rm(list=ls())
# Load libraries ---------------------------
library(haven)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(geeM)
## Loading required package: Matrix
library(magrittr)
# Set wd for generated output ---------------------------
setwd("/Volumes/caas/MERITS/RESOURCES/Data Processing/Data Analysis/Outcome Analyses 2016/Manuscript Revisions_2020/Aditya Khanna Analyses 2022/RESOURCES-AK-R")
# Load data ---------------------------
load(file="gee-formulae.RData")
# Specify corstr = ar1 ---------------------------
corstr = "ar1"
# DV = AVCIG ---------------------------
#relevel MI and CBT
AVCIG_nobl_dt$MI_ref1 <- AVCIG_nobl_dt$MI %>% relevel("1")
AVCIG_nobl_dt$CBT_ref1 <- AVCIG_nobl_dt$CBT %>% relevel("1")
AVCIG_nobl_dt$TIME_ref1 <- AVCIG_nobl_dt$TIME %>% relevel("1")
# ignore missing values
## create an indicator column for any missing values
AVCIG_nobl_dt$any_na <- AVCIG_nobl_dt %>% apply(1, function(x){any(is.na(x))})
## left join the dataset by the id column using the group_by() function
AVCIG_nobl_dt %<>% left_join(AVCIG_nobl_dt %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
## filter out rows with any NAs
AVCIG_nobl_dt %<>% filter(any_na2 != T)
# modeling
## main model
AVCIG_nobl_main <- geem(formula = AVCIG.nobl.main,
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print main model summary
summary(AVCIG_nobl_main)
## Estimates Model SE Robust SE wald p
## (Intercept) 1.732000 0.108400 0.117200 14.7800 0.000000
## cAGE -0.027910 0.053040 0.060770 -0.4592 0.646100
## cBLCGSMD 0.049730 0.006412 0.005501 9.0400 0.000000
## MI1 0.081820 0.117900 0.116900 0.6998 0.484100
## CBT1 -0.001529 0.118500 0.116700 -0.0131 0.989500
## TIME1 0.123500 0.045150 0.045430 2.7170 0.006579
##
## Estimated Correlation Parameter: 0.7434
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8339
##
## Number of GEE iterations: 4
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
## interaction model
AVCIG_nobl_interaction <- geem(formula = AVCIG.nobl.interaction,
#mi=0, cbt=0, time=0
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
AVCIG_nobl_interaction_TIMEref1 <- geem(formula =
AVCIG ~ cAGE + cBLCGSMD + MI + CBT + TIME_ref1 + MI * CBT + MI * TIME_ref1 +
CBT * TIME_ref1 + MI * CBT * TIME_ref1,
#mi=0, cbt=0, time=1
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
AVCIG_nobl_interaction_MIref1 <-
geem(formula = AVCIG ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 * TIME +
CBT * TIME + MI_ref1 * CBT * TIME,
#ref: Mi=1, Cbt=0, Time=0
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
AVCIG_nobl_interaction_MIref1_TIMEref1 <-
geem(formula = AVCIG ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME_ref1 +
MI_ref1 * CBT + MI_ref1 * TIME_ref1 +
CBT * TIME_ref1 + MI_ref1 * CBT * TIME_ref1,
#ref: Mi=1, Cbt=0, Time=1
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
AVCIG_nobl_interaction_CBTref1 <-
geem(formula = AVCIG ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI *
TIME + CBT_ref1 * TIME + MI * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, Time = 0
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
AVCIG_nobl_interaction_CBTref1_TIMEref1 <-
geem(formula = AVCIG ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME_ref1 +
MI * CBT_ref1 + MI * TIME_ref1 + CBT_ref1 * TIME_ref1 + MI * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, Time = 1
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
AVCIG_nobl_interaction_MIref1_CBTref1 <-
geem(formula = AVCIG ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME + CBT_ref1 * TIME + MI_ref1 * CBT_ref1 * TIME,
#ref: Mi=1, Cbt=1, Time=0
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
AVCIG_nobl_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = AVCIG ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME_ref1 +
MI_ref1 * CBT_ref1 + MI_ref1 * TIME_ref1 +
CBT_ref1 * TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1,
#ref: Mi=1, Cbt=1, Time=1
data = AVCIG_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction model summary
summary(AVCIG_nobl_interaction) #base: MI=0, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.8020000 0.133200 0.147800 12.190000 0.0000
## cAGE -0.0264300 0.053150 0.061120 -0.432500 0.6654
## cBLCGSMD 0.0509600 0.006433 0.005465 9.326000 0.0000
## MI1 -0.1181000 0.190900 0.194100 -0.608600 0.5428
## CBT1 -0.1225000 0.180400 0.185800 -0.659400 0.5096
## TIME1 0.1129000 0.093470 0.075660 1.493000 0.1356
## MI1:CBT1 0.3341000 0.256000 0.251800 1.327000 0.1845
## MI1:TIME1 0.1265000 0.133400 0.135700 0.932200 0.3512
## CBT1:TIME1 0.0009478 0.126400 0.109500 0.008652 0.9931
## MI1:CBT1:TIME1 -0.1793000 0.179200 0.182100 -0.984600 0.3248
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 4
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
summary(AVCIG_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.9150000 0.132100 0.123400 15.520000 0.0000
## cAGE -0.0264300 0.053150 0.061120 -0.432500 0.6654
## cBLCGSMD 0.0509600 0.006433 0.005465 9.326000 0.0000
## MI1 0.0083580 0.188500 0.190100 0.043960 0.9649
## CBT1 -0.1216000 0.178900 0.170100 -0.714700 0.4748
## TIME_ref10 -0.1129000 0.093470 0.075660 -1.493000 0.1356
## MI1:CBT1 0.1548000 0.253500 0.249900 0.619600 0.5355
## MI1:TIME_ref10 -0.1265000 0.133400 0.135700 -0.932200 0.3512
## CBT1:TIME_ref10 -0.0009478 0.126400 0.109500 -0.008652 0.9931
## MI1:CBT1:TIME_ref10 0.1793000 0.179200 0.182100 0.984600 0.3248
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 4
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
summary(AVCIG_nobl_interaction_MIref1) #ref: MI=1, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.68400 0.135900 0.124700 13.5000 0.00000
## cAGE -0.02643 0.053150 0.061120 -0.4325 0.66540
## cBLCGSMD 0.05096 0.006433 0.005465 9.3260 0.00000
## MI_ref10 0.11810 0.190900 0.194100 0.6086 0.54280
## CBT1 0.21160 0.180600 0.170100 1.2440 0.21340
## TIME1 0.23940 0.095130 0.112600 2.1260 0.03352
## MI_ref10:CBT1 -0.33410 0.256000 0.251800 -1.3270 0.18450
## MI_ref10:TIME1 -0.12650 0.133400 0.135700 -0.9322 0.35120
## CBT1:TIME1 -0.17830 0.127000 0.145500 -1.2260 0.22020
## MI_ref10:CBT1:TIME1 0.17930 0.179200 0.182100 0.9846 0.32480
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 3
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
summary(AVCIG_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.923000 0.133500 0.141500 13.59000 0.00000
## cAGE -0.026430 0.053150 0.061120 -0.43250 0.66540
## cBLCGSMD 0.050960 0.006433 0.005465 9.32600 0.00000
## MI_ref10 -0.008358 0.188500 0.190100 -0.04396 0.96490
## CBT1 0.033240 0.178600 0.180800 0.18380 0.85420
## TIME_ref10 -0.239400 0.095130 0.112600 -2.12600 0.03352
## MI_ref10:CBT1 -0.154800 0.253500 0.249900 -0.61960 0.53550
## MI_ref10:TIME_ref10 0.126500 0.133400 0.135700 0.93220 0.35120
## CBT1:TIME_ref10 0.178300 0.127000 0.145500 1.22600 0.22020
## MI_ref10:CBT1:TIME_ref10 -0.179300 0.179200 0.182100 -0.98460 0.32480
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 4
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
summary(AVCIG_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.6790000 0.121000 0.114800 14.630000 0.0000
## cAGE -0.0264300 0.053150 0.061120 -0.432500 0.6654
## cBLCGSMD 0.0509600 0.006433 0.005465 9.326000 0.0000
## MI1 0.2160000 0.169800 0.163100 1.324000 0.1854
## CBT_ref10 0.1225000 0.180400 0.185800 0.659400 0.5096
## TIME1 0.1139000 0.085110 0.079230 1.437000 0.1506
## MI1:CBT_ref10 -0.3341000 0.256000 0.251800 -1.327000 0.1845
## MI1:TIME1 -0.0528100 0.119700 0.121500 -0.434800 0.6637
## CBT_ref10:TIME1 -0.0009478 0.126400 0.109500 -0.008652 0.9931
## MI1:CBT_ref10:TIME1 0.1793000 0.179200 0.182100 0.984600 0.3248
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 4
