
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$v1_medikabehand_medi3_mediaenderung==1,"Y","N")
v1_con_medchange<-rep("-999",dim(v1_con)[1])
v1_medchange<-as.factor(c(v1_clin_medchange,v1_con_medchange))
descT(v1_medchange)
## -999 N Y <NA>
## [1,] No. cases 320 254 592 377 1543
## [2,] Percent 20.7 16.5 38.4 24.4 100
The following two items on medication with lithium are asked in all study vists, in order not to miss participants eligible for the ALDA scale, which is assessed at the last study visit.
v1_clin_lith<-rep(NA,dim(v1_clin)[1])
v1_clin_lith<-ifelse(v1_clin$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
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$v1_medikabehand_med_zusatz_dauer==0,3,NA))))
v1_lith_prd<-factor(c(v1_clin_lith_prd,v1_con_lith_prd))
descT(v1_lith_prd)
## -999 1 2 3 <NA>
## [1,] No. cases 849 103 26 117 448 1543
## [2,] Percent 55 6.7 1.7 7.6 29 100
Create dataset
v1_med<-data.frame(v1_drugs[,2:6],v1_adv,v1_medchange,v1_lith,v1_lith_prd)
The option “Participant does not know” is coded as “NA”. The option “Participant does not want to disclose information” is coded as “-999”.
v1_clin_fam_hist<-rep(NA,dim(v1_clin)[1])
v1_con_fam_hist<-rep(NA,dim(v1_con)[1])
v1_clin_fam_hist<-ifelse(v1_clin$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$v1_famanam_psyangeh==3,NA,v1_con_fam_hist))))
v1_fam_hist<-as.factor(c(v1_clin_fam_hist,v1_con_fam_hist))
descT(v1_fam_hist)
## -999 N Y <NA>
## [1,] No. cases 10 436 1034 63 1543
## [2,] Percent 0.6 28.3 67 4.1 100
Also, in the original assessment of psychiatric history, each affected family member is listed, together with an indication whther the diagnosis is certain or not. This detailed information is available on request.
The method of assessment of physical measures and somatic diseases changed during the course of the study. Selected diseases are now assessed specifically as dichotomous items. Before, it was only assessed whether a participant was ever affected by a group of diseases. If possble, these different phenotypes were harmonized. More detailed information on specific diseases (e.g. “Which type of cancer?”) may be available.
IMPORTANT:
Please note that all of the questions below are self-reported measurements and that interviews were mainly conducted by research/clinical psychologists. For diseases, participants were asked whethe a medical doctor had ever diagnosed them with the disease in question.
Due to the change of assessment of these diseases and the (rather poor) ad-hoc harmonization, a researcher should carefully consider whether these items should be used in research at all.
Height (continuous [centimeters], v1_height)
v1_clin_height<-ifelse(is.na(v1_clin$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$v1_medwkeii_medwkeii_auo==2,"N",v1_con_eyear))
v1_eyear<-factor(c(v1_clin_eyear,v1_con_eyear))
descT(v1_eyear)
## N Y <NA>
## [1,] No. cases 1304 208 31 1543
## [2,] Percent 84.5 13.5 2 100
Infectious diseases (dichotomous, v1_inf) As the assessment of this item was modified in the course of the study (at some point interviewers were instructed not to code benign childhood diseases such as chickenpox), this item is NOT included in this version of the dataset anymore. The variable name remains in the codebook, but not in the dataset.
Create dataset
v1_som_dsrdr<-data.frame(v1_height,
v1_weight,
v1_waist,
v1_bmi,
v1_chol_trig,
v1_hyperten,
v1_ang_pec,
v1_heart_att,
v1_stroke,
v1_diabetes,
v1_hyperthy,
v1_hypothy,
v1_osteopor,
v1_asthma,
v1_copd,
v1_allerg,
v1_neuroder,
v1_psoriasis,
v1_autoimm,
v1_cancer,
v1_stom_ulc,
v1_kid_fail,
v1_stone,
v1_epilepsy,
v1_migraine,
v1_parkinson,
v1_liv_cir_inf,
v1_tbi,
v1_eyear)
There is some more information available (whether smokers have stopped smoking more than one year, if yes how many years, how many pipes, cigars and cigarillos), not part of the present dataset.
