A Longitudinal Approach to Biological Psychiatric Research: The PsyCourse Study
urs.heilbronner@med.uni-muenchen.de
2017-09-27
- 1 Set working directory
- 2 Read in PsyCourse dataset version 1.1.2 (creates object psycrs1.1.2)
- 3 Load required libraries
- 4 Define functions
- 5 Prepare data
- 5.1 Export file for polygenic risk score analyses
- 5.2 Number of participants at each study visit (determined by data of interview)
- 5.3 Get participants by center
- 5.4 Determine number of center
- 5.5 Get DSM-IV diagnoses for people in PsyCourse study
- 5.6 Define groups “Affective” and “Psychotic”
- 5.7 How many subjects are in groups “Affective” and “Psychotic”
- 6 Create dataset for analysis
- 6.1 Get all variables needed
- 6.2 Rename dataframe
- 6.3 Create object potentially to be shared with other researchers for reproducibility purposes
- 6.4 Create new variable pat_type from cur_psy_trm (pool categories)
- 6.5 Make dataframes “crs” for cross-sectional and “lng” for longitudinal analyses
- 6.6 Recode all -999 (“insufficient information”) in GAF as NA in dataframe “crs” and get summary
- 6.7 Recode all -999 (“insufficient information”) in GAF as NA in dataframe “lng” and get summary
- 6.8 Reshape data frame from wide to long format and get summary
- 6.9 Transformations
- 7 Check distribution of data
- 8 Cross-sectional analyses
- 9 Longitudinal analyses
- 10 Plots
1 Set working directory
setwd("Z:\\Projekte\\Manuskript_Genes/") 2 Read in PsyCourse dataset version 1.1.2 (creates object psycrs1.1.2)
load("Z:\\Projekte\\PsyCoursePhenotypesBackup\\170718_v1.1.2\\170718_v1.1.2_psycourse.RData") 3 Load required libraries
library(ggplot2)
library(lmerTest)
library(car)4 Define functions
as.numeric.factor <- function(x) {as.numeric(levels(x))[x]}
sem<-function(x) sd(x)/sqrt(length(x))
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))))
}5 Prepare data
5.1 Export file for polygenic risk score analyses
mb<-data.frame(psycrs1.1.2$id,
psycrs1.1.2$v1_interv_date,
psycrs1.1.2$v1_center,
psycrs1.1.2$v1_tstlt,
psycrs1.1.2$v1_sex,
psycrs1.1.2$v1_ageBL,
psycrs1.1.2$v1_marital_stat,
psycrs1.1.2$v1_partner,
psycrs1.1.2$v1_liv_aln,
psycrs1.1.2$v1_school,
psycrs1.1.2$v1_prof_dgr,
psycrs1.1.2$v1_ed_status,
psycrs1.1.2$v1_curr_paid_empl,
psycrs1.1.2$v1_cur_psy_trm,
psycrs1.1.2$v1_age_1st_out_trm,
psycrs1.1.2$v1_age_1st_inpat_trm,
psycrs1.1.2$v1_dur_illness,
psycrs1.1.2$v1_cat_daypat_outpat_trm,
psycrs1.1.2$v1_scid_dsm_dx_cat,
psycrs1.1.2$v1_panss_p1,
psycrs1.1.2$v1_panss_p2,
psycrs1.1.2$v1_panss_p3,
psycrs1.1.2$v1_panss_p4,
psycrs1.1.2$v1_panss_p5,
psycrs1.1.2$v1_panss_p6,
psycrs1.1.2$v1_panss_p7,
psycrs1.1.2$v1_panss_n1,
psycrs1.1.2$v1_panss_n2,
psycrs1.1.2$v1_panss_n3,
psycrs1.1.2$v1_panss_n4,
psycrs1.1.2$v1_panss_n5,
psycrs1.1.2$v1_panss_n6,
psycrs1.1.2$v1_panss_n7,
psycrs1.1.2$v1_panss_g1,
psycrs1.1.2$v1_panss_g2,
psycrs1.1.2$v1_panss_g3,
psycrs1.1.2$v1_panss_g4,
psycrs1.1.2$v1_panss_g5,
psycrs1.1.2$v1_panss_g6,
psycrs1.1.2$v1_panss_g7,
psycrs1.1.2$v1_panss_g8,
psycrs1.1.2$v1_panss_g9,
psycrs1.1.2$v1_panss_g10,
psycrs1.1.2$v1_panss_g11,
psycrs1.1.2$v1_panss_g12,
psycrs1.1.2$v1_panss_g13,
psycrs1.1.2$v1_panss_g14,
psycrs1.1.2$v1_panss_g15,
psycrs1.1.2$v1_panss_g16,
psycrs1.1.2$v1_panss_sum_pos,
psycrs1.1.2$v1_panss_sum_neg,
psycrs1.1.2$v1_panss_sum_gen,
psycrs1.1.2$v1_panss_sum_tot,
psycrs1.1.2$v1_idsc_itm1,
psycrs1.1.2$v1_idsc_itm2,
psycrs1.1.2$v1_idsc_itm3,
psycrs1.1.2$v1_idsc_itm4,
psycrs1.1.2$v1_idsc_itm5,
psycrs1.1.2$v1_idsc_itm6,
psycrs1.1.2$v1_idsc_itm7,
psycrs1.1.2$v1_idsc_itm8,
psycrs1.1.2$v1_idsc_itm9,
psycrs1.1.2$v1_idsc_itm9a,
psycrs1.1.2$v1_idsc_itm9b,
psycrs1.1.2$v1_idsc_itm10,
psycrs1.1.2$v1_idsc_itm11,
psycrs1.1.2$v1_idsc_itm12,
psycrs1.1.2$v1_idsc_itm13,
psycrs1.1.2$v1_idsc_itm14,
psycrs1.1.2$v1_idsc_itm15,
psycrs1.1.2$v1_idsc_itm16,
psycrs1.1.2$v1_idsc_itm17,
psycrs1.1.2$v1_idsc_itm18,
psycrs1.1.2$v1_idsc_itm19,
psycrs1.1.2$v1_idsc_itm20,
psycrs1.1.2$v1_idsc_itm21,
psycrs1.1.2$v1_idsc_itm22,
psycrs1.1.2$v1_idsc_itm23,
psycrs1.1.2$v1_idsc_itm24,
psycrs1.1.2$v1_idsc_itm25,
psycrs1.1.2$v1_idsc_itm26,
psycrs1.1.2$v1_idsc_itm27,
psycrs1.1.2$v1_idsc_itm28,
psycrs1.1.2$v1_idsc_itm29,
psycrs1.1.2$v1_idsc_itm30,
psycrs1.1.2$v1_idsc_sum,
psycrs1.1.2$v1_ymrs_itm1,
psycrs1.1.2$v1_ymrs_itm2,
psycrs1.1.2$v1_ymrs_itm3,
psycrs1.1.2$v1_ymrs_itm4,
psycrs1.1.2$v1_ymrs_itm5,
psycrs1.1.2$v1_ymrs_itm6,
psycrs1.1.2$v1_ymrs_itm7,
psycrs1.1.2$v1_ymrs_itm8,
psycrs1.1.2$v1_ymrs_itm9,
psycrs1.1.2$v1_ymrs_itm10,
psycrs1.1.2$v1_ymrs_itm11,
psycrs1.1.2$v1_ymrs_sum,
psycrs1.1.2$v1_gaf,
psycrs1.1.2$v2_interv_date,
psycrs1.1.2$v2_ill_ep_snc_lst,
psycrs1.1.2$v2_gaf,
psycrs1.1.2$v3_interv_date,
psycrs1.1.2$v3_ill_ep_snc_lst,
psycrs1.1.2$v3_gaf,
psycrs1.1.2$v4_interv_date,
psycrs1.1.2$v4_ill_ep_snc_lst,
psycrs1.1.2$v4_gaf)
names(mb)<-gsub("psycrs1.1.2.","",names(mb))
write.table(mb,file="Z:\\Projekte\\Manuskript_Genes\\Data_files\\170721_Items_for_MB.csv", sep="\t", quote=F, row.names=F) 5.2 Number of participants at each study visit (determined by data of interview)
table(is.na(psycrs1.1.2$v1_interv_date)) ##
## FALSE
## 891table(is.na(psycrs1.1.2$v2_interv_date)) ##
## FALSE TRUE
## 526 365table(is.na(psycrs1.1.2$v3_interv_date))##
## FALSE TRUE
## 415 476table(is.na(psycrs1.1.2$v4_interv_date)) ##
## FALSE TRUE
## 351 5405.3 Get participants by center
table(psycrs1.1.2$v1_center)##
## Augsburg Bad Zwischenahn Bochum Bremen Ost
## 41 57 98 27
## Eschwege Göttingen Graz Günzburg
## 7 11 123 100
## Hildesheim Liebenburg LMU München Lüneburg
## 19 9 95 36
## Münster Osnabrück Rotenburg/Wümme Tiefenbrunn
## 6 39 29 5
## UMG Göttingen Wilhelmshaven
## 176 135.4 Determine number of center
length(unique(psycrs1.1.2$v1_center)) ## [1] 185.5 Get DSM-IV diagnoses for people in PsyCourse study
descT(psycrs1.1.2$v1_scid_dsm_dx_cat)## Bipolar-I Disorder Bipolar-II Disorder
## [1,] No. cases 294 68
## [2,] Percent 33 7.6
## Brief Psychotic Disorder Depression Schizoaffective Disorder
## [1,] 6 5 83
## [2,] 0.7 0.6 9.3
## Schizophrenia Schizophreniform Disorder
## [1,] 424 11 891
## [2,] 47.6 1.2 1005.6 Define groups “Affective” and “Psychotic”
psycrs1.1.2$v1_dsm_dx_grp<-rep(NA,dim(psycrs1.1.2)[1])
psycrs1.1.2$v1_dsm_dx_grp[psycrs1.1.2$v1_scid_dsm_dx_cat%in%c("Schizophrenia",
"Schizoaffective Disorder",
"Schizophreniform Disorder",
"Brief Psychotic Disorder")]<-"Psychotic"
psycrs1.1.2$v1_dsm_dx_grp[psycrs1.1.2$v1_scid_dsm_dx_cat%in%c("Bipolar-I Disorder",
"Bipolar-II Disorder","Depression")]<-"Affective"5.7 How many subjects are in groups “Affective” and “Psychotic”
descT(psycrs1.1.2$v1_dsm_dx_grp)## Affective Psychotic
## [1,] No. cases 367 524 891
## [2,] Percent 41.2 58.8 1006 Create dataset for analysis
6.1 Get all variables needed
var_df<-data.frame(psycrs1.1.2$id,
psycrs1.1.2$v1_sex,
psycrs1.1.2$v1_ageBL,
psycrs1.1.2$v1_dsm_dx_grp,
psycrs1.1.2$v1_center,
psycrs1.1.2$v1_cur_psy_trm,
psycrs1.1.2$v1_fam_hist,
psycrs1.1.2$v1_age_1st_inpat_trm,
psycrs1.1.2$v1_marital_stat,
psycrs1.1.2$v1_panss_sum_pos,
psycrs1.1.2$v2_panss_sum_pos,
psycrs1.1.2$v3_panss_sum_pos,
psycrs1.1.2$v4_panss_sum_pos,
psycrs1.1.2$v1_panss_sum_neg,
psycrs1.1.2$v2_panss_sum_neg,
psycrs1.1.2$v3_panss_sum_neg,
psycrs1.1.2$v4_panss_sum_neg,
psycrs1.1.2$v1_panss_sum_gen,
psycrs1.1.2$v2_panss_sum_gen,
psycrs1.1.2$v3_panss_sum_gen,
psycrs1.1.2$v4_panss_sum_gen,
psycrs1.1.2$v1_panss_sum_tot,