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
summary(AVCIG_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.7930000 0.120000 0.117700 15.230000 0.0000
## cAGE -0.0264300 0.053150 0.061120 -0.432500 0.6654
## cBLCGSMD 0.0509600 0.006433 0.005465 9.326000 0.0000
## MI1 0.1632000 0.168700 0.163200 0.999900 0.3173
## CBT_ref10 0.1216000 0.178900 0.170100 0.714700 0.4748
## TIME_ref10 -0.1139000 0.085110 0.079230 -1.437000 0.1506
## MI1:CBT_ref10 -0.1548000 0.253500 0.249900 -0.619600 0.5355
## MI1:TIME_ref10 0.0528100 0.119700 0.121500 0.434800 0.6637
## CBT_ref10:TIME_ref10 0.0009478 0.126400 0.109500 0.008652 0.9931
## MI1:CBT_ref10:TIME_ref10 -0.1793000 0.179200 0.182100 -0.984600 0.3248
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 4
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
summary(AVCIG_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.89500 0.119200 0.116600 16.2600 0.0000
## cAGE -0.02643 0.053150 0.061120 -0.4325 0.6654
## cBLCGSMD 0.05096 0.006433 0.005465 9.3260 0.0000
## MI_ref10 -0.21600 0.169800 0.163100 -1.3240 0.1854
## CBT_ref10 -0.21160 0.180600 0.170100 -1.2440 0.2134
## TIME1 0.06106 0.084170 0.092070 0.6632 0.5072
## MI_ref10:CBT_ref10 0.33410 0.256000 0.251800 1.3270 0.1845
## MI_ref10:TIME1 0.05281 0.119700 0.121500 0.4348 0.6637
## CBT_ref10:TIME1 0.17830 0.127000 0.145500 1.2260 0.2202
## MI_ref10:CBT_ref10:TIME1 -0.17930 0.179200 0.182100 -0.9846 0.3248
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 3
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
summary(AVCIG_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.95600 0.118700 0.114400 17.0900 0.0000
## cAGE -0.02643 0.053150 0.061120 -0.4325 0.6654
## cBLCGSMD 0.05096 0.006433 0.005465 9.3260 0.0000
## MI_ref10 -0.16320 0.168700 0.163200 -0.9999 0.3173
## CBT_ref10 -0.03324 0.178600 0.180800 -0.1838 0.8542
## TIME_ref10 -0.06106 0.084170 0.092070 -0.6632 0.5072
## MI_ref10:CBT_ref10 0.15480 0.253500 0.249900 0.6196 0.5355
## MI_ref10:TIME_ref10 -0.05281 0.119700 0.121500 -0.4348 0.6637
## CBT_ref10:TIME_ref10 -0.17830 0.127000 0.145500 -1.2260 0.2202
## MI_ref10:CBT_ref10:TIME_ref10 0.17930 0.179200 0.182100 0.9846 0.3248
##
## Estimated Correlation Parameter: 0.7493
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8299
##
## Number of GEE iterations: 4
## Number of Clusters: 246 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 492
# DV = Cig/SmkD ---------------------------
#relevel MI and CBT
CGSMD_nobl_dt$MI_ref1 <- CGSMD_nobl_dt$MI %>% relevel("1")
CGSMD_nobl_dt$CBT_ref1 <- CGSMD_nobl_dt$CBT %>% relevel("1")
CGSMD_nobl_dt$TIME_ref1 <- CGSMD_nobl_dt$TIME %>% relevel("1")
# ignore missing values
## create an indicator column for any missing values
CGSMD_nobl_dt$any_na <- CGSMD_nobl_dt %>% apply(1, function(x){any(is.na(x))})
# ## left join the dataset by the id column using the group_by() function
CGSMD_nobl_dt %<>% left_join(CGSMD_nobl_dt %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
# ## filter out rows with any NAs
CGSMD_nobl_dt %<>% filter(any_na2 != T)
## main model
CGSMD_nobl_main <- geem(formula = CGSMD.nobl.main,
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print main model summary
summary(CGSMD_nobl_main)
## Estimates Model SE Robust SE wald p
## (Intercept) 1.96100 0.089830 0.095270 20.5800 0.000000
## cAGE -0.02582 0.044690 0.050800 -0.5083 0.611200
## cBLCGSMD 0.04462 0.005711 0.004886 9.1330 0.000000
## MI1 -0.02427 0.098520 0.097120 -0.2499 0.802700
## CBT1 0.06931 0.098740 0.097130 0.7137 0.475400
## TIME1 0.09890 0.037940 0.038150 2.5920 0.009532
##
## Estimated Correlation Parameter: 0.7412
## Correlation Structure: ar1
## Est. Scale Parameter: 0.528
##
## Number of GEE iterations: 3
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
## interaction model
CGSMD_nobl_interaction <- geem(formula = CGSMD.nobl.interaction,
#mi=0, cbt=0, time=0
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
CGSMD_nobl_interaction_TIMEref1 <- geem(formula =
CGSMD ~ cAGE + cBLCGSMD + MI + CBT + TIME_ref1 + MI * CBT + MI * TIME_ref1 +
CBT * TIME_ref1 + MI * CBT * TIME_ref1,
#mi=0, cbt=0, time=1
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
CGSMD_nobl_interaction_MIref1 <-
geem(formula = CGSMD ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 * TIME +
CBT * TIME + MI_ref1 * CBT * TIME,
#ref: Mi=1, Cbt=0, time=0
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
CGSMD_nobl_interaction_MIref1_TIMEref1 <-
geem(formula = CGSMD ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME_ref1 +
MI_ref1 * CBT + MI_ref1 * TIME_ref1 +
CBT * TIME_ref1 + MI_ref1 * CBT * TIME_ref1,
#ref: Mi=1, Cbt=0, time=1
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
CGSMD_nobl_interaction_CBTref1 <-
geem(formula = CGSMD ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI *
TIME + CBT_ref1 * TIME + MI * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, time=0
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
CGSMD_nobl_interaction_CBTref1_TIMEref1 <-
geem(formula = CGSMD ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME_ref1 + MI * CBT_ref1 + MI *
TIME_ref1 + CBT_ref1 * TIME_ref1 + MI * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, time=1
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
CGSMD_nobl_interaction_MIref1_CBTref1 <-
geem(formula = CGSMD ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME + CBT_ref1 * TIME + MI_ref1 * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, time=0
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
CGSMD_nobl_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = CGSMD ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME_ref1 + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME_ref1 + CBT_ref1 * TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, time=1
data = CGSMD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction model summary
summary(CGSMD_nobl_interaction) #base: MI=0, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.01000 0.110100 0.120000 16.7500 0.0000
## cAGE -0.02455 0.044860 0.051060 -0.4808 0.6307
## cBLCGSMD 0.04529 0.005742 0.004835 9.3670 0.0000
## MI1 -0.15170 0.155900 0.160100 -0.9479 0.3432
## CBT1 -0.04097 0.153100 0.149300 -0.2744 0.7837
## TIME1 0.07267 0.077410 0.062440 1.1640 0.2445
## MI1:CBT1 0.25960 0.213500 0.207800 1.2490 0.2116
## MI1:TIME1 0.10730 0.109100 0.119300 0.8989 0.3687
## CBT1:TIME1 0.07835 0.107200 0.085290 0.9187 0.3583
## MI1:CBT1:TIME1 -0.24370 0.149300 0.151800 -1.6050 0.1084
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
summary(CGSMD_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.08300 0.109700 0.104200 19.98000 0.0000
## cAGE -0.02455 0.044860 0.051060 -0.48080 0.6307
## cBLCGSMD 0.04529 0.005742 0.004835 9.36700 0.0000
## MI1 -0.04446 0.154600 0.162400 -0.27370 0.7843
## CBT1 0.03738 0.152200 0.135800 0.27520 0.7832
## TIME_ref10 -0.07267 0.077410 0.062440 -1.16400 0.2445
## MI1:CBT1 0.01595 0.212100 0.209700 0.07605 0.9394
## MI1:TIME_ref10 -0.10730 0.109100 0.119300 -0.89890 0.3687
## CBT1:TIME_ref10 -0.07835 0.107200 0.085290 -0.91870 0.3583
## MI1:CBT1:TIME_ref10 0.24370 0.149300 0.151800 1.60500 0.1084
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
summary(CGSMD_nobl_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.85800 0.109800 0.106200 17.5000 0.00000