This is a categorical item with three optional answers: “never”-N, “yes”-Y, and “former” (having smoked in the past but not now)-F. It is required to have stopped smoking for at least three months to qualify as former smoker.
v1_clin_ever_smkd<-ifelse(v1_clin$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
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$v1_tabalk_ta2_alter_jahre))
v1_age_smk<-factor(c(v1_clin_age_smk,v1_con_age_smk))
summary(v1_age_smk)
## -999 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 662 2 2 2 1 9 4 10 35 51 109 118 167 58 83
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
## 27 41 23 10 11 6 11 2 3 5 5 11 2 2 1
## 35 36 38 40 44 45 48 53 57 61 NA's
## 4 1 1 1 1 1 1 1 1 1 57
In the original item, the number of cigarettes is to be entered by the investigator, however there are three options to which timeframe these cigarettes refer to: per day, per week or per month. Here, we have decided to give the cigarettes per year.
Please note that people who have stopped smoking but less than three months ago are still labeled as smokers, therefore zeros can occur. For people presently do not smoke this question is coded as “-999”.
v1_clin_no_cig<-ifelse(v1_clin$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$v1_tabalk_ta3_anz_zig*12,NA))))
v1_no_cig<-c(v1_clin_no_cig,v1_con_no_cig)
summary(v1_no_cig)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -999 -999 1825 3384 7300 23725 380
This is and ordinal item. Optional answers are: “never”-1, “only on special occasions”-2, “once per month or less”-3, “two to four times per month”-4, “two to three times per week”-5, “four times per week or several times but not daily”-6, “daily”-7.
v1_alc_pst12_mths<-factor(c(v1_clin$v1_tabalk_ta9_alkkonsum,v1_con$v1_tabalk_alkohol9), ordered=T)
descT(v1_alc_pst12_mths)
## 1 2 3 4 5 6 7 <NA>
## [1,] No. cases 275 332 182 352 189 67 72 74 1543
## [2,] Percent 17.8 21.5 11.8 22.8 12.2 4.3 4.7 4.8 100
This is an ordinal item. Optional answers are: “never”-1, “once or twice”-2, “three to five times”-3, “six to eleven times”-4, “approximately once per month”-5, “two to three times per month”-6, “one to two times per week”-7, “three to four times per week”-8, “daily or almost daily”-9. Note that this item was skipped if participants chose answering alternatives 1, 2 or 3 in the previous question. In these cases, coding is -999.
v1_clin_alc_5orm<-ifelse((v1_clin$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$v1_tabalk_alkohol10))
v1_alc_5orm<-factor(c(v1_clin_alc_5orm,v1_con_alc_5orm), ordered=T)
descT(v1_alc_5orm)
## -999 1 2 3 4 5 6 7 8 9 <NA>
## [1,] No. cases 789 213 98 93 64 61 70 43 17 14 81 1543
## [2,] Percent 51.1 13.8 6.4 6 4.1 4 4.5 2.8 1.1 0.9 5.2 100
The criteria for alcohol dependence are checked, resulting in a dichotomous assessment whether lifetime alcohol dependence is present.
v1_clin_lftm_alc_dep<-ifelse(v1_clin$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
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:
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)
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.
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 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!