psycrs1.1.2$v2_panss_sum_tot,
psycrs1.1.2$v3_panss_sum_tot,
psycrs1.1.2$v4_panss_sum_tot,
psycrs1.1.2$v1_idsc_sum,
psycrs1.1.2$v2_idsc_sum,
psycrs1.1.2$v3_idsc_sum,
psycrs1.1.2$v4_idsc_sum,
psycrs1.1.2$v1_ymrs_sum,
psycrs1.1.2$v2_ymrs_sum,
psycrs1.1.2$v3_ymrs_sum,
psycrs1.1.2$v4_ymrs_sum,
psycrs1.1.2$v1_gaf,
psycrs1.1.2$v2_gaf,
psycrs1.1.2$v3_gaf,
psycrs1.1.2$v4_gaf)6.2 Rename dataframe
names(var_df)[1]<-gsub("psycrs1.1.2.","",names(var_df)[1])
names(var_df)[2:9]<-gsub("psycrs1.1.2.v1_","",names(var_df)[2:9])
names(var_df)[10:37]<-paste(substr(names(var_df)[10:37],16,28),substr(names(var_df)[10:37],14,14),sep=".")6.4 Create new variable pat_type from cur_psy_trm (pool categories)
Define this variable the same as MB (to ensure aproximately equal size of each category): - Outpatient: pool “No” (1) and “Yes, outpatient” (2) - Inpatient: pool “Yes, daypatient” (3) and “Yes, inpatient” (4)
var_df$pat_type[var_df$cur_psy_trm==1 | var_df$cur_psy_trm==2]<- 1
var_df$pat_type[var_df$cur_psy_trm==3 | var_df$cur_psy_trm==4]<- 2
var_df$pat_type<-ordered(var_df$pat_type)
descT(var_df$pat_type)## 1 2 <NA>
## [1,] No. cases 444 440 7 891
## [2,] Percent 49.8 49.4 0.8 1006.5 Make dataframes “crs” for cross-sectional and “lng” for longitudinal analyses
crs<-var_df[,c(1:5,38,7:9,10,26,30,34)]
lng<-var_df[,c(1:5,38,8,10:37)]6.6 Recode all -999 (“insufficient information”) in GAF as NA in dataframe “crs” and get summary
crs$gaf.1[crs$gaf.1==-999]<-NA
summary(crs)## id sex ageBL dsm_dx_grp
## aaph286: 1 F:388 Min. :18.00 Affective:367
## aaru067: 1 M:503 1st Qu.:32.00 Psychotic:524
## abuj304: 1 Median :44.00
## achw003: 1 Mean :42.72
## acok454: 1 3rd Qu.:52.00
## adrr818: 1 Max. :78.00
## (Other):885
## center pat_type fam_hist age_1st_inpat_trm
## UMG Göttingen :176 1 :444 -999: 10 Min. : 7.00
## Graz :123 2 :440 N :231 1st Qu.:21.00
## Günzburg :100 NA's: 7 Y :602 Median :27.00
## Bochum : 98 NA's: 48 Mean :30.15
## LMU München : 95 3rd Qu.:37.00
## Bad Zwischenahn: 57 Max. :73.00
## (Other) :242 NA's :42
## marital_stat panss_sum_pos.1 idsc_sum.1 ymrs_sum.1
## Divorced :152 Min. : 7.00 Min. : 0.00 Min. : 0.000
## Married :185 1st Qu.: 7.00 1st Qu.: 4.00 1st Qu.: 0.000
## Married_living_sep: 44 Median :10.00 Median :10.00 Median : 0.000
## Single :494 Mean :12.29 Mean :12.63 Mean : 3.018
## Widowed : 11 3rd Qu.:16.00 3rd Qu.:19.00 3rd Qu.: 3.000
## NA's : 5 Max. :35.00 Max. :55.00 Max. :39.000
## NA's :18 NA's :126 NA's :40
## gaf.1
## Min. : 4.0
## 1st Qu.:48.0
## Median :56.0
## Mean :56.6
## 3rd Qu.:65.0
## Max. :97.0
## NA's :166.7 Recode all -999 (“insufficient information”) in GAF as NA in dataframe “lng” and get summary
length(subset(lng$gaf.1,lng$gaf.1==-999)) #8 individuals in V1## [1] 8lng$gaf.1[lng$gaf.1==-999]<-NA
length(subset(lng$gaf.2,lng$gaf.2==-999)) #15 individuals in V2## [1] 15lng$gaf.2[lng$gaf.2==-999]<-NA
length(subset(lng$gaf.3,lng$gaf.3==-999)) #8 individuals in V3## [1] 8lng$gaf.3[lng$gaf.3==-999]<-NA
length(subset(lng$gaf.4,lng$gaf.4==-999)) #2 individuals in V4## [1] 2lng$gaf.4[lng$gaf.4==-999]<-NA
summary(lng)## id sex ageBL dsm_dx_grp
## aaph286: 1 F:388 Min. :18.00 Affective:367
## aaru067: 1 M:503 1st Qu.:32.00 Psychotic:524
## abuj304: 1 Median :44.00
## achw003: 1 Mean :42.72
## acok454: 1 3rd Qu.:52.00
## adrr818: 1 Max. :78.00
## (Other):885
## center pat_type age_1st_inpat_trm panss_sum_pos.1
## UMG Göttingen :176 1 :444 Min. : 7.00 Min. : 7.00
## Graz :123 2 :440 1st Qu.:21.00 1st Qu.: 7.00
## Günzburg :100 NA's: 7 Median :27.00 Median :10.00
## Bochum : 98 Mean :30.15 Mean :12.29
## LMU München : 95 3rd Qu.:37.00 3rd Qu.:16.00
## Bad Zwischenahn: 57 Max. :73.00 Max. :35.00
## (Other) :242 NA's :42 NA's :18
## panss_sum_pos.2 panss_sum_pos.3 panss_sum_pos.4 panss_sum_neg.1
## Min. : 7.00 Min. : 7.00 Min. : 7.00 Min. : 7.00
## 1st Qu.: 7.00 1st Qu.: 7.00 1st Qu.: 7.00 1st Qu.: 8.00
## Median : 9.00 Median : 9.00 Median : 9.00 Median :12.00
## Mean :10.29 Mean :10.39 Mean :10.08 Mean :13.66
## 3rd Qu.:12.00 3rd Qu.:12.50 3rd Qu.:12.00 3rd Qu.:18.00
## Max. :32.00 Max. :30.00 Max. :27.00 Max. :38.00
## NA's :372 NA's :480 NA's :540 NA's :29
## panss_sum_neg.2 panss_sum_neg.3 panss_sum_neg.4 panss_sum_gen.1
## Min. : 7.00 Min. : 7.00 Min. : 7.00 Min. :16.00
## 1st Qu.: 8.00 1st Qu.: 7.00 1st Qu.: 7.00 1st Qu.:20.00
## Median :10.00 Median :10.00 Median :10.00 Median :25.00
## Mean :12.14 Mean :11.64 Mean :12.34 Mean :27.42
## 3rd Qu.:15.25 3rd Qu.:14.00 3rd Qu.:15.00 3rd Qu.:33.25
## Max. :39.00 Max. :34.00 Max. :34.00 Max. :74.00
## NA's :375 NA's :490 NA's :545 NA's :31
## panss_sum_gen.2 panss_sum_gen.3 panss_sum_gen.4 panss_sum_tot.1
## Min. :16.00 Min. :16.0 Min. :16.00 Min. : 30.00
## 1st Qu.:18.00 1st Qu.:18.0 1st Qu.:18.00 1st Qu.: 38.00
## Median :22.00 Median :22.0 Median :22.00 Median : 49.00
## Mean :24.43 Mean :24.1 Mean :23.89 Mean : 53.44
## 3rd Qu.:29.00 3rd Qu.:28.0 3rd Qu.:28.00 3rd Qu.: 65.00
## Max. :68.00 Max. :56.0 Max. :50.00 Max. :141.00
## NA's :381 NA's :492 NA's :561 NA's :58
## panss_sum_tot.2 panss_sum_tot.3 panss_sum_tot.4 idsc_sum.1
## Min. : 30.00 Min. : 30.00 Min. : 30.00 Min. : 0.00
## 1st Qu.: 35.00 1st Qu.: 34.00 1st Qu.: 34.00 1st Qu.: 4.00
## Median : 42.50 Median : 41.00 Median : 41.00 Median :10.00
## Mean : 47.01 Mean : 46.21 Mean : 46.06 Mean :12.63
## 3rd Qu.: 55.00 3rd Qu.: 54.00 3rd Qu.: 52.00 3rd Qu.:19.00
## Max. :137.00 Max. :112.00 Max. :100.00 Max. :55.00
## NA's :389 NA's :500 NA's :563 NA's :126
## idsc_sum.2 idsc_sum.3 idsc_sum.4 ymrs_sum.1
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 4.00 1st Qu.: 4.00 1st Qu.: 4.00 1st Qu.: 0.000
## Median : 9.00 Median : 9.00 Median : 9.00 Median : 0.000
## Mean :11.68 Mean :11.13 Mean :11.38 Mean : 3.018
## 3rd Qu.:17.00 3rd Qu.:16.00 3rd Qu.:16.00 3rd Qu.: 3.000
## Max. :46.00 Max. :52.00 Max. :55.00 Max. :39.000
## NA's :419 NA's :529 NA's :586 NA's :40
## ymrs_sum.2 ymrs_sum.3 ymrs_sum.4 gaf.1
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 4.0
## 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:48.0
## Median : 0.000 Median : 1.000 Median : 0.000 Median :56.0
## Mean : 2.132 Mean : 2.522 Mean : 2.065 Mean :56.6
## 3rd Qu.: 3.000 3rd Qu.: 3.000 3rd Qu.: 2.000 3rd Qu.:65.0
## Max. :36.000 Max. :30.000 Max. :24.000 Max. :97.0
## NA's :377 NA's :496 NA's :551 NA's :16
## gaf.2 gaf.3 gaf.4
## Min. :20.00 Min. :25.00 Min. :15.00
## 1st Qu.:52.00 1st Qu.:52.00 1st Qu.:50.00
## Median :62.00 Median :62.00 Median :60.00
## Mean :62.78 Mean :62.12 Mean :60.89
## 3rd Qu.:71.00 3rd Qu.:72.00 3rd Qu.:71.00
## Max. :98.00 Max. :98.00 Max. :95.00
## NA's :374 NA's :482 NA's :5426.8 Reshape data frame from wide to long format and get summary
var<-reshape(data=lng,
direction="long",
varying=c("panss_sum_pos.1","panss_sum_pos.2","panss_sum_pos.3","panss_sum_pos.4",
"panss_sum_neg.1","panss_sum_neg.2","panss_sum_neg.3",
"panss_sum_neg.4","panss_sum_gen.1","panss_sum_gen.2",
"panss_sum_gen.3","panss_sum_gen.4","panss_sum_tot.1",
"panss_sum_tot.2","panss_sum_tot.3","panss_sum_tot.4",
"idsc_sum.1","idsc_sum.2","idsc_sum.3","idsc_sum.4",
"ymrs_sum.1","ymrs_sum.2","ymrs_sum.3","ymrs_sum.4",
"gaf.1","gaf.2","gaf.3","gaf.4"),
idvar="id")
summary(var)## id sex ageBL dsm_dx_grp