## cAGE -0.02455 0.044860 0.051060 -0.4808 0.63070
## cBLCGSMD 0.04529 0.005742 0.004835 9.3670 0.00000
## MI_ref10 0.15170 0.155900 0.160100 0.9479 0.34320
## CBT1 0.21860 0.147900 0.145100 1.5070 0.13190
## TIME1 0.17990 0.076820 0.101700 1.7700 0.07681
## MI_ref10:CBT1 -0.25960 0.213500 0.207800 -1.2490 0.21160
## MI_ref10:TIME1 -0.10730 0.109100 0.119300 -0.8989 0.36870
## CBT1:TIME1 -0.16530 0.103800 0.125600 -1.3160 0.18800
## MI_ref10:CBT1:TIME1 0.24370 0.149300 0.151800 1.6050 0.10840
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
summary(CGSMD_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.03800 0.108500 0.123000 16.57000 0.00000
## cAGE -0.02455 0.044860 0.051060 -0.48080 0.63070
## cBLCGSMD 0.04529 0.005742 0.004835 9.36700 0.00000
## MI_ref10 0.04446 0.154600 0.162400 0.27370 0.78430
## CBT1 0.05333 0.146900 0.157900 0.33770 0.73560
## TIME_ref10 -0.17990 0.076820 0.101700 -1.77000 0.07681
## MI_ref10:CBT1 -0.01595 0.212100 0.209700 -0.07605 0.93940
## MI_ref10:TIME_ref10 0.10730 0.109100 0.119300 0.89890 0.36870
## CBT1:TIME_ref10 0.16530 0.103800 0.125600 1.31600 0.18800
## MI_ref10:CBT1:TIME_ref10 -0.24370 0.149300 0.151800 -1.60500 0.10840
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
summary(CGSMD_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.96900 0.105800 0.090330 21.8000 0.00000
## cAGE -0.02455 0.044860 0.051060 -0.4808 0.63070
## cBLCGSMD 0.04529 0.005742 0.004835 9.3670 0.00000
## MI1 0.10790 0.145000 0.134300 0.8032 0.42190
## CBT_ref10 0.04097 0.153100 0.149300 0.2744 0.78370
## TIME1 0.15100 0.074220 0.058110 2.5990 0.00935
## MI1:CBT_ref10 -0.25960 0.213500 0.207800 -1.2490 0.21160
## MI1:TIME1 -0.13640 0.101900 0.093840 -1.4540 0.14600
## CBT_ref10:TIME1 -0.07835 0.107200 0.085290 -0.9187 0.35830
## MI1:CBT_ref10:TIME1 0.24370 0.149300 0.151800 1.6050 0.10840
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
summary(CGSMD_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.12000 0.104900 0.087900 24.12000 0.00000
## cAGE -0.02455 0.044860 0.051060 -0.48080 0.63070
## cBLCGSMD 0.04529 0.005742 0.004835 9.36700 0.00000
## MI1 -0.02851 0.144200 0.132600 -0.21510 0.82970
## CBT_ref10 -0.03738 0.152200 0.135800 -0.27520 0.78320
## TIME_ref10 -0.15100 0.074220 0.058110 -2.59900 0.00935
## MI1:CBT_ref10 -0.01595 0.212100 0.209700 -0.07605 0.93940
## MI1:TIME_ref10 0.13640 0.101900 0.093840 1.45400 0.14600
## CBT_ref10:TIME_ref10 0.07835 0.107200 0.085290 0.91870 0.35830
## MI1:CBT_ref10:TIME_ref10 -0.24370 0.149300 0.151800 -1.60500 0.10840
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
summary(CGSMD_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.07700 0.098990 0.099590 20.8500 0.0000
## cAGE -0.02455 0.044860 0.051060 -0.4808 0.6307
## cBLCGSMD 0.04529 0.005742 0.004835 9.3670 0.0000
## MI_ref10 -0.10790 0.145000 0.134300 -0.8032 0.4219
## CBT_ref10 -0.21860 0.147900 0.145100 -1.5070 0.1319
## TIME1 0.01461 0.069870 0.073680 0.1983 0.8428
## MI_ref10:CBT_ref10 0.25960 0.213500 0.207800 1.2490 0.2116
## MI_ref10:TIME1 0.13640 0.101900 0.093840 1.4540 0.1460
## CBT_ref10:TIME1 0.16530 0.103800 0.125600 1.3160 0.1880
## MI_ref10:CBT_ref10:TIME1 -0.24370 0.149300 0.151800 -1.6050 0.1084
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
summary(CGSMD_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.09200 0.098910 0.099930 20.93000 0.0000
## cAGE -0.02455 0.044860 0.051060 -0.48080 0.6307
## cBLCGSMD 0.04529 0.005742 0.004835 9.36700 0.0000
## MI_ref10 0.02851 0.144200 0.132600 0.21510 0.8297
## CBT_ref10 -0.05333 0.146900 0.157900 -0.33770 0.7356
## TIME_ref10 -0.01461 0.069870 0.073680 -0.19830 0.8428
## MI_ref10:CBT_ref10 0.01595 0.212100 0.209700 0.07605 0.9394
## MI_ref10:TIME_ref10 -0.13640 0.101900 0.093840 -1.45400 0.1460
## CBT_ref10:TIME_ref10 -0.16530 0.103800 0.125600 -1.31600 0.1880
## MI_ref10:CBT_ref10:TIME_ref10 0.24370 0.149300 0.151800 1.60500 0.1084
##
## Estimated Correlation Parameter: 0.7507
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5267
##
## Number of GEE iterations: 4
## Number of Clusters: 216 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 432
# DV = PCSMKD ---------------------------
#relevel MI and CBT
PCSMKD_nobl_dt$MI_ref1 <- PCSMKD_nobl_dt$MI %>% relevel("1")
PCSMKD_nobl_dt$CBT_ref1 <- PCSMKD_nobl_dt$CBT %>% relevel("1")
PCSMKD_nobl_dt$TIME_ref1 <- PCSMKD_nobl_dt$TIME %>% relevel("1")
# check contrasts
contrasts(PCSMKD_nobl_dt$MI) #ref: MI=0
## 1
## 0 0
## 1 1
contrasts(PCSMKD_nobl_dt$MI_ref1) #ref: MI=1
## 0
## 1 0
## 0 1
contrasts(PCSMKD_nobl_dt$CBT) #ref: CBT=0
## 1
## 0 0
## 1 1
contrasts(PCSMKD_nobl_dt$CBT_ref1) #ref: CBT=1
## 0
## 1 0
## 0 1
# ignore missing values
## create an indicator column for any missing values
PCSMKD_nobl_dt$any_na <- PCSMKD_nobl_dt %>% apply(1, function(x){any(is.na(x))})
# ## left join the dataset by the id column using the group_by() function
PCSMKD_nobl_dt %<>% left_join(PCSMKD_nobl_dt %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
# ## filter out rows with any NAs
PCSMKD_nobl_dt %<>% filter(any_na2 != T)
## fit main model
PCSMKD_nobl_main <- geem(formula = PCSMKD.nobl.main,
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print main model summary
summary(PCSMKD_nobl_main) #ref: Mi=0, Cbt=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.333000 0.045790 0.046850 92.4800 0.000000
## cAGE 0.013610 0.022390 0.023650 0.5755 0.564900
## cBLCGSMD 0.008767 0.002774 0.003216 2.7260 0.006414
## MI1 0.102300 0.049550 0.048600 2.1050 0.035310
## CBT1 -0.055010 0.049770 0.048560 -1.1330 0.257300
## TIME1 0.027570 0.024220 0.024300 1.1340 0.256600
##
## Estimated Correlation Parameter: 0.6129
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1818
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
## fit MI and CBT 2-way interactions
PCSMKD_MI_interactions <- geem(formula = PCSMDK.MI.interaction,
#ref: Mi=0, Cbt=0
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_MI_interactions_MIref1_CBT_ref0 <-
geem(PCSMKD ~ cAGE + cBLCGSMD + MI_ref1*TIME + CBT,
#ref: Mi=1, Cbt=0
data = PCSMKD_nobl_dt ,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_CBT_interactions <- geem(formula = PCSMDK.CBT.interaction,
#ref: Mi=0, Cbt=0
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print main model summaries
summary(PCSMKD_MI_interactions)
## Estimates Model SE Robust SE wald p
## (Intercept) 4.350000 0.04744 0.048590 89.5300 0.000000
## cAGE 0.013420 0.02245 0.023670 0.5671 0.570700
## cBLCGSMD 0.008770 0.00278 0.003221 2.7230 0.006469
## MI1 0.068350 0.05524 0.055790 1.2250 0.220500
## CBT1 -0.055710 0.04988 0.048600 -1.1460 0.251700
## TIME1 -0.006276 0.03415 0.036440 -0.1722 0.863300
## MI1:TIME1 0.067900 0.04837 0.048550 1.3980 0.162000
##
## Estimated Correlation Parameter: 0.6152
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1824
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_MI_interactions_MIref1_CBT_ref0)
## Estimates Model SE Robust SE wald p
## (Intercept) 4.41800 0.04806 0.043320 102.0000 0.000000
## cAGE 0.01342 0.02245 0.023670 0.5671 0.570700
## cBLCGSMD 0.00877 0.00278 0.003221 2.7230 0.006469
## MI_ref10 -0.06835 0.05524 0.055790 -1.2250 0.220500
## TIME1 0.06162 0.03426 0.032090 1.9200 0.054810
## CBT1 -0.05571 0.04988 0.048600 -1.1460 0.251700
## MI_ref10:TIME1 -0.06790 0.04837 0.048550 -1.3980 0.162000
##
## Estimated Correlation Parameter: 0.6152
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1824
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_CBT_interactions)