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:
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$v1_sna_21_mde1_a79_beurteilung==0), -999, NA)))
descT(v1_clin_scid_no_MDE)
## -999 1 2 3 4 5 6 7 8 9 10 11 12 13
## [1,] No. cases 355 71 93 90 79 60 34 17 32 9 38 7 14 3
## [2,] Percent 29 5.8 7.6 7.4 6.5 4.9 2.8 1.4 2.6 0.7 3.1 0.6 1.1 0.2
## 14 15 16 17 18 20 21 22 23 25 26 29 30 32 35 36 39
## [1,] 4 17 2 3 1 18 1 1 1 3 2 2 12 1 1 1 1
## [2,] 0.3 1.4 0.2 0.2 0.1 1.5 0.1 0.1 0.1 0.2 0.2 0.2 1 0.1 0.1 0.1 0.1
## 40 50 60 70 75 99 <NA>
## [1,] 2 3 2 1 1 188 53 1223
## [2,] 0.2 0.2 0.2 0.1 0.1 15.4 4.3 100
v1_scid_no_MDE<-c(v1_clin_scid_no_MDE,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_MDE)
## -999 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## [1,] No. cases 675 71 93 90 79 60 34 17 32 9 38 7 14 3 4
## [2,] Percent 43.7 4.6 6 5.8 5.1 3.9 2.2 1.1 2.1 0.6 2.5 0.5 0.9 0.2 0.3
## 15 16 17 18 20 21 22 23 25 26 29 30 32 35 36 39 40
## [1,] 17 2 3 1 18 1 1 1 3 2 2 12 1 1 1 1 2
## [2,] 1.1 0.1 0.2 0.1 1.2 0.1 0.1 0.1 0.2 0.1 0.1 0.8 0.1 0.1 0.1 0.1 0.1
## 50 60 70 75 99 <NA>
## [1,] 3 2 1 1 188 53 1543
## [2,] 0.2 0.1 0.1 0.1 12.2 3.4 100
Age at first manic episode (continuous, v1_scid_age_mania) This item includes all individuals that ever fulfilled mania criteria. Also a person with e.g. schizophrenia can have a value on this item. If the individual ever fulfilled mania criteria, the age at the first manic episode is given. NA on this item means that age at first mania episode is missing.
Individuals that never experienced a manic episode are coded as “-999”.
Control individiuals are coded as “-999”.
v1_clin_scid_age_mania<-ifelse(is.na(v1_clin$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:
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:
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$v1_sna_24_hypomane1_a_160_beurteilung==0), -999, NA)))
descT(v1_clin_scid_no_hypomania)
## -999 1 2 3 4 5 6 7 8 10 12 14 15 18
## [1,] No. cases 1101 19 14 9 7 4 2 5 2 5 1 1 2 1
## [2,] Percent 90 1.6 1.1 0.7 0.6 0.3 0.2 0.4 0.2 0.4 0.1 0.1 0.2 0.1
## 20 30 50 70 75 99 <NA>
## [1,] 2 1 1 1 1 13 31 1223
## [2,] 0.2 0.1 0.1 0.1 0.1 1.1 2.5 100
v1_scid_no_hypomania<-c(v1_clin_scid_no_hypomania,rep(-999,dim(v1_con)[1])) #add -999 for control individuals
descT(v1_scid_no_hypomania)
## -999 1 2 3 4 5 6 7 8 10 12 14 15 18
## [1,] No. cases 1421 19 14 9 7 4 2 5 2 5 1 1 2 1
## [2,] Percent 92.1 1.2 0.9 0.6 0.5 0.3 0.1 0.3 0.1 0.3 0.1 0.1 0.1 0.1
## 20 30 50 70 75 99 <NA>
## [1,] 2 1 1 1 1 13 31 1543
## [2,] 0.1 0.1 0.1 0.1 0.1 0.8 2 100
This section was assessed in all study participants. If at least one item covering delusions (delusion item) or hallucinations (hallucination item) was answered in the affirmative this was coded “Y”, otherwise “N”. Please note that an individual was also coded as “N” if there was insufficient information (“0” on the original SCID item; occuring only in few individuals) or if the symptom was too mild to fulfill criteria (“2” on the original SCID item).