## aaph286: 4 F:1552 Min. :18.00 Affective:1468
## aaru067: 4 M:2012 1st Qu.:32.00 Psychotic:2096
## abuj304: 4 Median :44.00
## achw003: 4 Mean :42.72
## acok454: 4 3rd Qu.:52.00
## adrr818: 4 Max. :78.00
## (Other):3540
## center pat_type age_1st_inpat_trm time
## UMG Göttingen :704 1 :1776 Min. : 7.00 Min. :1.00
## Graz :492 2 :1760 1st Qu.:21.00 1st Qu.:1.75
## Günzburg :400 NA's: 28 Median :27.00 Median :2.50
## Bochum :392 Mean :30.15 Mean :2.50
## LMU München :380 3rd Qu.:37.00 3rd Qu.:3.25
## Bad Zwischenahn:228 Max. :73.00 Max. :4.00
## (Other) :968 NA's :168
## panss_sum_pos panss_sum_neg panss_sum_gen panss_sum_tot
## Min. : 7.00 Min. : 7.00 Min. :16.00 Min. : 30.00
## 1st Qu.: 7.00 1st Qu.: 8.00 1st Qu.:19.00 1st Qu.: 35.00
## Median : 9.00 Median :11.00 Median :23.00 Median : 44.00
## Mean :11.08 Mean :12.69 Mean :25.51 Mean : 49.31
## 3rd Qu.:13.00 3rd Qu.:16.00 3rd Qu.:30.00 3rd Qu.: 58.00
## Max. :35.00 Max. :39.00 Max. :74.00 Max. :141.00
## NA's :1410 NA's :1439 NA's :1465 NA's :1510
## idsc_sum ymrs_sum gaf
## Min. : 0.00 Min. : 0.000 Min. : 4.00
## 1st Qu.: 4.00 1st Qu.: 0.000 1st Qu.:50.00
## Median : 9.00 Median : 0.000 Median :60.00
## Mean :11.91 Mean : 2.553 Mean :59.83
## 3rd Qu.:17.00 3rd Qu.: 3.000 3rd Qu.:70.00
## Max. :55.00 Max. :39.000 Max. :98.00
## NA's :1660 NA's :1464 NA's :14146.9 Transformations
6.9.1 Time as factor
var$time<-as.factor(var$time)6.9.2 PANSS Positive Sum Score (log transformation)
var$panss_sum_pos_log<-log(var$panss_sum_pos)6.9.3 IDS-C Sum Score (log transformation)
var$idsc_sum_log<-log(var$idsc_sum+1)6.9.4 YMRS Sum Score (log transformation)
var$ymrs_sum_log<-log(var$ymrs_sum+1)7 Check distribution of data
7.1 PANSS Positive Sum Score
## Warning: Removed 1410 rows containing non-finite values (stat_bin).## Warning: Removed 1410 rows containing non-finite values (stat_density).
7.2 IDS-C Sum Score
## Warning: Removed 1439 rows containing non-finite values (stat_bin).## Warning: Removed 1439 rows containing non-finite values (stat_density).
7.3 YMRS Sum Score
## Warning: Removed 1464 rows containing non-finite values (stat_bin).## Warning: Removed 1464 rows containing non-finite values (stat_density).
7.4 GAF Score
## Warning: Removed 1414 rows containing non-finite values (stat_bin).## Warning: Removed 1414 rows containing non-finite values (stat_density).
8 Cross-sectional analyses
8.1 Sex
8.1.1 Psychotic group: descriptive statistics
descT(subset(crs$sex,crs$dsm_dx_grp=="Psychotic"))## F M
## [1,] No. cases 210 314 524
## [2,] Percent 40.1 59.9 1008.1.2 Affective group: descriptive statistics
descT(subset(crs$sex,crs$dsm_dx_grp=="Affective"))## F M
## [1,] No. cases 178 189 367
## [2,] Percent 48.5 51.5 1008.1.3 Both groups together: descriptive statistics
table(crs$sex,crs$dsm_dx_grp)##
## Affective Psychotic
## F 178 210
## M 189 3148.1.4 Chi-square test for equal distribution of sexes across diagnostic groups
chisq.test(table(crs$sex,crs$dsm_dx_grp))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(crs$sex, crs$dsm_dx_grp)
## X-squared = 5.8939, df = 1, p-value = 0.015198.2 Age at first interview
8.2.1 Psychotic group: descriptive statistics
round(summary(subset(crs$ageBL,crs$dsm_dx_grp=="Psychotic")),1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.0 31.0 42.0 40.8 50.0 73.0round(sd(subset(crs$ageBL,crs$dsm_dx_grp=="Psychotic")),1)## [1] 12.2round(sem(subset(crs$ageBL,crs$dsm_dx_grp=="Psychotic")),1) ## [1] 0.58.2.2 Affective group: descriptive statistics
round(summary(subset(crs$ageBL,crs$dsm_dx_grp=="Affective")),1)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 18.0 35.0 47.0 45.4 55.0 78.0round(sd(subset(crs$ageBL,crs$dsm_dx_grp=="Affective")),1) ## [1] 13.3round(sem(subset(crs$ageBL,crs$dsm_dx_grp=="Affective")),1) ## [1] 0.78.2.3 T-test between both groups
t.test(ageBL~dsm_dx_grp, data=crs, paired=F)##
## Welch Two Sample t-test
##
## data: ageBL by dsm_dx_grp
## t = 5.271, df = 741.43, p-value = 1.781e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.896901 6.335445
## sample estimates:
## mean in group Affective mean in group Psychotic
## 45.43869 40.822528.3 Marital status: single (never married)
8.3.1 Psychotic group: descriptive statistics
descT(subset(crs$marital_stat,crs$dsm_dx_grp=="Psychotic")) ## Divorced Married Married_living_sep Single Widowed <NA>
## [1,] No. cases 76 88 16 336 6 2 524
## [2,] Percent 14.5 16.8 3.1 64.1 1.1 0.4 1008.3.2 Affective group: descriptive statistics
descT(subset(crs$marital_stat,crs$dsm_dx_grp=="Affective")) ## Divorced Married Married_living_sep Single Widowed <NA>
## [1,] No. cases 76 97 28 158 5 3 367
## [2,] Percent 20.7 26.4 7.6 43.1 1.4 0.8 1008.3.3 Both groups together: descriptive statistics
table(crs$marital_stat=="Single",crs$dsm_dx_grp)##
## Affective Psychotic
## FALSE 206 186
## TRUE 158 3368.3.4 Chi-square test for equal distribution of across groups
chisq.test(table(crs$marital_stat=="Single",crs$dsm_dx_grp)) ##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(crs$marital_stat == "Single", crs$dsm_dx_grp)
## X-squared = 37.352, df = 1, p-value = 9.863e-108.4 Age at onset
8.4.1 Psychotic group: descriptive statistics
round(summary(subset(crs$age_1st_inpat_trm,crs$dsm_dx_grp=="Psychotic")),1) ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 7.0 21.0 25.0 27.9 32.0 73.0 13round(sd(na.omit(subset(crs$age_1st_inpat_trm,crs$dsm_dx_grp=="Psychotic"))),1) ## [1] 9.9round(sem(na.omit(subset(crs$age_1st_inpat_trm,crs$dsm_dx_grp=="Psychotic"))),1)## [1] 0.48.4.2 Affective group: descriptive statistics
round(summary(subset(crs$age_1st_inpat_trm,crs$dsm_dx_grp=="Affective")),1) ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 11.0 23.0 32.0 33.6 43.0 73.0 29round(sd(na.omit(subset(crs$age_1st_inpat_trm,crs$dsm_dx_grp=="Affective"))),1) ## [1] 12.9round(sem(na.omit(subset(crs$age_1st_inpat_trm,crs$dsm_dx_grp=="Affective"))),1) ## [1] 0.78.4.3 T-test between both groups
t.test(age_1st_inpat_trm~dsm_dx_grp, data=crs,paired=F) ##
## Welch Two Sample t-test
##
## data: age_1st_inpat_trm by dsm_dx_grp
## t = 6.9438, df = 592.21, p-value = 1.007e-11
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.112684 7.356648
## sample estimates:
## mean in group Affective mean in group Psychotic
## 33.60355 27.868888.5 Family history of psychiatric illness (“-999” -> “Participant does not want to disclose information” excluded)
8.5.1 Psychotic group: descriptive statistics
table(subset(crs$fam_hist=="Y",crs$dsm_dx_grp=="Psychotic"),exclude="-999")##
## FALSE TRUE
## 164 334round(prop.table(table(subset(crs$fam_hist=="Y",crs$dsm_dx_grp=="Psychotic"),exclude="-999"))*100,2)##
## FALSE TRUE
## 32.93 67.078.5.2 Affective group: descriptive statistics
table(subset(crs$fam_hist=="Y",crs$dsm_dx_grp=="Affective"),exclude="-999")##
## FALSE TRUE
## 77 268round(prop.table(table(subset(crs$fam_hist=="Y",crs$dsm_dx_grp=="Affective"),exclude="-999"))*100,2)##
## FALSE TRUE
## 22.32 77.688.5.3 Both groups together: descriptive statistics
table(crs$fam_hist=="Y",crs$dsm_dx_grp,exclude="-999")##
## Affective Psychotic
## FALSE 77 164
## TRUE 268 334round(prop.table(table(crs$fam_hist=="Y",crs$dsm_dx_grp,exclude="-999"))*100,2)##
## Affective Psychotic
## FALSE 9.13 19.45
## TRUE 31.79 39.628.5.4 Chi-square test between Psychotic and Affective group in positive psychiatric family history
chisq.test(table(crs$fam_hist=="Y",crs$dsm_dx_grp)) ##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(crs$fam_hist == "Y", crs$dsm_dx_grp)