## Estimates Model SE Robust SE wald p
## (Intercept) 4.31300 0.04780 0.05075 84.9800 0.000000
## cAGE 0.01357 0.02245 0.02365 0.5735 0.566300
## cBLCGSMD 0.00879 0.00278 0.00322 2.7300 0.006327
## MI1 0.10300 0.04966 0.04864 2.1180 0.034160
## CBT1 -0.01928 0.05549 0.05630 -0.3424 0.732000
## TIME1 0.06690 0.03607 0.03507 1.9070 0.056460
## CBT1:TIME1 -0.07137 0.04858 0.04850 -1.4720 0.141200
##
## Estimated Correlation Parameter: 0.6159
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1823
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
## fit 3-way interaction model
PCSMKD_nobl_interaction <-
geem(formula = PCSMKD.nobl.interaction,
#ref: Mi=0, Cbt=0, time=0
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_nobl_interaction_TIMEref1 <-
geem(formula = PCSMKD ~ cAGE + cBLCGSMD + MI + CBT + TIME_ref1 + MI * CBT + MI *
TIME_ref1 + CBT * TIME_ref1 + MI * CBT * TIME_ref1,
#ref: Mi=0, Cbt=0, time=1
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_nobl_interaction_MIref1 <-
geem(formula = PCSMKD ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 *
TIME + CBT * TIME + MI_ref1 * CBT * TIME,
#ref: Mi=1, Cbt=0, time=0
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_nobl_interaction_MIref1_TIMEref1 <-
geem(formula = PCSMKD ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME_ref1 + MI_ref1 * CBT + MI_ref1 *
TIME_ref1 + CBT * TIME_ref1 + MI_ref1 * CBT * TIME_ref1,
#ref: Mi=1, Cbt=0, time=1
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_nobl_interaction_CBTref1 <-
geem(formula = PCSMKD ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI *
TIME + CBT_ref1 * TIME + MI * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, time=0
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_nobl_interaction_CBTref1_TIMEref1 <-
geem(formula = PCSMKD ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME_ref1 + MI * CBT_ref1 + MI *
TIME_ref1 + CBT_ref1 * TIME_ref1 + MI * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, time=1
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_nobl_interaction_MIref1_CBTref1 <-
geem(formula = PCSMKD ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME + CBT_ref1 * TIME + MI_ref1 * CBT_ref1 * TIME,
#ref: Mi=1, Cbt=1, time=0
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
PCSMKD_nobl_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = PCSMKD ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME_ref1 + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME_ref1 + CBT_ref1 * TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1,
#ref: Mi=1, Cbt=1, time=1
data = PCSMKD_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction model summary
summary(PCSMKD_nobl_interaction) #base: MI=0, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.3300000 0.05797 0.064030 67.620000 0.000000
## cAGE 0.0146700 0.02263 0.023800 0.616200 0.537800
## cBLCGSMD 0.0090460 0.00281 0.003239 2.793000 0.005224
## MI1 0.0676200 0.08316 0.081390 0.830700 0.406100
## CBT1 -0.0186100 0.07880 0.085690 -0.217200 0.828100
## TIME1 0.0789700 0.04993 0.055900 1.413000 0.157800
## MI1:CBT1 -0.0001742 0.11190 0.112500 -0.001549 0.998800
## MI1:TIME1 -0.0253200 0.07158 0.069030 -0.366800 0.713800
## CBT1:TIME1 -0.1607000 0.06790 0.073140 -2.197000 0.027990
## MI1:CBT1:TIME1 0.1746000 0.09640 0.096000 1.819000 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.409000 0.05794 0.054990 80.1700 0.000000
## cAGE 0.014670 0.02263 0.023800 0.6162 0.537800
## cBLCGSMD 0.009046 0.00281 0.003239 2.7930 0.005224
## MI1 0.042290 0.08313 0.069030 0.6126 0.540100
## CBT1 -0.179300 0.07880 0.086900 -2.0640 0.039060
## TIME_ref10 -0.078970 0.04993 0.055900 -1.4130 0.157800
## MI1:CBT1 0.174400 0.11190 0.105600 1.6520 0.098580
## MI1:TIME_ref10 0.025320 0.07158 0.069030 0.3668 0.713800
## CBT1:TIME_ref10 0.160700 0.06790 0.073140 2.1970 0.027990
## MI1:CBT1:TIME_ref10 -0.174600 0.09640 0.096000 -1.8190 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_nobl_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.3970000 0.05932 0.049960 88.010000 0.000000
## cAGE 0.0146700 0.02263 0.023800 0.616200 0.537800
## cBLCGSMD 0.0090460 0.00281 0.003239 2.793000 0.005224
## MI_ref10 -0.0676200 0.08316 0.081390 -0.830700 0.406100
## CBT1 -0.0187800 0.07911 0.072500 -0.259100 0.795500
## TIME1 0.0536500 0.05130 0.040500 1.325000 0.185300
## MI_ref10:CBT1 0.0001742 0.11190 0.112500 0.001549 0.998800
## MI_ref10:TIME1 0.0253200 0.07158 0.069030 0.366800 0.713800
## CBT1:TIME1 0.0138500 0.06843 0.062170 0.222800 0.823700
## MI_ref10:CBT1:TIME1 -0.1746000 0.09640 0.096000 -1.819000 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.451000 0.05930 0.040970 108.70000 0.000000
## cAGE 0.014670 0.02263 0.023800 0.61620 0.537800
## cBLCGSMD 0.009046 0.00281 0.003239 2.79300 0.005224
## MI_ref10 -0.042290 0.08313 0.069030 -0.61260 0.540100
## CBT1 -0.004933 0.07908 0.059260 -0.08323 0.933700
## TIME_ref10 -0.053650 0.05130 0.040500 -1.32500 0.185300
## MI_ref10:CBT1 -0.174400 0.11190 0.105600 -1.65200 0.098580
## MI_ref10:TIME_ref10 -0.025320 0.07158 0.069030 -0.36680 0.713800
## CBT1:TIME_ref10 -0.013850 0.06843 0.062170 -0.22280 0.823700
## MI_ref10:CBT1:TIME_ref10 0.174600 0.09640 0.096000 1.81900 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.3110000 0.05316 0.056170 76.750000 0.000000
## cAGE 0.0146700 0.02263 0.023800 0.616200 0.537800
## cBLCGSMD 0.0090460 0.00281 0.003239 2.793000 0.005224
## MI1 0.0674400 0.07462 0.076760 0.878600 0.379600
## CBT_ref10 0.0186100 0.07880 0.085690 0.217200 0.828100
## TIME1 -0.0817500 0.04601 0.047170 -1.733000 0.083050
## MI1:CBT_ref10 0.0001742 0.11190 0.112500 0.001549 0.998800
## MI1:TIME1 0.1492000 0.06457 0.066710 2.237000 0.025260
## CBT_ref10:TIME1 0.1607000 0.06790 0.073140 2.197000 0.027990
## MI1:CBT_ref10:TIME1 -0.1746000 0.09640 0.096000 -1.819000 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.229000 0.05319 0.066420 63.6700 0.000000
## cAGE 0.014670 0.02263 0.023800 0.6162 0.537800
## cBLCGSMD 0.009046 0.00281 0.003239 2.7930 0.005224
## MI1 0.216700 0.07462 0.078930 2.7450 0.006043
## CBT_ref10 0.179300 0.07880 0.086900 2.0640 0.039060
## TIME_ref10 0.081750 0.04601 0.047170 1.7330 0.083050
## MI1:CBT_ref10 -0.174400 0.11190 0.105600 -1.6520 0.098580
## MI1:TIME_ref10 -0.149200 0.06457 0.066710 -2.2370 0.025260
## CBT_ref10:TIME_ref10 -0.160700 0.06790 0.073140 -2.1970 0.027990
## MI1:CBT_ref10:TIME_ref10 0.174600 0.09640 0.096000 1.8190 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.3790000 0.05233 0.052640 83.180000 0.000000
## cAGE 0.0146700 0.02263 0.023800 0.616200 0.537800
## cBLCGSMD 0.0090460 0.00281 0.003239 2.793000 0.005224
## MI_ref10 -0.0674400 0.07462 0.076760 -0.878600 0.379600
## CBT_ref10 0.0187800 0.07911 0.072500 0.259100 0.795500
## TIME1 0.0675000 0.04529 0.047170 1.431000 0.152500
## MI_ref10:CBT_ref10 -0.0001742 0.11190 0.112500 -0.001549 0.998800
## MI_ref10:TIME1 -0.1492000 0.06457 0.066710 -2.237000 0.025260
## CBT_ref10:TIME1 -0.0138500 0.06843 0.062170 -0.222800 0.823700
## MI_ref10:CBT_ref10:TIME1 0.1746000 0.09640 0.096000 1.819000 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
summary(PCSMKD_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.446000 0.05231 0.043060 103.30000 0.000000
## cAGE 0.014670 0.02263 0.023800 0.61620 0.537800
## cBLCGSMD 0.009046 0.00281 0.003239 2.79300 0.005224
## MI_ref10 -0.216700 0.07462 0.078930 -2.74500 0.006043
## CBT_ref10 0.004933 0.07908 0.059260 0.08323 0.933700
## TIME_ref10 -0.067500 0.04529 0.047170 -1.43100 0.152500
## MI_ref10:CBT_ref10 0.174400 0.11190 0.105600 1.65200 0.098580
## MI_ref10:TIME_ref10 0.149200 0.06457 0.066710 2.23700 0.025260
## CBT_ref10:TIME_ref10 0.013850 0.06843 0.062170 0.22280 0.823700
## MI_ref10:CBT_ref10:TIME_ref10 -0.174600 0.09640 0.096000 -1.81900 0.068990
##
## Estimated Correlation Parameter: 0.6249
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1837