NA means that at least one question was not completed and all other completed questions were coded “N”.
v1_clin_scid_ever_delus<-ifelse((is.na(v1_clin$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$v1_snb_31_prodsymp1_b14_gedankenueber==3),"Y",NA)))
v1_scid_ever_delus<-factor(c(v1_clin_scid_ever_delus,rep(-999,dim(v1_con)[1])))
summary(v1_scid_ever_delus)
## -999 N Y NA's
## 320 302 862 59
v1_clin_scid_ever_halls<-ifelse(v1_clin$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
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
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$v1_snx_112_suizged2_x7_suizid_gedanken,rep(-999,dim(v1_con)[1]))==3, "Y",
v1_scid_evr_suic_ide)))
descT(v1_scid_evr_suic_ide)
## -999 N Y <NA>
## [1,] No. cases 328 286 903 26 1543
## [2,] Percent 21.3 18.5 58.5 1.7 100
This is an ordinal item with the following gradation: “only fleeting thoughts”-1, “serious thoughts (details were elaborated)”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).
v1_scid_suic_ide<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_ide<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
ifelse(c(v1_clin$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$v1_snx_112_suizged2_x8_suizged_inhalt==4,rep(-999,dim(v1_con)[1])), 4, v1_scid_suic_ide)))))
v1_scid_suic_ide<-factor(v1_scid_suic_ide,ordered=T)
descT(v1_scid_suic_ide)
## -999 1 2 3 4 <NA>
## [1,] No. cases 614 291 123 128 330 57 1543
## [2,] Percent 39.8 18.9 8 8.3 21.4 3.7 100
This is an ordinal item with the following gradation: “no”-1, “yes, but no details”-2, “yes, including details”-3. The item is coded “-999” if skipped (see above).
v1_scid_suic_thght_mth<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_scid_suic_thght_mth<-ifelse(v1_scid_evr_suic_ide!="Y", -999,
ifelse(c(v1_clin$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
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
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
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$v1_snx_111_suizvrs2_x2_suiz_anz,rep(-999,dim(v1_con)[1])), v1_scid_no_suic_attmpt))
v1_scid_no_suic_attmpt<-factor(v1_scid_no_suic_attmpt,ordered=T)
descT(v1_scid_no_suic_attmpt)
## -999 1 2 3 <NA>
## [1,] No. cases 1097 221 93 79 53 1543
## [2,] Percent 71.1 14.3 6 5.1 3.4 100
This is an ordinal item with the following gradation: “no preparation (impulsive attempt)”-1, “little preparation”-2, “moderate preparation”-3, “Extensive, all details planned”-4. The item is coded “-999” if skipped (see above).
v1_prep_suic_attp_ord<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_prep_suic_attp_ord<-ifelse(v1_suic_attmpt==1, -999,
ifelse(v1_suic_attmpt>1 &
c(v1_clin$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$v1_snx_111_suizvrs2_x5_suiz_vorb,rep(-999,dim(v1_con)[1]))==4, 4,
v1_prep_suic_attp_ord)))))
v1_prep_suic_attp_ord<-factor(v1_prep_suic_attp_ord,ordered=T)
descT(v1_prep_suic_attp_ord)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1097 118 36 72 132 88 1543
## [2,] Percent 71.1 7.6 2.3 4.7 8.6 5.7 100
This is on ordinal item with the following gradation: “no”-1, “having thought about”-2, “persistent thoughts (serious and less serious)”-3, “options 2 and 3 together”-4. The item is coded “-999” if skipped (see above).