## X-squared = 10.73, df = 1, p-value = 0.0010548.6 Inpatient at first visit
8.6.1 Psychotic group: descriptive statistics
descT(subset(crs$pat_type==2,crs$dsm_dx_grp=="Psychotic"))## FALSE TRUE
## [1,] No. cases 212 312 524
## [2,] Percent 40.5 59.5 1008.6.2 Affective group: descriptive statistics
descT(subset(crs$pat_type==2,crs$dsm_dx_grp=="Affective"))## FALSE TRUE <NA>
## [1,] No. cases 232 128 7 367
## [2,] Percent 63.2 34.9 1.9 1008.6.3 Both groups together: descriptive statistics.
table(crs$pat_type==2,crs$dsm_dx_grp)##
## Affective Psychotic
## FALSE 232 212
## TRUE 128 312round(prop.table(table(crs$pat_type==2,crs$dsm_dx_grp))*100,2)##
## Affective Psychotic
## FALSE 26.24 23.98
## TRUE 14.48 35.298.6.4 Chi-square test for equal distribution of across groups
chisq.test(table(crs$pat_type==2,crs$dsm_dx_grp)) ##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(crs$pat_type == 2, crs$dsm_dx_grp)
## X-squared = 48.157, df = 1, p-value = 3.935e-128.6.5 Both groups together (not split according to diagnostic group): descriptive statistics.
table(crs$pat_type)##
## 1 2
## 444 440round(prop.table(table(crs$pat_type))*100,1)##
## 1 2
## 50.2 49.88.7 Sex-specific differences according to diagnostic group
Affective group
Males
summary(subset(crs, crs$dsm_dx_grp=="Affective" & crs$sex=="M"))## id sex ageBL dsm_dx_grp
## adxo842: 1 F: 0 Min. :18.00 Affective:189
## ajty814: 1 M:189 1st Qu.:35.00 Psychotic: 0
## aqol331: 1 Median :47.00
## areh851: 1 Mean :45.63
## athi262: 1 3rd Qu.:55.00
## auqo590: 1 Max. :76.00
## (Other):183
## center pat_type fam_hist age_1st_inpat_trm
## Graz :72 1 :115 -999: 1 Min. :11.00
## UMG Göttingen :37 2 : 69 N : 43 1st Qu.:24.00
## LMU München :17 NA's: 5 Y :131 Median :31.00
## Bad Zwischenahn:12 NA's: 14 Mean :33.47
## Lüneburg :11 3rd Qu.:42.50
## Osnabrück : 9 Max. :73.00
## (Other) :31 NA's :18
## marital_stat panss_sum_pos.1 idsc_sum.1
## Divorced :32 Min. : 7.000 Min. : 0.00
## Married :55 1st Qu.: 7.000 1st Qu.: 4.50
## Married_living_sep:11 Median : 8.000 Median :11.00
## Single :88 Mean : 9.659 Mean :12.44
## Widowed : 1 3rd Qu.:11.000 3rd Qu.:18.00
## NA's : 2 Max. :29.000 Max. :54.00
## NA's :7 NA's :26
## ymrs_sum.1 gaf.1
## Min. : 0.000 Min. :21.0
## 1st Qu.: 0.000 1st Qu.:51.0
## Median : 1.000 Median :60.0
## Mean : 4.084 Mean :61.6
## 3rd Qu.: 5.000 3rd Qu.:70.0
## Max. :39.000 Max. :90.0
## NA's :11 NA's :5Calculate percentages: current inpatient, males
inp_m_aff<-subset(crs$pat_type, crs$dsm_dx_grp=="Affective" & crs$sex=="M")
round(prop.table(table(inp_m_aff))*100,1)## inp_m_aff
## 1 2
## 62.5 37.5Calculate percentages: family history, males (“-999” -> “Participant does not want to disclose information” excluded)
fam_m_aff<-subset(crs$fam_hist, crs$dsm_dx_grp=="Affective" & crs$sex=="M")
round(prop.table(table(fam_m_aff,exclude="-999"))*100,1)## fam_m_aff
## N Y
## 24.7 75.3Calculate percentages: marital status single (never married), males
mar_m_aff<-subset(crs$marital_stat, crs$dsm_dx_grp=="Affective" & crs$sex=="M")
round(prop.table(table(mar_m_aff))*100,1)## mar_m_aff
## Divorced Married Married_living_sep
## 17.1 29.4 5.9
## Single Widowed
## 47.1 0.5Females
summary(subset(crs, crs$dsm_dx_grp=="Affective" & crs$sex=="F"))## id sex ageBL dsm_dx_grp
## aaph286: 1 F:178 Min. :21.00 Affective:178
## abuj304: 1 M: 0 1st Qu.:35.00 Psychotic: 0
## achw003: 1 Median :47.00
## acok454: 1 Mean :45.23
## adrr818: 1 3rd Qu.:54.00
## aghk530: 1 Max. :78.00
## (Other):172
## center pat_type fam_hist age_1st_inpat_trm
## Graz :51 1 :117 -999: 0 Min. :12.00
## UMG Göttingen :43 2 : 59 N : 33 1st Qu.:22.00
## Osnabrück :13 NA's: 2 Y :137 Median :33.00
## Bad Zwischenahn:12 NA's: 8 Mean :33.74
## LMU München :12 3rd Qu.:43.50
## Lüneburg :10 Max. :73.00
## (Other) :37 NA's :11
## marital_stat panss_sum_pos.1 idsc_sum.1 ymrs_sum.1
## Divorced :44 Min. : 7.00 Min. : 0.00 Min. : 0.000
## Married :42 1st Qu.: 7.00 1st Qu.: 5.00 1st Qu.: 0.000
## Married_living_sep:17 Median : 8.00 Median :11.00 Median : 1.000
## Single :70 Mean : 9.26 Mean :14.45 Mean : 3.842
## Widowed : 4 3rd Qu.:11.00 3rd Qu.:20.00 3rd Qu.: 6.500
## NA's : 1 Max. :25.00 Max. :55.00 Max. :28.000
## NA's :5 NA's :29 NA's :7
## gaf.1
## Min. :30.00
## 1st Qu.:51.25
## Median :60.00
## Mean :61.47
## 3rd Qu.:70.00
## Max. :97.00
## NA's :4Calculate percentages: current inpatient, females
inp_f_aff<-subset(crs$pat_type, crs$dsm_dx_grp=="Affective" & crs$sex=="F")
round(prop.table(table(inp_f_aff))*100,1)## inp_f_aff
## 1 2
## 66.5 33.5Calculate percentages: family history, females (“-999” -> “Participant does not want to disclose information” excluded)
fam_f_aff<-subset(crs$fam_hist, crs$dsm_dx_grp=="Affective" & crs$sex=="F")
round(prop.table(table(fam_f_aff,exclude="-999"))*100,1)## fam_f_aff
## N Y
## 19.4 80.6Calculate percentages: marital status single (never married), females
mar_f_aff<-subset(crs$marital_stat, crs$dsm_dx_grp=="Affective" & crs$sex=="F")
round(prop.table(table(mar_f_aff))*100,1)## mar_f_aff
## Divorced Married Married_living_sep
## 24.9 23.7 9.6
## Single Widowed
## 39.5 2.3Psychotic group
Males
summary(subset(crs, crs$dsm_dx_grp=="Psychotic" & crs$sex=="M"))## id sex ageBL dsm_dx_grp
## agts319: 1 F: 0 Min. :18.00 Affective: 0
## ainp421: 1 M:314 1st Qu.:29.00 Psychotic:314
## ajfy391: 1 Median :38.00
## ajnr155: 1 Mean :38.86
## akmi829: 1 3rd Qu.:48.00
## alxu530: 1 Max. :72.00
## (Other):308
## center pat_type fam_hist age_1st_inpat_trm
## UMG Göttingen :58 1:120 -999: 6 Min. : 7.00
## Günzburg :57 2:194 N :102 1st Qu.:21.00
## Bochum :53 Y :194 Median :24.00
## LMU München :45 NA's: 12 Mean :27.08
## Bad Zwischenahn:20 3rd Qu.:31.00
## Augsburg :18 Max. :65.00
## (Other) :63 NA's :6
## marital_stat panss_sum_pos.1 idsc_sum.1 ymrs_sum.1
## Divorced : 23 Min. : 7.0 Min. : 0.00 Min. : 0.000
## Married : 45 1st Qu.: 9.0 1st Qu.: 4.00 1st Qu.: 0.000
## Married_living_sep: 7 Median :14.0 Median : 9.00 Median : 0.000
## Single :236 Mean :14.6 Mean :11.94 Mean : 2.425
## Widowed : 2 3rd Qu.:19.0 3rd Qu.:18.00 3rd Qu.: 3.000
## NA's : 1 Max. :32.0 Max. :48.00 Max. :23.000
## NA's :4 NA's :41 NA's :15
## gaf.1
## Min. : 4.00
## 1st Qu.:42.00
## Median :51.00
## Mean :52.31
## 3rd Qu.:61.00
## Max. :90.00
## NA's :3Calculate percentages: current inpatient, males
inp_m_psy<-subset(crs$pat_type, crs$dsm_dx_grp=="Psychotic" & crs$sex=="M")