##
## Number of GEE iterations: 2
## Number of Clusters: 243 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 486
# DV = PPVABS ---------------------------
#relevel MI and CBT
PPVABS_nobl_dt$MI_ref1 <- PPVABS_nobl_dt$MI %>% relevel("1")
PPVABS_nobl_dt$CBT_ref1 <- PPVABS_nobl_dt$CBT %>% relevel("1")
PPVABS_nobl_dt$TIME_ref1 <- PPVABS_nobl_dt$TIME %>% relevel("1")
# ignore missing values
## create an indicator column for any missing values
PPVABS_nobl_dt$any_na <- PPVABS_nobl_dt %>% apply(1, function(x){any(is.na(x))})
# ## left join the dataset by the id column using the group_by() function
PPVABS_nobl_dt %<>% left_join(PPVABS_nobl_dt %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
# ## filter out rows with any NAs
PPVABS_nobl_dt %<>% filter(any_na2 != T)
## fit main model
PPVABS_nobl_main <- geem(formula = PPVABS.nobl.main,
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
## Warning in geem(formula = PPVABS.nobl.main, data = PPVABS_nobl_dt, id = id, :
## Did not converge
## print model summary
summary(PPVABS_nobl_main)
## Estimates Model SE Robust SE wald p
## (Intercept) -1.338e+00 0.24400 0.24570 -5.445e+00 5.000e-08
## cAGE -3.475e-01 0.12080 0.10210 -3.403e+00 6.665e-04
## cBLCGSMD -2.310e-02 0.01568 0.01911 -1.209e+00 2.267e-01
## MI1 -1.101e-01 0.26170 0.25810 -4.267e-01 6.696e-01
## CBT1 1.659e-01 0.26240 0.26370 6.293e-01 5.291e-01
## TIME1 1.840e-17 0.13640 0.13510 1.361e-16 1.000e+00
##
## Estimated Correlation Parameter: 0.569
## Correlation Structure: ar1
## Est. Scale Parameter: 1.01
##
## Number of GEE iterations: 20
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
## fit 3-way interaction model
PPVABS_nobl_interaction <-
geem(formula = PPVABS.nobl.interaction,
#ref: Mi=0, Cbt=0, time=0
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
PPVABS_nobl_interaction_TIMEref1 <-
geem(formula = PPVABS ~ cAGE + cBLCGSMD + MI + CBT + TIME_ref1 + MI * CBT + MI *
TIME_ref1 + CBT * TIME_ref1 + MI * CBT * TIME_ref1,
#ref: Mi=0, Cbt=0, time=1
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
PPVABS_nobl_interaction_MIref1 <-
geem(formula = PPVABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 *
TIME + CBT * TIME + MI_ref1 * CBT * TIME,
#ref: Mi=1, Cbt=0, time=0
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
PPVABS_nobl_interaction_MIref1_TIMEref1 <-
geem(formula = PPVABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME_ref1 + MI_ref1 * CBT + MI_ref1 *
TIME_ref1 + CBT * TIME_ref1 + MI_ref1 * CBT * TIME_ref1,
#ref: Mi=1, Cbt=0, time=1
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
PPVABS_nobl_interaction_CBTref1 <-
geem(formula = PPVABS ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI *
TIME + CBT_ref1 * TIME + MI * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, time=0
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
PPVABS_nobl_interaction_CBTref1_TIMEref1 <-
geem(formula = PPVABS ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME_ref1 + MI * CBT_ref1 + MI *
TIME_ref1 + CBT_ref1 * TIME_ref1 + MI * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, time=1
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
PPVABS_nobl_interaction_MIref1_CBTref1 <-
geem(formula = PPVABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME + CBT_ref1 * TIME + MI_ref1 * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, time=0
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
PPVABS_nobl_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = PPVABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME_ref1 + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME_ref1 + CBT_ref1 * TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, time=1
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
## print interaction model summary
summary(PPVABS_nobl_interaction) #base: MI=0, CBT=0, TIME=0
## Estimates Model SE Robust SE wald p
## (Intercept) -1.29600 0.30370 0.3033 -4.27400 1.917e-05
## cAGE -0.35640 0.12210 0.1029 -3.46200 5.355e-04
## cBLCGSMD -0.02131 0.01562 0.0189 -1.12700 2.597e-01
## MI1 -0.49950 0.47360 0.4754 -1.05100 2.934e-01
## CBT1 0.16200 0.40480 0.4072 0.39790 6.907e-01
## TIME1 -0.08773 0.27320 0.2647 -0.33150 7.403e-01
## MI1:CBT1 0.50510 0.61170 0.6096 0.82870 4.073e-01
## MI1:TIME1 0.72270 0.41420 0.4566 1.58300 1.134e-01
## CBT1:TIME1 0.01319 0.37110 0.3621 0.03643 9.709e-01
## MI1:CBT1:TIME1 -0.97240 0.55010 0.5651 -1.72100 8.529e-02
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(PPVABS_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.38400 0.31010 0.3149 -4.39500 1.107e-05
## cAGE -0.35640 0.12210 0.1029 -3.46200 5.355e-04
## cBLCGSMD -0.02131 0.01562 0.0189 -1.12700 2.597e-01
## MI1 0.22320 0.43250 0.4362 0.51170 6.089e-01
## CBT1 0.17520 0.41270 0.4168 0.42040 6.742e-01
## TIME_ref10 0.08773 0.27320 0.2647 0.33150 7.403e-01
## MI1:CBT1 -0.46720 0.59440 0.5933 -0.78750 4.310e-01
## MI1:TIME_ref10 -0.72270 0.41420 0.4566 -1.58300 1.134e-01
## CBT1:TIME_ref10 -0.01319 0.37110 0.3621 -0.03643 9.709e-01
## MI1:CBT1:TIME_ref10 0.97240 0.55010 0.5651 1.72100 8.529e-02
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(PPVABS_nobl_interaction_MIref1) #ref: MI=1, CBT=0, TIME=0
## Estimates Model SE Robust SE wald p
## (Intercept) -1.79600 0.36710 0.3754 -4.7830 1.720e-06
## cAGE -0.35640 0.12210 0.1029 -3.4620 5.355e-04
## cBLCGSMD -0.02131 0.01562 0.0189 -1.1270 2.597e-01
## MI_ref10 0.49950 0.47360 0.4754 1.0510 2.934e-01
## CBT1 0.66720 0.45720 0.4552 1.4660 1.427e-01
## TIME1 0.63500 0.31130 0.3717 1.7080 8.760e-02
## MI_ref10:CBT1 -0.50510 0.61170 0.6096 -0.8287 4.073e-01
## MI_ref10:TIME1 -0.72270 0.41420 0.4566 -1.5830 1.134e-01
## CBT1:TIME1 -0.95920 0.40620 0.4334 -2.2130 2.690e-02
## MI_ref10:CBT1:TIME1 0.97240 0.55010 0.5651 1.7210 8.529e-02
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(PPVABS_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.16100 0.30430 0.3122 -3.7180 0.0002009
## cAGE -0.35640 0.12210 0.1029 -3.4620 0.0005355
## cBLCGSMD -0.02131 0.01562 0.0189 -1.1270 0.2597000
## MI_ref10 -0.22320 0.43250 0.4362 -0.5117 0.6089000
## CBT1 -0.29200 0.42520 0.4226 -0.6910 0.4895000
## TIME_ref10 -0.63500 0.31130 0.3717 -1.7080 0.0876000
## MI_ref10:CBT1 0.46720 0.59440 0.5933 0.7875 0.4310000
## MI_ref10:TIME_ref10 0.72270 0.41420 0.4566 1.5830 0.1134000
## CBT1:TIME_ref10 0.95920 0.40620 0.4334 2.2130 0.0269000
## MI_ref10:CBT1:TIME_ref10 -0.97240 0.55010 0.5651 -1.7210 0.0852900
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(PPVABS_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, TIME=0
## Estimates Model SE Robust SE wald p
## (Intercept) -1.134000 0.27390 0.2730 -4.15400 3.262e-05
## cAGE -0.356400 0.12210 0.1029 -3.46200 5.355e-04
## cBLCGSMD -0.021310 0.01562 0.0189 -1.12700 2.597e-01
## MI1 0.005599 0.38640 0.3780 0.01481 9.882e-01
## CBT_ref10 -0.162000 0.40480 0.4072 -0.39790 6.907e-01
## TIME1 -0.074540 0.25110 0.2471 -0.30170 7.629e-01
## MI1:CBT_ref10 -0.505100 0.61170 0.6096 -0.82870 4.073e-01
## MI1:TIME1 -0.249600 0.36200 0.3327 -0.75030 4.531e-01
## CBT_ref10:TIME1 -0.013190 0.37110 0.3621 -0.03643 9.709e-01
## MI1:CBT_ref10:TIME1 0.972400 0.55010 0.5651 1.72100 8.529e-02
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(PPVABS_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.20900 0.27890 0.2741 -4.41000 1.033e-05