v1_suic_note_attmpt<-c(rep(NA,dim(v1_clin)[1]),rep(-999,dim(v1_con)[1]))
v1_suic_note_attmpt<-ifelse(v1_suic_attmpt==1, -999,
ifelse(v1_suic_attmpt>1 & c(v1_clin$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$v1_snx_111_suizvrs2_x6_abschiedshdl
,rep(-999,dim(v1_con)[1]))==4, "4",
v1_suic_note_attmpt)))))
v1_suic_note_attmpt<-factor(v1_suic_note_attmpt,ordered=T)
descT(v1_suic_note_attmpt)
## -999 1 2 3 4 <NA>
## [1,] No. cases 1097 250 17 17 90 72 1543
## [2,] Percent 71.1 16.2 1.1 1.1 5.8 4.7 100
Create dataset
v1_scid<-data.frame(v1_scid_dsm_dx,
v1_scid_dsm_dx_cat,
v1_scid_age_MDE,
v1_scid_no_MDE,
v1_scid_age_mania,
v1_scid_no_mania,
v1_scid_age_hypomania,
v1_scid_no_hypomania,
v1_scid_ever_halls,
v1_scid_ever_delus,
v1_scid_ever_psyc,
v1_scid_age_fst_psyc,
v1_scid_yr_fst_psyc,
v1_scid_evr_suic_ide,
v1_scid_suic_ide,
v1_scid_suic_thght_mth,
v1_scid_suic_note_thgts,
v1_suic_attmpt,
v1_scid_no_suic_attmpt,
v1_prep_suic_attp_ord,
v1_suic_note_attmpt)
The PANSS (Kay, Fiszbein, & Opler, 1987) is a rating scale measuring positive and negative symptoms in schizophrenia. It has three subscales: positive, negative and gereral psychopathology symptoms. Each item is rated on an ordinal scale from one to seven with the following gradation: “absent”-1, “minimal”-2, “mild”-3, “moderate”-4, “moderate severe”-5, “severe”-6, “extreme”-7. On all items, higher scores mean more severe symptoms. The ratings refer to the past seven days. Please find the items below.
P1 Delusions (ordinal [1,2,3,4,5,6,7], v1_panss_p1)
v1_panss_p1<-c(v1_clin$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$v1_panss_p_p7_feindseligkeit)
v1_panss_p7<-factor(v1_panss_p7, ordered=T)
descT(v1_panss_p7)
## 1 2 3 4 5 <NA>
## [1,] No. cases 1245 96 76 16 4 106 1543
## [2,] Percent 80.7 6.2 4.9 1 0.3 6.9 100
PANSS Positive sum score (continuous [7-49], v1_panss_sum_pos)
v1_panss_sum_pos<-as.numeric.factor(v1_panss_p1)+
as.numeric.factor(v1_panss_p2)+
as.numeric.factor(v1_panss_p3)+
as.numeric.factor(v1_panss_p4)+
as.numeric.factor(v1_panss_p5)+
as.numeric.factor(v1_panss_p6)+
as.numeric.factor(v1_panss_p7)
summary(v1_panss_sum_pos)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 7.00 8.50 10.81 13.00 35.00 117
N1 Blunted affect (ordinal [1,2,3,4,5,6,7], v1_panss_n1)
v1_panss_n1<-c(v1_clin$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$v1_panss_n_n7_stereotyp_ged)
v1_panss_n7<-factor(v1_panss_n7, ordered=T)
descT(v1_panss_n7)
## 1 2 3 4 5 6 <NA>
## [1,] No. cases 1096 138 144 42 11 5 107 1543
## [2,] Percent 71 8.9 9.3 2.7 0.7 0.3 6.9 100
PANSS Negative sum score (continuous [7-49], v1_panss_sum_neg)
v1_panss_sum_neg<-as.numeric.factor(v1_panss_n1)+
as.numeric.factor(v1_panss_n2)+
as.numeric.factor(v1_panss_n3)+
as.numeric.factor(v1_panss_n4)+
as.numeric.factor(v1_panss_n5)+
as.numeric.factor(v1_panss_n6)+
as.numeric.factor(v1_panss_n7)
summary(v1_panss_sum_neg)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.00 7.00 10.00 12.22 16.00 38.00 136
G1 Somatic concerns (ordinal [1,2,3,4,5,6,7], v1_panss_g1)
v1_panss_g1<-c(v1_clin$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)
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)))))