round(prop.table(table(inp_m_psy))*100,1)## inp_m_psy
## 1 2
## 38.2 61.8Calculate percentages: family history, males (“-999” -> “Participant does not want to disclose information” exluded)
fam_m_psy<-subset(crs$fam_hist, crs$dsm_dx_grp=="Psychotic" & crs$sex=="M")
round(prop.table(table(fam_m_psy,exclude="-999"))*100,1)## fam_m_psy
## N Y
## 34.5 65.5Calculate percentages: marital status single (never married), males
mar_m_psy<-subset(crs$marital_stat, crs$dsm_dx_grp=="Psychotic" & crs$sex=="M")
round(prop.table(table(mar_m_psy))*100,1)## mar_m_psy
## Divorced Married Married_living_sep
## 7.3 14.4 2.2
## Single Widowed
## 75.4 0.6Females
summary(subset(crs, crs$dsm_dx_grp=="Psychotic" & crs$sex=="F"))## id sex ageBL dsm_dx_grp
## aaru067: 1 F:210 Min. :19.00 Affective: 0
## afsj906: 1 M: 0 1st Qu.:35.00 Psychotic:210
## ajiu782: 1 Median :45.00
## akym393: 1 Mean :43.75
## amrd867: 1 3rd Qu.:52.00
## aojl071: 1 Max. :73.00
## (Other):204
## center pat_type fam_hist age_1st_inpat_trm
## Günzburg :43 1: 92 -999: 3 Min. :12.00
## UMG Göttingen :38 2:118 N : 53 1st Qu.:21.00
## Bochum :28 Y :140 Median :27.00
## LMU München :21 NA's: 14 Mean :29.06
## Bad Zwischenahn:13 3rd Qu.:35.00
## Augsburg :12 Max. :73.00
## (Other) :55 NA's :7
## marital_stat panss_sum_pos.1 idsc_sum.1 ymrs_sum.1
## Divorced : 53 Min. : 7.00 Min. : 0.00 Min. : 0.000
## Married : 43 1st Qu.: 8.00 1st Qu.: 4.00 1st Qu.: 0.000
## Married_living_sep: 9 Median :12.00 Median :10.00 Median : 0.000
## Single :100 Mean :13.65 Mean :12.33 Mean : 2.261
## Widowed : 4 3rd Qu.:17.25 3rd Qu.:18.25 3rd Qu.: 2.000
## NA's : 1 Max. :35.00 Max. :51.00 Max. :32.000
## NA's :2 NA's :30 NA's :7
## gaf.1
## Min. :13.00
## 1st Qu.:45.00
## Median :55.00
## Mean :54.48
## 3rd Qu.:62.00
## Max. :88.00
## NA's :4Calculate percentages: current inpatient, females
inp_f_psy<-subset(crs$pat_type, crs$dsm_dx_grp=="Psychotic" & crs$sex=="F")
round(prop.table(table(inp_f_psy))*100,1)## inp_f_psy
## 1 2
## 43.8 56.2Calculate percentages: family history, females (“-999” -> “Participant does not want to disclose information” exluded)
fam_f_psy<-subset(crs$fam_hist, crs$dsm_dx_grp=="Psychotic" & crs$sex=="F")
round(prop.table(table(fam_f_psy,exclude="-999"))*100,1)## fam_f_psy
## N Y
## 27.5 72.5Calculate percentages: marital status single (never married), females
mar_f_psy<-subset(crs$marital_stat, crs$dsm_dx_grp=="Psychotic" & crs$sex=="F")
round(prop.table(table(mar_f_psy))*100,1)## mar_f_psy
## Divorced Married Married_living_sep
## 25.4 20.6 4.3
## Single Widowed
## 47.8 1.99 Longitudinal analyses
9.1 Descriptive statistics
9.1.1 IDS-C Sum Score
Descriptive statistics according to sex at each measurement:
Females
desc_mean_dep_F<-round(c(mean(var$idsc_sum[var$sex=="F" & var$time=="1"],na.rm=T),mean(var$idsc_sum[var$sex=="F" & var$time=="2"],na.rm=T),mean(var$idsc_sum[var$sex=="F"& var$time=="3"],na.rm=T),mean(var$idsc_sum[var$sex=="F"& var$time=="4"],na.rm=T)),1)
desc_mean_dep_F## [1] 13.3 12.2 12.3 12.4Males
desc_mean_dep_M<-round(c(mean(var$idsc_sum[var$sex=="M" & var$time=="1"],na.rm=T),mean(var$idsc_sum[var$sex=="M" & var$time=="2"],na.rm=T),mean(var$idsc_sum[var$sex=="M"& var$time=="3"],na.rm=T),mean(var$idsc_sum[var$sex=="M"& var$time=="4"],na.rm=T)),1)
desc_mean_dep_M## [1] 12.1 11.2 10.2 10.6Descriptive statistics according to patient status at each measurement:
Inpatients
desc_mean_dep_INP<-round(c(mean(var$idsc_sum[var$pat_type=="2" & var$time=="1"],na.rm=T),mean(var$idsc_sum[var$pat_type=="2" & var$time=="2"],na.rm=T),mean(var$idsc_sum[var$pat_type=="2" & var$time=="3"],na.rm=T),mean(var$idsc_sum[var$pat_type=="2" & var$time=="4"],na.rm=T)),1)
desc_mean_dep_INP## [1] 14.5 12.2 13.5 12.1Outpatients
desc_mean_dep_OUT<-round(c(mean(var$idsc_sum[var$pat_type=="1" & var$time=="1"],na.rm=T),mean(var$idsc_sum[var$pat_type=="1" & var$time=="2"],na.rm=T),mean(var$idsc_sum[var$pat_type=="1" & var$time=="3"],na.rm=T),mean(var$idsc_sum[var$pat_type=="1" & var$time=="4"],na.rm=T)),1)
desc_mean_dep_OUT## [1] 10.7 11.3 9.8 11.09.1.2 YMRS
Descriptive statistics according to diagnostic group at each measurement:
Affective
desc_mean_ymrs_AFF<-round(c(mean(var$ymrs_sum[var$dsm_dx_grp=="Affective" & var$time=="1"],na.rm=T),mean(var$ymrs_sum[var$dsm_dx_grp=="Affective" & var$time=="2"],na.rm=T),mean(var$ymrs_sum[var$dsm_dx_grp=="Affective"& var$time=="3"],na.rm=T),mean(var$ymrs_sum[var$dsm_dx_grp=="Affective" & var$time=="4"],na.rm=T)),1)
desc_mean_ymrs_AFF## [1] 4.0 2.5 2.8 1.9Psychotic
desc_mean_ymrs_PSY<-round(c(mean(var$ymrs_sum[var$dsm_dx_grp=="Psychotic" & var$time=="1"],na.rm=T),mean(var$ymrs_sum[var$dsm_dx_grp=="Psychotic" & var$time=="2"],na.rm=T),mean(var$ymrs_sum[var$dsm_dx_grp=="Psychotic"& var$time=="3"],na.rm=T),mean(var$ymrs_sum[var$dsm_dx_grp=="Psychotic" & var$time=="4"],na.rm=T)),1)
desc_mean_ymrs_PSY## [1] 2.4 1.9 2.3 2.1Time
desc_mean_ymrs_TIME<-round(c(mean(var$ymrs_sum[var$time=="1"],na.rm=T),mean(var$ymrs_sum[var$time=="2"],na.rm=T),mean(var$ymrs_sum[var$time=="3"],na.rm=T),mean(var$ymrs_sum[var$time=="4"],na.rm=T)),1)
desc_mean_ymrs_TIME## [1] 3.0 2.1 2.5 2.19.1.3 PANSS
Affective group
desc_mean_panss_AFF<-round(c(mean(var$panss_sum_pos[var$dsm_dx_grp=="Affective" & var$time=="1"],na.rm=T),mean(var$panss_sum_pos[var$dsm_dx_grp=="Affective" & var$time=="2"],na.rm=T),mean(var$panss_sum_pos[var$dsm_dx_grp=="Affective"& var$time=="3"],na.rm=T),mean(var$panss_sum_pos[var$dsm_dx_grp=="Affective" & var$time=="4"],na.rm=T)),1)
desc_mean_panss_AFF## [1] 9.5 8.5 8.6 8.3Psychotic group
desc_mean_panss_PSY<-round(c(mean(var$panss_sum_pos[var$dsm_dx_grp=="Psychotic" & var$time=="1"],na.rm=T),mean(var$panss_sum_pos[var$dsm_dx_grp=="Psychotic" & var$time=="2"],na.rm=T),mean(var$panss_sum_pos[var$dsm_dx_grp=="Psychotic"& var$time=="3"],na.rm=T),mean(var$panss_sum_pos[var$dsm_dx_grp=="Psychotic" & var$time=="4"],na.rm=T)),1)
desc_mean_panss_PSY## [1] 14.2 11.5 11.6 11.1Time
desc_mean_panss_TIME<-round(c(mean(var$panss_sum_pos[var$time=="1"],na.rm=T),mean(var$panss_sum_pos[var$time=="2"],na.rm=T),mean(var$panss_sum_pos[var$time=="3"],na.rm=T),mean(var$panss_sum_pos[var$time=="4"],na.rm=T)),1)