## cAGE -0.35640 0.12210 0.1029 -3.46200 5.355e-04
## cBLCGSMD -0.02131 0.01562 0.0189 -1.12700 2.597e-01
## MI1 -0.24400 0.40660 0.3979 -0.61330 5.397e-01
## CBT_ref10 -0.17520 0.41270 0.4168 -0.42040 6.742e-01
## TIME_ref10 0.07454 0.25110 0.2471 0.30170 7.629e-01
## MI1:CBT_ref10 0.46720 0.59440 0.5933 0.78750 4.310e-01
## MI1:TIME_ref10 0.24960 0.36200 0.3327 0.75030 4.531e-01
## CBT_ref10:TIME_ref10 0.01319 0.37110 0.3621 0.03643 9.709e-01
## MI1:CBT_ref10:TIME_ref10 -0.97240 0.55010 0.5651 -1.72100 8.529e-02
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(PPVABS_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, TIME=0
## Estimates Model SE Robust SE wald p
## (Intercept) -1.129000 0.27520 0.2611 -4.32300 1.538e-05
## cAGE -0.356400 0.12210 0.1029 -3.46200 5.355e-04
## cBLCGSMD -0.021310 0.01562 0.0189 -1.12700 2.597e-01
## MI_ref10 -0.005599 0.38640 0.3780 -0.01481 9.882e-01
## CBT_ref10 -0.667200 0.45720 0.4552 -1.46600 1.427e-01
## TIME1 -0.324200 0.26080 0.2227 -1.45500 1.456e-01
## MI_ref10:CBT_ref10 0.505100 0.61170 0.6096 0.82870 4.073e-01
## MI_ref10:TIME1 0.249600 0.36200 0.3327 0.75030 4.531e-01
## CBT_ref10:TIME1 0.959200 0.40620 0.4334 2.21300 2.690e-02
## MI_ref10:CBT_ref10:TIME1 -0.972400 0.55010 0.5651 -1.72100 8.529e-02
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(PPVABS_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.45300 0.29890 0.2879 -5.0460 4.500e-07
## cAGE -0.35640 0.12210 0.1029 -3.4620 5.355e-04
## cBLCGSMD -0.02131 0.01562 0.0189 -1.1270 2.597e-01
## MI_ref10 0.24400 0.40660 0.3979 0.6133 5.397e-01
## CBT_ref10 0.29200 0.42520 0.4226 0.6910 4.895e-01
## TIME_ref10 0.32420 0.26080 0.2227 1.4550 1.456e-01
## MI_ref10:CBT_ref10 -0.46720 0.59440 0.5933 -0.7875 4.310e-01
## MI_ref10:TIME_ref10 -0.24960 0.36200 0.3327 -0.7503 4.531e-01
## CBT_ref10:TIME_ref10 -0.95920 0.40620 0.4334 -2.2130 2.690e-02
## MI_ref10:CBT_ref10:TIME_ref10 0.97240 0.55010 0.5651 1.7210 8.529e-02
##
## Estimated Correlation Parameter: 0.5833
## Correlation Structure: ar1
## Est. Scale Parameter: 1.014
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
# DV: NCIGS ---------------------------
#relevel MI and CBT
ncigs_dt_na.omit$MI_ref1 <- ncigs_dt_na.omit$MI %>% relevel("1")
ncigs_dt_na.omit$CBT_ref1 <- ncigs_dt_na.omit$CBT %>% relevel("1")
ncigs_dt_na.omit$TIME_ref1 <- ncigs_dt_na.omit$TIME %>% relevel("1")
ncigs_dt_na.omit$TIME_ref2 <- ncigs_dt_na.omit$TIME %>% relevel("2")
# ignore missing values
## create an indicator column for any missing values
ncigs_dt_na.omit$any_na <- ncigs_dt_na.omit %>% apply(1, function(x){any(is.na(x))})
# ## left join the dataset by the id column using the group_by() function
ncigs_dt_na.omit %<>% left_join(ncigs_dt_na.omit %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
# ## filter out rows with any NAs
ncigs_dt_na.omit %<>% filter(any_na2 != T)
## fit main model
ncigs_main <- geem(formula = ncigs.main,
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print model summary
summary(ncigs_main)
## Estimates Model SE Robust SE wald p
## (Intercept) 2.40600 0.09502 0.08162 29.4800 0.000000
## cAGE 0.11440 0.04536 0.04437 2.5770 0.009962
## MI1 0.03442 0.10080 0.09391 0.3665 0.714000
## CBT1 -0.01055 0.10090 0.09603 -0.1099 0.912500
## TIME1 -0.55700 0.05347 0.06819 -8.1680 0.000000
## TIME2 -0.47850 0.06816 0.07091 -6.7480 0.000000
##
## Estimated Correlation Parameter: 0.6187
## Correlation Structure: ar1
## Est. Scale Parameter: 1.002
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
## fit 3-way interaction model
ncigs_interaction <- geem(formula = ncigs.interaction,
#ref: Mi=0, Cbt=0, time=0
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_TIMEref1 <- geem(formula =
NCIGS ~ cAGE + MI + CBT + TIME_ref1 + MI * CBT + MI * TIME_ref1 + CBT *
TIME_ref1 + MI * CBT * TIME_ref1 + offset(log_unctrldays),
#ref: Mi=0, Cbt=0, time=1
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_TIMEref2 <- geem(formula =
NCIGS ~ cAGE + MI + CBT + TIME_ref2 + MI * CBT + MI * TIME_ref2 + CBT *
TIME_ref2 + MI * CBT * TIME_ref2 + offset(log_unctrldays),
#ref: Mi=0, Cbt=0, time=1
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_MIref1 <-
geem(formula = NCIGS ~ cAGE + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 * TIME + CBT *
TIME + MI_ref1 * CBT * TIME + offset(log_unctrldays),
#ref: Mi=1, Cbt=0, time=0
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_MIref1_TIMEref1 <-
geem(formula = NCIGS ~ cAGE + MI_ref1 + CBT + TIME_ref1 + MI_ref1 * CBT + MI_ref1 * TIME_ref1 + CBT *
TIME_ref1 + MI_ref1 * CBT * TIME_ref1 + offset(log_unctrldays),
#ref: Mi=1, Cbt=0, time=1
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_MIref1_TIMEref2 <-
geem(formula = NCIGS ~ cAGE + MI_ref1 + CBT + TIME_ref2 + MI_ref1 * CBT + MI_ref1 * TIME_ref2 + CBT *
TIME_ref2 + MI_ref1 * CBT * TIME_ref2 + offset(log_unctrldays),
#ref: Mi=1, Cbt=0, time=2
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_CBTref1 <-
geem(formula = NCIGS ~ cAGE + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI * TIME + CBT_ref1 *
TIME + MI * CBT_ref1 * TIME + offset(log_unctrldays),
#ref: Mi=0, Cbt=1, time=0
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_CBTref1_TIMEref1 <-
geem(formula = NCIGS ~ cAGE + MI + CBT_ref1 + TIME_ref1 + MI * CBT_ref1 + MI * TIME_ref1 + CBT_ref1 *
TIME_ref1 + MI * CBT_ref1 * TIME_ref1 + offset(log_unctrldays),
#ref: Mi=0, Cbt=1, time=1
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_CBTref1_TIMEref2 <-
geem(formula = NCIGS ~ cAGE + MI + CBT_ref1 + TIME_ref2 + MI * CBT_ref1 + MI * TIME_ref2 + CBT_ref1 *
TIME_ref2 + MI * CBT_ref1 * TIME_ref2 + offset(log_unctrldays),
#ref: Mi=0, Cbt=1, time=2
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_MIref1_CBTref1 <-
geem(formula = NCIGS ~ cAGE + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 * TIME + CBT_ref1 *
TIME + MI_ref1 * CBT_ref1 * TIME + offset(log_unctrldays),
#ref: MI_ref1=1, Cbt=1, time=0
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = NCIGS ~ cAGE + MI_ref1 + CBT_ref1 + TIME_ref1 + MI_ref1 * CBT_ref1 + MI_ref1 * TIME_ref1 + CBT_ref1 *
TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1 + offset(log_unctrldays),
#ref: MI_ref1=1, Cbt=1, time=1
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
ncigs_interaction_MIref1_CBTref1_TIMEref2 <-
geem(formula = NCIGS ~ cAGE + MI_ref1 + CBT_ref1 + TIME_ref2 + MI_ref1 * CBT_ref1 + MI_ref1 * TIME_ref2 + CBT_ref1 *
TIME_ref2 + MI_ref1 * CBT_ref1 * TIME_ref2 + offset(log_unctrldays),
#ref: MI_ref1=1, Cbt=1, time=2
data = ncigs_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print model summary
summary(ncigs_interaction) #ref: MI=0, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.32800 0.12470 0.11870 19.6000 0.000000
## cAGE 0.11290 0.04564 0.04463 2.5300 0.011400
## MI1 0.11850 0.17780 0.15330 0.7729 0.439600
## CBT1 0.18630 0.16950 0.15820 1.1780 0.239000
## TIME1 -0.48660 0.10920 0.17520 -2.7770 0.005493
## TIME2 -0.42420 0.14040 0.15860 -2.6760 0.007461
## MI1:CBT1 -0.27090 0.24160 0.20400 -1.3280 0.184200
## MI1:TIME1 -0.08472 0.15680 0.21690 -0.3905 0.696200
## MI1:TIME2 0.04657 0.20090 0.20570 0.2264 0.820900
## CBT1:TIME1 -0.19570 0.14940 0.21670 -0.9032 0.366400
## CBT1:TIME2 -0.21650 0.19140 0.21980 -0.9850 0.324600
## MI1:CBT1:TIME1 0.29440 0.21310 0.27600 1.0670 0.286100
## MI1:CBT1:TIME2 0.15550 0.27220 0.28010 0.5552 0.578800
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_TIMEref1) #ref: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.841000 0.12670 0.14300 12.87000 0.000000