desc_mean_panss_TIME## [1] 12.3 10.3 10.4 10.19.1.4 GAF
Affective group - females
desc_mean_gaf_AFF_F<-round(c(mean(var$gaf[var$dsm_dx_grp=="Affective" & var$time=="1" & var$sex=="F"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Affective" & var$time=="2" & var$sex=="F"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Affective"& var$time=="3" & var$sex=="F"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Affective" & var$time=="4" & var$sex=="F"],na.rm=T)),1)
desc_mean_gaf_AFF_F## [1] 61.5 65.9 65.1 64.8Affective group - males
desc_mean_gaf_AFF_M<-round(c(mean(var$gaf[var$dsm_dx_grp=="Affective" & var$time=="1" & var$sex=="M"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Affective" & var$time=="2" & var$sex=="M"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Affective"& var$time=="3" & var$sex=="M"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Affective" & var$time=="4" & var$sex=="M"],na.rm=T)),1)
desc_mean_gaf_AFF_M## [1] 61.6 66.5 65.5 66.6Psychotic group - females
desc_mean_gaf_PSY_F<-round(c(mean(var$gaf[var$dsm_dx_grp=="Psychotic" & var$time=="1" & var$sex=="F"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Psychotic" & var$time=="2" & var$sex=="F"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Psychotic"& var$time=="3" & var$sex=="F"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Psychotic" & var$time=="4" & var$sex=="F"],na.rm=T)),1)
desc_mean_gaf_PSY_F## [1] 54.5 61.5 61.6 60.5Psychotic group - males
desc_mean_gaf_PSY_M<-round(c(mean(var$gaf[var$dsm_dx_grp=="Psychotic" & var$time=="1" & var$sex=="M"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Psychotic" & var$time=="2" & var$sex=="M"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Psychotic"& var$time=="3" & var$sex=="M"],na.rm=T),mean(var$gaf[var$dsm_dx_grp=="Psychotic" & var$time=="4" & var$sex=="M"],na.rm=T)),1)
desc_mean_gaf_PSY_M## [1] 52.3 59.8 58.8 56.2In- or daypatient status at T1
desc_mean_gaf_PAT<-round(c(mean(var$gaf[var$time=="1" & var$dsm_dx_grp=="Psychotic"& var$pat_type=="1"],na.rm=T), mean(var$gaf[var$time=="1" & var$dsm_dx_grp=="Psychotic"& var$pat_type=="2"],na.rm=T),
mean(var$gaf[var$time=="1" & var$dsm_dx_grp=="Affective"& var$pat_type=="1"],na.rm=T),
mean(var$gaf[var$time=="1" & var$dsm_dx_grp=="Affective"& var$pat_type=="2"],na.rm=T)),1)
desc_mean_gaf_PAT## [1] 59.3 49.0 65.7 54.19.2 Inferential statistics
9.2.1 IDS-C Sum Score over time
Mixed-linear model with log-transformed data
idsc.log.model = lmer(idsc_sum_log~ageBL+pat_type+sex*dsm_dx_grp*time+(1|id)+(1|center), data=var)
summary(idsc.log.model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: idsc_sum_log ~ ageBL + pat_type + sex * dsm_dx_grp * time + (1 |
## id) + (1 | center)
## Data: var
##
## REML criterion at convergence: 4655.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3518 -0.4606 0.0885 0.5486 2.4211
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.3100 0.5568
## center (Intercept) 0.1372 0.3705
## Residual 0.4377 0.6616
## Number of obs: 1893, groups: id, 844; center, 18
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.489e+00 1.535e-01 1.004e+02 16.214
## ageBL -9.611e-04 2.103e-03 8.239e+02 -0.457
## pat_type.L 2.876e-01 4.640e-02 7.924e+02 6.197
## sexM -1.499e-01 9.848e-02 1.546e+03 -1.522
## dsm_dx_grpPsychotic -3.958e-02 1.019e-01 1.477e+03 -0.389
## time2 -1.503e-01 9.441e-02 1.341e+03 -1.592
## time3 -5.884e-02 9.958e-02 1.377e+03 -0.591
## time4 -1.198e-01 1.191e-01 1.351e+03 -1.006
## sexM:dsm_dx_grpPsychotic 4.925e-02 1.289e-01 1.537e+03 0.382
## sexM:time2 2.982e-02 1.326e-01 1.362e+03 0.225
## sexM:time3 -2.008e-01 1.435e-01 1.383e+03 -1.399
## sexM:time4 -1.383e-01 1.624e-01 1.379e+03 -0.852
## dsm_dx_grpPsychotic:time2 1.502e-01 1.260e-01 1.341e+03 1.192
## dsm_dx_grpPsychotic:time3 7.744e-02 1.368e-01 1.368e+03 0.566
## dsm_dx_grpPsychotic:time4 6.352e-02 1.513e-01 1.350e+03 0.420
## sexM:dsm_dx_grpPsychotic:time2 -4.956e-02 1.712e-01 1.352e+03 -0.290
## sexM:dsm_dx_grpPsychotic:time3 2.091e-01 1.876e-01 1.370e+03 1.114
## sexM:dsm_dx_grpPsychotic:time4 3.514e-01 2.039e-01 1.368e+03 1.723
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ageBL 0.6478
## pat_type.L 9.23e-10 ***
## sexM 0.1282
## dsm_dx_grpPsychotic 0.6977
## time2 0.1117
## time3 0.5547
## time4 0.3145
## sexM:dsm_dx_grpPsychotic 0.7026
## sexM:time2 0.8221
## sexM:time3 0.1621
## sexM:time4 0.3946
## dsm_dx_grpPsychotic:time2 0.2334
## dsm_dx_grpPsychotic:time3 0.5715
## dsm_dx_grpPsychotic:time4 0.6746
## sexM:dsm_dx_grpPsychotic:time2 0.7722
## sexM:dsm_dx_grpPsychotic:time3 0.2653
## sexM:dsm_dx_grpPsychotic:time4 0.0851 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itanova(idsc.log.model)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## ageBL 0.0914 0.0914 1 823.91 0.209 0.64776
## pat_type 16.8120 16.8120 1 792.37 38.407 9.229e-10 ***
## sex 2.7078 2.7078 1 888.67 6.186 0.01306 *
## dsm_dx_grp 1.5181 1.5181 1 812.12 3.468 0.06293 .
## time 1.7239 0.5746 3 1295.28 1.313 0.26869
## sex:dsm_dx_grp 1.1073 1.1073 1 883.85 2.530 0.11209
## sex:time 0.7573 0.2524 3 1308.10 0.577 0.63037
## dsm_dx_grp:time 3.0761 1.0254 3 1304.03 2.342 0.07155 .
## sex:dsm_dx_grp:time 2.0411 0.6804 3 1307.70 1.554 0.19876
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Check assumptions of normal distribution of residuals via Q-Q plot
qqnorm(residuals(idsc.log.model),main="",cex.main=1,cex.lab=1)
qqline(residuals(idsc.log.model))
9.2.2 YMRS Sum Score over time
Mixed-linear model with log-transformed data
ymrs.log.model = lmer(ymrs_sum_log~ageBL+pat_type+sex*dsm_dx_grp*time+(1|id)+(1|center), data=var)
summary(ymrs.log.model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: ymrs_sum_log ~ ageBL + pat_type + sex * dsm_dx_grp * time + (1 |
## id) + (1 | center)
## Data: var
##
## REML criterion at convergence: 5275.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4168 -0.6135 -0.2368 0.5790 3.1214
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.2294 0.4790
## center (Intercept) 0.1352 0.3677
## Residual 0.5306 0.7284
## Number of obs: 2087, groups: id, 865; center, 18
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 9.270e-01 1.469e-01 8.110e+01 6.310
## ageBL 2.406e-03 1.967e-03 7.746e+02 1.223
## pat_type.L 6.313e-02 4.358e-02 7.719e+02 1.449
## sexM 4.138e-02 9.464e-02 1.735e+03 0.437
## dsm_dx_grpPsychotic -3.350e-01 9.700e-02 1.627e+03 -3.454
## time2 -2.858e-01 9.418e-02 1.472e+03 -3.035
## time3 -1.870e-01 1.018e-01 1.527e+03 -1.836
## time4 -5.339e-01 1.158e-01 1.523e+03 -4.609
## sexM:dsm_dx_grpPsychotic -1.852e-02 1.241e-01 1.723e+03 -0.149
## sexM:time2 -1.580e-02 1.337e-01 1.505e+03 -0.118
## sexM:time3 3.159e-02 1.466e-01 1.548e+03 0.215
## sexM:time4 2.993e-01 1.614e-01 1.554e+03 1.854
## dsm_dx_grpPsychotic:time2 1.975e-01 1.277e-01 1.484e+03 1.548
## dsm_dx_grpPsychotic:time3 1.064e-01 1.393e-01 1.529e+03 0.764
## dsm_dx_grpPsychotic:time4 5.353e-01 1.504e-01 1.530e+03 3.559
## sexM:dsm_dx_grpPsychotic:time2 1.046e-01 1.742e-01 1.502e+03 0.600
## sexM:dsm_dx_grpPsychotic:time3 1.977e-01 1.917e-01 1.544e+03 1.031
## sexM:dsm_dx_grpPsychotic:time4 -3.040e-01 2.051e-01 1.548e+03 -1.482
## Pr(>|t|)
## (Intercept) 1.39e-08 ***
## ageBL 0.221684
## pat_type.L 0.147780
## sexM 0.662004
## dsm_dx_grpPsychotic 0.000566 ***
## time2 0.002449 **
## time3 0.066553 .
## time4 4.38e-06 ***
## sexM:dsm_dx_grpPsychotic 0.881412
## sexM:time2 0.905965
## sexM:time3 0.829439
## sexM:time4 0.063893 .
## dsm_dx_grpPsychotic:time2 0.121943
## dsm_dx_grpPsychotic:time3 0.444835
## dsm_dx_grpPsychotic:time4 0.000383 ***
## sexM:dsm_dx_grpPsychotic:time2 0.548495
## sexM:dsm_dx_grpPsychotic:time3 0.302542
## sexM:dsm_dx_grpPsychotic:time4 0.138502
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itanova(ymrs.log.model)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## ageBL 0.7936 0.7936 1 774.58 1.4958 0.2216844
## pat_type 1.1137 1.1137 1 771.85 2.0992 0.1477799
## sex 2.3852 2.3852 1 822.08 4.4956 0.0342808 *
## dsm_dx_grp 2.5869 2.5869 1 748.24 4.8759 0.0275360 *
## time 11.4965 3.8322 3 1454.76 7.2230 8.222e-05 ***
## sex:dsm_dx_grp 0.0175 0.0175 1 814.37 0.0330 0.8558947
## sex:time 1.6320 0.5440 3 1471.93 1.0254 0.3803642
## dsm_dx_grp:time 8.9806 2.9935 3 1466.84 5.6423 0.0007639 ***
## sex:dsm_dx_grp:time 2.8429 0.9476 3 1471.75 1.7861 0.1478645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Check assumptions of normal distribution of residuals via Q-Q plot
qqnorm(residuals(ymrs.log.model),main="",cex.main=2,cex.lab=2)
qqline(residuals(ymrs.log.model))
Get backtransformed least-squares-means and confindence intervals for fixed effects (plus p-values)
cbind(
round(exp(difflsmeans(ymrs.log.model,test.effs=c("time","dsm_dx_grp:time"))$diffs.lsmeans.table[1]),2),
round(exp(difflsmeans(ymrs.log.model,test.effs=c("time","dsm_dx_grp:time"))$diffs.lsmeans.table[5]),2),
round(exp(difflsmeans(ymrs.log.model,test.effs=c("time","dsm_dx_grp:time"))$diffs.lsmeans.table[6]),2),
round(difflsmeans(ymrs.log.model,test.effs=c("time","dsm_dx_grp:time"))$diffs.lsmeans.table[7],3))## Estimate Lower CI Upper CI
## time 1 - 2 1.18 1.09 1.29
## time 1 - 3 1.07 0.97 1.18
## time 1 - 4 1.21 1.10 1.34
## time 2 - 3 0.90 0.82 1.00
## time 2 - 4 1.02 0.92 1.14
## time 3 - 4 1.13 1.01 1.27
## dsm_dx_grp:time Affective 1 - Psychotic 1 1.41 1.23 1.62
## dsm_dx_grp:time Affective 1 - Affective 2 1.34 1.18 1.53
## dsm_dx_grp:time Affective 1 - Psychotic 2 1.47 1.27 1.71
## dsm_dx_grp:time Affective 1 - Affective 3 1.19 1.03 1.37
## dsm_dx_grp:time Affective 1 - Psychotic 3 1.36 1.16 1.60
## dsm_dx_grp:time Affective 1 - Affective 4 1.47 1.25 1.72
## dsm_dx_grp:time Affective 1 - Psychotic 4 1.41 1.20 1.66
## dsm_dx_grp:time Psychotic 1 - Affective 2 0.95 0.81 1.11
## dsm_dx_grp:time Psychotic 1 - Psychotic 2 1.04 0.94 1.17
## dsm_dx_grp:time Psychotic 1 - Affective 3 0.84 0.71 0.99
## dsm_dx_grp:time Psychotic 1 - Psychotic 3 0.97 0.86 1.09
## dsm_dx_grp:time Psychotic 1 - Affective 4 1.04 0.87 1.25
## dsm_dx_grp:time Psychotic 1 - Psychotic 4 1.00 0.88 1.13
## dsm_dx_grp:time Affective 2 - Psychotic 2 1.10 0.93 1.30
## dsm_dx_grp:time Affective 2 - Affective 3 0.88 0.76 1.03
## dsm_dx_grp:time Affective 2 - Psychotic 3 1.02 0.86 1.21
## dsm_dx_grp:time Affective 2 - Affective 4 1.09 0.92 1.30
## dsm_dx_grp:time Affective 2 - Psychotic 4 1.05 0.88 1.25
## dsm_dx_grp:time Psychotic 2 - Affective 3 0.80 0.68 0.96
## dsm_dx_grp:time Psychotic 2 - Psychotic 3 0.92 0.81 1.05
## dsm_dx_grp:time Psychotic 2 - Affective 4 1.00 0.83 1.20
## dsm_dx_grp:time Psychotic 2 - Psychotic 4 0.96 0.84 1.09
## dsm_dx_grp:time Affective 3 - Psychotic 3 1.15 0.96 1.38
## dsm_dx_grp:time Affective 3 - Affective 4 1.24 1.04 1.48
## dsm_dx_grp:time Affective 3 - Psychotic 4 1.19 0.99 1.43
## dsm_dx_grp:time Psychotic 3 - Affective 4 1.08 0.89 1.31
## dsm_dx_grp:time Psychotic 3 - Psychotic 4 1.04 0.90 1.19
## dsm_dx_grp:time Affective 4 - Psychotic 4 0.96 0.79 1.17
## p-value
## time 1 - 2 0.000
## time 1 - 3 0.156
## time 1 - 4 0.000
## time 2 - 3 0.052
## time 2 - 4 0.663
## time 3 - 4 0.031
## dsm_dx_grp:time Affective 1 - Psychotic 1 0.000
## dsm_dx_grp:time Affective 1 - Affective 2 0.000
## dsm_dx_grp:time Affective 1 - Psychotic 2 0.000
## dsm_dx_grp:time Affective 1 - Affective 3 0.020
## dsm_dx_grp:time Affective 1 - Psychotic 3 0.000
## dsm_dx_grp:time Affective 1 - Affective 4 0.000
## dsm_dx_grp:time Affective 1 - Psychotic 4 0.000
## dsm_dx_grp:time Psychotic 1 - Affective 2 0.523
## dsm_dx_grp:time Psychotic 1 - Psychotic 2 0.435
## dsm_dx_grp:time Psychotic 1 - Affective 3 0.042
## dsm_dx_grp:time Psychotic 1 - Psychotic 3 0.584
## dsm_dx_grp:time Psychotic 1 - Affective 4 0.662
## dsm_dx_grp:time Psychotic 1 - Psychotic 4 0.987
## dsm_dx_grp:time Affective 2 - Psychotic 2 0.259
## dsm_dx_grp:time Affective 2 - Affective 3 0.122
## dsm_dx_grp:time Affective 2 - Psychotic 3 0.851
## dsm_dx_grp:time Affective 2 - Affective 4 0.292
## dsm_dx_grp:time Affective 2 - Psychotic 4 0.562
## dsm_dx_grp:time Psychotic 2 - Affective 3 0.015
## dsm_dx_grp:time Psychotic 2 - Psychotic 3 0.237
## dsm_dx_grp:time Psychotic 2 - Affective 4 0.967
## dsm_dx_grp:time Psychotic 2 - Psychotic 4 0.527
## dsm_dx_grp:time Affective 3 - Psychotic 3 0.135
## dsm_dx_grp:time Affective 3 - Affective 4 0.018
## dsm_dx_grp:time Affective 3 - Psychotic 4 0.064
## dsm_dx_grp:time Psychotic 3 - Affective 4 0.452
## dsm_dx_grp:time Psychotic 3 - Psychotic 4 0.624
## dsm_dx_grp:time Affective 4 - Psychotic 4 0.6969.2.3 PANSS Positive Score over time
Mixed-linear model with log-transformed data
panss_p.log.model = lmer(panss_sum_pos_log~ageBL+pat_type+sex*dsm_dx_grp*time+(1|id)+(1|center), data=var)
summary(panss_p.log.model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: panss_sum_pos_log ~ ageBL + pat_type + sex * dsm_dx_grp * time +
## (1 | id) + (1 | center)
## Data: var
##
## REML criterion at convergence: 920.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0547 -0.5230 -0.0841 0.4976 3.7216
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 0.05243 0.2290
## center (Intercept) 0.01519 0.1232
## Residual 0.05288 0.2300
## Number of obs: 2141, groups: id, 877; center, 18
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2.269e+00 5.457e-02 1.281e+02 41.573
## ageBL -8.755e-04 7.875e-04 8.487e+02 -1.112
## pat_type.L 5.681e-02 1.737e-02 7.913e+02 3.271
## sexM 3.570e-02 3.488e-02 1.465e+03 1.024
## dsm_dx_grpPsychotic 2.087e-01 3.599e-02 1.375e+03 5.799
## time2 -8.702e-02 3.008e-02 1.455e+03 -2.893
## time3 -8.364e-02 3.251e-02 1.486e+03 -2.573
## time4 -1.071e-01 3.689e-02 1.480e+03 -2.902
## sexM:dsm_dx_grpPsychotic 4.034e-02 4.568e-02 1.452e+03 0.883
## sexM:time2 -2.789e-02 4.272e-02 1.484e+03 -0.653
## sexM:time3 -2.671e-02 4.667e-02 1.502e+03 -0.572
## sexM:time4 -3.731e-02 5.146e-02 1.501e+03 -0.725
## dsm_dx_grpPsychotic:time2 -5.395e-02 4.069e-02 1.463e+03 -1.326
## dsm_dx_grpPsychotic:time3 -4.375e-02 4.432e-02 1.486e+03 -0.987
## dsm_dx_grpPsychotic:time4 -4.187e-02 4.776e-02 1.484e+03 -0.877
## sexM:dsm_dx_grpPsychotic:time2 3.780e-02 5.561e-02 1.480e+03 0.680
## sexM:dsm_dx_grpPsychotic:time3 2.413e-02 6.082e-02 1.496e+03 0.397
## sexM:dsm_dx_grpPsychotic:time4 -3.592e-02 6.525e-02 1.496e+03 -0.550
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ageBL 0.26659
## pat_type.L 0.00112 **
## sexM 0.30622
## dsm_dx_grpPsychotic 8.25e-09 ***
## time2 0.00388 **
## time3 0.01017 *
## time4 0.00376 **
## sexM:dsm_dx_grpPsychotic 0.37739
## sexM:time2 0.51397
## sexM:time3 0.56720
## sexM:time4 0.46855
## dsm_dx_grpPsychotic:time2 0.18514
## dsm_dx_grpPsychotic:time3 0.32371
## dsm_dx_grpPsychotic:time4 0.38073
## sexM:dsm_dx_grpPsychotic:time2 0.49681
## sexM:dsm_dx_grpPsychotic:time3 0.69164
## sexM:dsm_dx_grpPsychotic:time4 0.58207
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itanova(panss_p.log.model)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## ageBL 0.0653 0.0653 1 848.74 1.236 0.266586