## cAGE 0.112900 0.04564 0.04463 2.53000 0.011400
## MI1 0.033810 0.18170 0.21440 0.15770 0.874700
## CBT1 -0.009413 0.17280 0.19130 -0.04920 0.960800
## TIME_ref10 0.486600 0.10920 0.17520 2.77700 0.005493
## TIME_ref12 0.062350 0.11390 0.06659 0.93630 0.349100
## MI1:CBT1 0.023550 0.24640 0.27490 0.08567 0.931700
## MI1:TIME_ref10 0.084720 0.15680 0.21690 0.39050 0.696200
## MI1:TIME_ref12 0.131300 0.16320 0.12910 1.01700 0.309300
## CBT1:TIME_ref10 0.195700 0.14940 0.21670 0.90320 0.366400
## CBT1:TIME_ref12 -0.020810 0.15500 0.10510 -0.19810 0.843000
## MI1:CBT1:TIME_ref10 -0.294400 0.21310 0.27600 -1.06700 0.286100
## MI1:CBT1:TIME_ref12 -0.138900 0.22030 0.17550 -0.79140 0.428700
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_TIMEref2) #ref: MI=0, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 1.90300 0.12940 0.12340 15.4200 0.000000
## cAGE 0.11290 0.04564 0.04463 2.5300 0.011400
## MI1 0.16510 0.18550 0.19410 0.8505 0.395100
## CBT1 -0.03022 0.17620 0.18520 -0.1632 0.870300
## TIME_ref20 0.42420 0.14040 0.15860 2.6760 0.007461
## TIME_ref21 -0.06235 0.11390 0.06659 -0.9363 0.349100
## MI1:CBT1 -0.11540 0.25060 0.26350 -0.4377 0.661600
## MI1:TIME_ref20 -0.04657 0.20090 0.20570 -0.2264 0.820900
## MI1:TIME_ref21 -0.13130 0.16320 0.12910 -1.0170 0.309300
## CBT1:TIME_ref20 0.21650 0.19140 0.21980 0.9850 0.324600
## CBT1:TIME_ref21 0.02081 0.15500 0.10510 0.1981 0.843000
## MI1:CBT1:TIME_ref20 -0.15550 0.27220 0.28010 -0.5552 0.578800
## MI1:CBT1:TIME_ref21 0.13890 0.22030 0.17550 0.7914 0.428700
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.44600 0.12620 0.09717 25.1700 0.000e+00
## cAGE 0.11290 0.04564 0.04463 2.5300 1.140e-02
## MI_ref10 -0.11850 0.17780 0.15330 -0.7729 4.396e-01
## CBT1 -0.08460 0.17170 0.12750 -0.6634 5.071e-01
## TIME1 -0.57130 0.11250 0.12790 -4.4680 7.890e-06
## TIME2 -0.37770 0.14370 0.13110 -2.8800 3.978e-03
## MI_ref10:CBT1 0.27090 0.24160 0.20400 1.3280 1.842e-01
## MI_ref10:TIME1 0.08472 0.15680 0.21690 0.3905 6.962e-01
## MI_ref10:TIME2 -0.04657 0.20090 0.20570 -0.2264 8.209e-01
## CBT1:TIME1 0.09874 0.15200 0.17100 0.5774 5.636e-01
## CBT1:TIME2 -0.06098 0.19350 0.17370 -0.3510 7.256e-01
## MI_ref10:CBT1:TIME1 -0.29440 0.21310 0.27600 -1.0670 2.861e-01
## MI_ref10:CBT1:TIME2 -0.15550 0.27220 0.28010 -0.5552 5.788e-01
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.87500 0.12970 0.16000 11.72000 0.000e+00
## cAGE 0.11290 0.04564 0.04463 2.53000 1.140e-02
## MI_ref10 -0.03381 0.18170 0.21440 -0.15770 8.747e-01
## CBT1 0.01414 0.17530 0.19710 0.07175 9.428e-01
## TIME_ref10 0.57130 0.11250 0.12790 4.46800 7.890e-06
## TIME_ref12 0.19360 0.11690 0.11070 1.74900 8.037e-02
## MI_ref10:CBT1 -0.02355 0.24640 0.27490 -0.08567 9.317e-01
## MI_ref10:TIME_ref10 -0.08472 0.15680 0.21690 -0.39050 6.962e-01
## MI_ref10:TIME_ref12 -0.13130 0.16320 0.12910 -1.01700 3.093e-01
## CBT1:TIME_ref10 -0.09874 0.15200 0.17100 -0.57740 5.636e-01
## CBT1:TIME_ref12 -0.15970 0.15650 0.14060 -1.13600 2.561e-01
## MI_ref10:CBT1:TIME_ref10 0.29440 0.21310 0.27600 1.06700 2.861e-01
## MI_ref10:CBT1:TIME_ref12 0.13890 0.22030 0.17550 0.79140 4.287e-01
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_MIref1_TIMEref2) #ref: MI=1, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 2.06800 0.13240 0.14870 13.9100 0.000000
## cAGE 0.11290 0.04564 0.04463 2.5300 0.011400
## MI_ref10 -0.16510 0.18550 0.19410 -0.8505 0.395100
## CBT1 -0.14560 0.17780 0.18550 -0.7847 0.432600
## TIME_ref20 0.37770 0.14370 0.13110 2.8800 0.003978
## TIME_ref21 -0.19360 0.11690 0.11070 -1.7490 0.080370
## MI_ref10:CBT1 0.11540 0.25060 0.26350 0.4377 0.661600
## MI_ref10:TIME_ref20 0.04657 0.20090 0.20570 0.2264 0.820900
## MI_ref10:TIME_ref21 0.13130 0.16320 0.12910 1.0170 0.309300
## CBT1:TIME_ref20 0.06098 0.19350 0.17370 0.3510 0.725600
## CBT1:TIME_ref21 0.15970 0.15650 0.14060 1.1360 0.256100
## MI_ref10:CBT1:TIME_ref20 0.15550 0.27220 0.28010 0.5552 0.578800
## MI_ref10:CBT1:TIME_ref21 -0.13890 0.22030 0.17550 -0.7914 0.428700
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.5140 0.11480 0.10260 24.5000 0.000e+00
## cAGE 0.1129 0.04564 0.04463 2.5300 1.140e-02
## MI1 -0.1524 0.16340 0.13150 -1.1580 2.467e-01
## CBT_ref10 -0.1863 0.16950 0.15820 -1.1780 2.390e-01
## TIME1 -0.6823 0.10190 0.12750 -5.3530 9.000e-08
## TIME2 -0.6407 0.13010 0.15230 -4.2080 2.575e-05
## MI1:CBT_ref10 0.2709 0.24160 0.20400 1.3280 1.842e-01
## MI1:TIME1 0.2097 0.14430 0.17070 1.2290 2.192e-01
## MI1:TIME2 0.2021 0.18370 0.19020 1.0630 2.880e-01
## CBT_ref10:TIME1 0.1957 0.14940 0.21670 0.9032 3.664e-01
## CBT_ref10:TIME2 0.2165 0.19140 0.21980 0.9850 3.246e-01
## MI1:CBT_ref10:TIME1 -0.2944 0.21310 0.27600 -1.0670 2.861e-01
## MI1:CBT_ref10:TIME2 -0.1555 0.27220 0.28010 -0.5552 5.788e-01
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.832000 0.11740 0.12570 14.57000 0.000e+00
## cAGE 0.112900 0.04564 0.04463 2.53000 1.140e-02
## MI1 0.057360 0.16640 0.17030 0.33680 7.363e-01
## CBT_ref10 0.009413 0.17280 0.19130 0.04920 9.608e-01
## TIME_ref10 0.682300 0.10190 0.12750 5.35300 9.000e-08
## TIME_ref12 0.041540 0.10520 0.08130 0.51100 6.094e-01
## MI1:CBT_ref10 -0.023550 0.24640 0.27490 -0.08567 9.317e-01
## MI1:TIME_ref10 -0.209700 0.14430 0.17070 -1.22900 2.192e-01
## MI1:TIME_ref12 -0.007612 0.14790 0.11890 -0.06404 9.489e-01
## CBT_ref10:TIME_ref10 -0.195700 0.14940 0.21670 -0.90320 3.664e-01
## CBT_ref10:TIME_ref12 0.020810 0.15500 0.10510 0.19810 8.430e-01
## MI1:CBT_ref10:TIME_ref10 0.294400 0.21310 0.27600 1.06700 2.861e-01
## MI1:CBT_ref10:TIME_ref12 0.138900 0.22030 0.17550 0.79140 4.287e-01
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_CBTref1_TIMEref2) #ref: MI=0, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 1.873000 0.11950 0.13640 13.74000 0.000e+00
## cAGE 0.112900 0.04564 0.04463 2.53000 1.140e-02
## MI1 0.049750 0.16840 0.17560 0.28330 7.770e-01
## CBT_ref10 0.030220 0.17620 0.18520 0.16320 8.703e-01
## TIME_ref20 0.640700 0.13010 0.15230 4.20800 2.575e-05
## TIME_ref21 -0.041540 0.10520 0.08130 -0.51100 6.094e-01
## MI1:CBT_ref10 0.115400 0.25060 0.26350 0.43770 6.616e-01
## MI1:TIME_ref20 -0.202100 0.18370 0.19020 -1.06300 2.880e-01
## MI1:TIME_ref21 0.007612 0.14790 0.11890 0.06404 9.489e-01
## CBT_ref10:TIME_ref20 -0.216500 0.19140 0.21980 -0.98500 3.246e-01
## CBT_ref10:TIME_ref21 -0.020810 0.15500 0.10510 -0.19810 8.430e-01
## MI1:CBT_ref10:TIME_ref20 0.155500 0.27220 0.28010 0.55520 5.788e-01
## MI1:CBT_ref10:TIME_ref21 -0.138900 0.22030 0.17550 -0.79140 4.287e-01
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.36200 0.11630 0.08222 28.7200 0.000e+00
## cAGE 0.11290 0.04564 0.04463 2.5300 1.140e-02
## MI_ref10 0.15240 0.16340 0.13150 1.1580 2.467e-01
## CBT_ref10 0.08460 0.17170 0.12750 0.6634 5.071e-01
## TIME1 -0.47260 0.10210 0.11350 -4.1630 3.145e-05
## TIME2 -0.43860 0.12970 0.11400 -3.8470 1.194e-04
## MI_ref10:CBT_ref10 -0.27090 0.24160 0.20400 -1.3280 1.842e-01
## MI_ref10:TIME1 -0.20970 0.14430 0.17070 -1.2290 2.192e-01
## MI_ref10:TIME2 -0.20210 0.18370 0.19020 -1.0630 2.880e-01
## CBT_ref10:TIME1 -0.09874 0.15200 0.17100 -0.5774 5.636e-01
## CBT_ref10:TIME2 0.06098 0.19350 0.17370 0.3510 7.256e-01
## MI_ref10:CBT_ref10:TIME1 0.29440 0.21310 0.27600 1.0670 2.861e-01
## MI_ref10:CBT_ref10:TIME2 0.15550 0.27220 0.28010 0.5552 5.788e-01
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.889000 0.11800 0.11490 16.43000 0.000e+00