## pat_type 0.5658 0.5658 1 791.26 10.701 0.001117 **
## sex 0.1608 0.1608 1 923.94 3.041 0.081501 .
## dsm_dx_grp 3.4633 3.4633 1 847.05 65.497 1.998e-15 ***
## time 6.7035 2.2345 3 1424.64 42.258 < 2.2e-16 ***
## sex:dsm_dx_grp 0.0677 0.0677 1 919.44 1.281 0.258079
## sex:time 0.1567 0.0522 3 1437.95 0.988 0.397529
## dsm_dx_grp:time 0.2023 0.0674 3 1434.62 1.275 0.281429
## sex:dsm_dx_grp:time 0.0701 0.0234 3 1437.35 0.442 0.723066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Check assumptions of normal distribution of residuals via Q-Q plot
qqnorm(residuals(panss_p.log.model),main="",cex.main=1,cex.lab=1)
qqline(residuals(panss_p.log.model))
Get backtransformed least-squares-means and confindence intervals for fixed effects (plus p-values)
cbind(
round(exp(difflsmeans(panss_p.log.model,test.effs=c("time"))$diffs.lsmeans.table[1]),2),
round(exp(difflsmeans(panss_p.log.model,test.effs=c("time"))$diffs.lsmeans.table[5]),2),
round(exp(difflsmeans(panss_p.log.model,test.effs=c("time"))$diffs.lsmeans.table[6]),2),
round(difflsmeans(panss_p.log.model,test.effs=c("time"))$diffs.lsmeans.table[7],3))## Estimate Lower CI Upper CI p-value
## time 1 - 2 1.13 1.10 1.16 0.000
## time 1 - 3 1.12 1.09 1.15 0.000
## time 1 - 4 1.17 1.13 1.21 0.000
## time 2 - 3 0.99 0.96 1.03 0.728
## time 2 - 4 1.04 1.00 1.07 0.031
## time 3 - 4 1.04 1.01 1.08 0.0179.2.4 GAF Score over time
Mixed-linear model with raw scores
gaf.model = lmer(gaf~ageBL+pat_type+sex*dsm_dx_grp*time+(1|id) + (1|center), data=var)
summary(gaf.model)## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: gaf ~ ageBL + pat_type + sex * dsm_dx_grp * time + (1 | id) +
## (1 | center)
## Data: var
##
## REML criterion at convergence: 16616.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5862 -0.4914 -0.0151 0.4820 4.2337
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 89.479 9.459
## center (Intercept) 2.902 1.704
## Residual 87.288 9.343
## Number of obs: 2137, groups: id, 877; center, 18
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 62.34403 1.85390 339.60000 33.629
## ageBL -0.05423 0.03205 861.40000 -1.692
## pat_type.L -5.52156 0.64701 215.20000 -8.534
## sexM 0.21788 1.42080 1468.90000 0.153
## dsm_dx_grpPsychotic -4.88716 1.43319 1071.20000 -3.410
## time2 4.28438 1.22213 1466.20000 3.506
## time3 3.25953 1.32129 1494.70000 2.467
## time4 3.91637 1.49660 1485.70000 2.617
## sexM:dsm_dx_grpPsychotic -2.26470 1.86542 1459.20000 -1.214
## sexM:time2 0.75581 1.74304 1491.20000 0.434
## sexM:time3 0.70254 1.89878 1506.00000 0.370
## sexM:time4 0.27918 2.09284 1502.40000 0.133
## dsm_dx_grpPsychotic:time2 1.96752 1.65166 1475.30000 1.191
## dsm_dx_grpPsychotic:time3 2.49918 1.80614 1496.60000 1.384
## dsm_dx_grpPsychotic:time4 1.41439 1.94023 1491.90000 0.729
## sexM:dsm_dx_grpPsychotic:time2 -1.86108 2.26419 1487.20000 -0.822
## sexM:dsm_dx_grpPsychotic:time3 -1.79811 2.47694 1501.90000 -0.726
## sexM:dsm_dx_grpPsychotic:time4 -2.94634 2.65531 1499.60000 -1.110
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## ageBL 0.091040 .
## pat_type.L 2.66e-15 ***
## sexM 0.878144
## dsm_dx_grpPsychotic 0.000674 ***
## time2 0.000469 ***
## time3 0.013739 *
## time4 0.008965 **
## sexM:dsm_dx_grpPsychotic 0.224927
## sexM:time2 0.664632
## sexM:time3 0.711439
## sexM:time4 0.893898
## dsm_dx_grpPsychotic:time2 0.233753
## dsm_dx_grpPsychotic:time3 0.166653
## dsm_dx_grpPsychotic:time4 0.466129
## sexM:dsm_dx_grpPsychotic:time2 0.411229
## sexM:dsm_dx_grpPsychotic:time3 0.467989
## sexM:dsm_dx_grpPsychotic:time4 0.267348
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 18 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itanova(gaf.model)## Analysis of Variance Table of type III with Satterthwaite
## approximation for degrees of freedom
## Sum Sq Mean Sq NumDF DenDF F.value Pr(>F)
## ageBL 249.8 249.8 1 861.39 2.862 0.09104 .
## pat_type 6357.0 6357.0 1 215.18 72.828 2.665e-15 ***
## sex 207.2 207.2 1 947.67 2.374 0.12369
## dsm_dx_grp 2820.6 2820.6 1 387.57 32.314 2.584e-08 ***
## time 8941.0 2980.3 3 1435.55 34.144 < 2.2e-16 ***
## sex:dsm_dx_grp 466.7 466.7 1 939.32 5.347 0.02097 *
## sex:time 74.6 24.9 3 1446.20 0.285 0.83643
## dsm_dx_grp:time 203.1 67.7 3 1444.13 0.775 0.50767
## sex:dsm_dx_grp:time 130.7 43.6 3 1445.69 0.499 0.68283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Check assumptions of normal distribution of residuals via Q-Q plot
qqnorm(residuals(gaf.model),main="",cex.main=1,cex.lab=1)
qqline(residuals(gaf.model))
Get least-squares-means and confindence intervals for fixed effects (plus p-values)
cbind(
round(difflsmeans(gaf.model,test.effs=c("time"))$diffs.lsmeans.table[1],2),
round(difflsmeans(gaf.model,test.effs=c("time"))$diffs.lsmeans.table[5],2),
round(difflsmeans(gaf.model,test.effs=c("time"))$diffs.lsmeans.table[6],2),
round(difflsmeans(gaf.model,test.effs=c("time"))$diffs.lsmeans.table[7],3))## Estimate Lower CI Upper CI p-value
## time 1 - 2 -5.18 -6.29 -4.07 0.000
## time 1 - 3 -4.41 -5.63 -3.19 0.000
## time 1 - 4 -4.03 -5.34 -2.72 0.000
## time 2 - 3 0.77 -0.53 2.07 0.245
## time 2 - 4 1.15 -0.23 2.54 0.101
## time 3 - 4 0.38 -1.05 1.82 0.60010 Plots
10.1 IDS-C Sum Score
e <- ggplot(data = var, aes(x = time, y = idsc_sum, group = id, fill=dsm_dx_grp))
e + scale_fill_manual(values=c("lightblue","lightcoral")) + geom_line(color="gray") + geom_violin(data = var, aes(x = time, y = idsc_sum, group=time)) + geom_boxplot(data = var, fill="gray",aes(x = time, y = idsc_sum, group=time),width=0.15) + facet_grid(. ~ dsm_dx_grp) + xlab("Study Visit") + ylab(expression(IDS-C[30]~Sum~Score)) + theme(text = element_text(size=16),legend.position = "none")
ggsave("Z:\\Projekte\\Manuskript_Genes\\Figures\\Figure_2_IDS-C.png")## Saving 7 x 5 in image10.2 YMRS Sum Score
f <- ggplot(data = var, aes(x = time, y = ymrs_sum, group = id, fill=dsm_dx_grp))
f + scale_fill_manual(values=c("lightblue","lightcoral")) + geom_line(color="gray") + geom_violin(data = var, aes(x = time, y = ymrs_sum, group=time)) + geom_boxplot(data = var, fill="gray",aes(x = time, y = ymrs_sum, group=time),width=0.15) + facet_grid(. ~ dsm_dx_grp) + xlab("Study Visit") + ylab("YMRS Sum Score") + theme(text = element_text(size=16),legend.position = "none")
ggsave("Z:\\Projekte\\Manuskript_Genes\\Figures\\Figure_3_YMRS.png")## Saving 7 x 5 in image10.3 PANSS Positive Sum Score
a <- ggplot(data = var, aes(x = time, y = panss_sum_pos, group = id, fill=dsm_dx_grp))
a + scale_fill_manual(values=c("lightblue","lightcoral")) + geom_line(color="gray") + geom_violin(data =var, aes(x = time, y = panss_sum_pos, group=time)) + geom_boxplot(data=var, fill="gray",aes(x=time, y = panss_sum_pos, group=time),width=0.15) + facet_grid(. ~dsm_dx_grp) + xlab("Study Visit") + ylab("PANSS Positive Score") + theme(text = element_text(size=16),legend.position = "none")
ggsave("Z:\\Projekte\\Manuskript_Genes\\Figures\\Figure_4_PANSS_Positive_Symptoms.png")## Saving 7 x 5 in image10.4 GAF Score
g <- ggplot(data = var, aes(x = time, y = gaf, group = id, fill=dsm_dx_grp))
g + scale_fill_manual(values=c("lightblue","lightcoral")) + geom_line(color="gray") + geom_violin(data = var, aes(x = time, y = gaf, group=time)) + geom_boxplot(data = var, fill="gray",aes(x = time, y = gaf, group=time),width=0.15) + facet_grid(. ~ dsm_dx_grp) + xlab("Study Visit") + ylab("Global Assessment of Functioning") + theme(text = element_text(size=16),legend.position = "none")
ggsave("Z:\\Projekte\\Manuskript_Genes\\Figures\\Figure_5_GAF.png")## Saving 7 x 5 in image