## cAGE 0.112900 0.04564 0.04463 2.53000 1.140e-02
## MI_ref10 -0.057360 0.16640 0.17030 -0.33680 7.363e-01
## CBT_ref10 -0.014140 0.17530 0.19710 -0.07175 9.428e-01
## TIME_ref10 0.472600 0.10210 0.11350 4.16300 3.145e-05
## TIME_ref12 0.033930 0.10400 0.08674 0.39120 6.956e-01
## MI_ref10:CBT_ref10 0.023550 0.24640 0.27490 0.08567 9.317e-01
## MI_ref10:TIME_ref10 0.209700 0.14430 0.17070 1.22900 2.192e-01
## MI_ref10:TIME_ref12 0.007612 0.14790 0.11890 0.06404 9.489e-01
## CBT_ref10:TIME_ref10 0.098740 0.15200 0.17100 0.57740 5.636e-01
## CBT_ref10:TIME_ref12 0.159700 0.15650 0.14060 1.13600 2.561e-01
## MI_ref10:CBT_ref10:TIME_ref10 -0.294400 0.21310 0.27600 -1.06700 2.861e-01
## MI_ref10:CBT_ref10:TIME_ref12 -0.138900 0.22030 0.17550 -0.79140 4.287e-01
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(ncigs_interaction_MIref1_CBTref1_TIMEref2) #ref: MI=1, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 1.923000 0.11870 0.11070 17.37000 0.0000000
## cAGE 0.112900 0.04564 0.04463 2.53000 0.0114000
## MI_ref10 -0.049750 0.16840 0.17560 -0.28330 0.7770000
## CBT_ref10 0.145600 0.17780 0.18550 0.78470 0.4326000
## TIME_ref20 0.438600 0.12970 0.11400 3.84700 0.0001194
## TIME_ref21 -0.033930 0.10400 0.08674 -0.39120 0.6956000
## MI_ref10:CBT_ref10 -0.115400 0.25060 0.26350 -0.43770 0.6616000
## MI_ref10:TIME_ref20 0.202100 0.18370 0.19020 1.06300 0.2880000
## MI_ref10:TIME_ref21 -0.007612 0.14790 0.11890 -0.06404 0.9489000
## CBT_ref10:TIME_ref20 -0.060980 0.19350 0.17370 -0.35100 0.7256000
## CBT_ref10:TIME_ref21 -0.159700 0.15650 0.14060 -1.13600 0.2561000
## MI_ref10:CBT_ref10:TIME_ref20 -0.155500 0.27220 0.28010 -0.55520 0.5788000
## MI_ref10:CBT_ref10:TIME_ref21 0.138900 0.22030 0.17550 0.79140 0.4287000
##
## Estimated Correlation Parameter: 0.624
## Correlation Structure: ar1
## Est. Scale Parameter: 1.001
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
# DV: SMDK ---------------------------
#relevel MI and CBT
SMDK_dt_na.omit$MI_ref1 <- SMDK_dt_na.omit$MI %>% relevel("1")
SMDK_dt_na.omit$CBT_ref1 <- SMDK_dt_na.omit$CBT %>% relevel("1")
SMDK_dt_na.omit$TIME_ref1 <- SMDK_dt_na.omit$TIME %>% relevel("1")
SMDK_dt_na.omit$TIME_ref2 <- SMDK_dt_na.omit$TIME %>% relevel("2")
# ignore missing values
## create an indicator column for any missing values
SMDK_dt_na.omit$any_na <- SMDK_dt_na.omit %>% apply(1, function(x){any(is.na(x))})
## left join the dataset by the id column using the group_by() function
SMDK_dt_na.omit %<>% left_join(SMDK_dt_na.omit %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
## filter out rows with any NAs
SMDK_dt_na.omit %<>% filter(any_na2 != T)
## fit main model
SMDK_main <- geem(formula = SMDK.main,
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print main model summary
summary(SMDK_main)
## Estimates Model SE Robust SE wald p
## (Intercept) -0.08032 0.03220 0.02432 -3.303 0.0009558
## cAGE 0.02530 0.01492 0.01339 1.890 0.0588100
## MI1 0.05554 0.03317 0.02938 1.890 0.0587300
## CBT1 -0.03473 0.03321 0.02935 -1.183 0.2367000
## TIME1 -0.21080 0.02223 0.02664 -7.912 0.0000000
## TIME2 -0.19200 0.02703 0.02752 -6.977 0.0000000
##
## Estimated Correlation Parameter: 0.4749
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1209
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
## fit interaction model
SMDK_interaction <- geem(formula = SMDK.interaction,
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_TIMEref1 <- geem(formula =
SMDK ~ cAGE + MI + CBT + TIME_ref1 + MI * CBT + MI * TIME_ref1 + CBT *
TIME_ref1 + MI * CBT * TIME_ref1 + offset(log_unctrldays),
#ref: Mi=0, Cbt=0, time=1
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_TIMEref2 <- geem(formula =
SMDK ~ cAGE + MI + CBT + TIME_ref2 + MI * CBT + MI * TIME_ref2 + CBT *
TIME_ref2 + MI * CBT * TIME_ref2 + offset(log_unctrldays),
#ref: Mi=0, Cbt=0, time=1
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_MIref1 <-
geem(formula = SMDK ~ cAGE + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 * TIME + CBT *
TIME + MI_ref1 * CBT * TIME + offset(log_unctrldays),
#ref: Mi=1, Cbt=0, time=0
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_MIref1_TIMEref1 <-
geem(formula = SMDK ~ cAGE + MI_ref1 + CBT + TIME_ref1 + MI_ref1 * CBT + MI_ref1 * TIME_ref1 + CBT *
TIME_ref1 + MI_ref1 * CBT * TIME_ref1 + offset(log_unctrldays),
#ref: Mi=1, Cbt=0, time=1
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_MIref1_TIMEref2 <-
geem(formula = SMDK ~ cAGE + MI_ref1 + CBT + TIME_ref2 + MI_ref1 * CBT + MI_ref1 * TIME_ref2 + CBT *
TIME_ref2 + MI_ref1 * CBT * TIME_ref2 + offset(log_unctrldays),
#ref: Mi=1, Cbt=0, time=2
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_CBTref1 <-
geem(formula = SMDK ~ cAGE + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI * TIME + CBT_ref1 *
TIME + MI * CBT_ref1 * TIME + offset(log_unctrldays),
#ref: Mi=0, Cbt=1, time=0
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_CBTref1_TIMEref1 <-
geem(formula = SMDK ~ cAGE + MI + CBT_ref1 + TIME_ref1 + MI * CBT_ref1 + MI * TIME_ref1 + CBT_ref1 *
TIME_ref1 + MI * CBT_ref1 * TIME_ref1 + offset(log_unctrldays),
#ref: Mi=0, Cbt=1, time=1
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_CBTref1_TIMEref2 <-
geem(formula = SMDK ~ cAGE + MI + CBT_ref1 + TIME_ref2 + MI * CBT_ref1 + MI * TIME_ref2 + CBT_ref1 *
TIME_ref2 + MI * CBT_ref1 * TIME_ref2 + offset(log_unctrldays),
#ref: Mi=0, Cbt=1, time=2
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_MIref1_CBTref1 <-
geem(formula = SMDK ~ cAGE + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 * TIME + CBT_ref1 *
TIME + MI_ref1 * CBT_ref1 * TIME + offset(log_unctrldays),
#ref: MI_ref1=1, Cbt=1, time=0
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = SMDK ~ cAGE + MI_ref1 + CBT_ref1 + TIME_ref1 + MI_ref1 * CBT_ref1 + MI_ref1 * TIME_ref1 + CBT_ref1 *
TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1 + offset(log_unctrldays),
#ref: MI_ref1=1, Cbt=1, time=1
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
SMDK_interaction_MIref1_CBTref1_TIMEref2 <-
geem(formula = SMDK ~ cAGE + MI_ref1 + CBT_ref1 + TIME_ref2 + MI_ref1 * CBT_ref1 + MI_ref1 * TIME_ref2 + CBT_ref1 *
TIME_ref2 + MI_ref1 * CBT_ref1 * TIME_ref2 + offset(log_unctrldays),
#ref: MI_ref1=1, Cbt=1, time=2
data = SMDK_dt_na.omit,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction model summary
summary(SMDK_interaction) #ref: MI=0, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) -0.0916800 0.04436 0.02252 -4.071000 4.688e-05
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI1 0.0211400 0.06328 0.02733 0.773500 4.392e-01
## CBT1 0.0298700 0.06032 0.02805 1.065000 2.870e-01
## TIME1 -0.2095000 0.04589 0.05909 -3.546000 3.917e-04
## TIME2 -0.1404000 0.05625 0.05370 -2.614000 8.961e-03
## MI1:CBT1 -0.0254400 0.08595 0.03711 -0.685600 4.930e-01
## MI1:TIME1 0.0311200 0.06584 0.07422 0.419300 6.750e-01
## MI1:TIME2 0.0313600 0.08037 0.06463 0.485200 6.275e-01
## CBT1:TIME1 -0.0304000 0.06265 0.07843 -0.387600 6.983e-01
## CBT1:TIME2 -0.2042000 0.07665 0.08605 -2.373000 1.765e-02
## MI1:CBT1:TIME1 -0.0008054 0.08935 0.10550 -0.007637 9.939e-01
## MI1:CBT1:TIME2 0.1546000 0.10890 0.10650 1.451000 1.468e-01
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_TIMEref1) #ref: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.3012000 0.04556 0.05909 -5.098000 3.400e-07
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI1 0.0522500 0.06541 0.07627 0.685100 4.933e-01
## CBT1 -0.0005307 0.06209 0.07857 -0.006755 9.946e-01
## TIME_ref10 0.2095000 0.04589 0.05909 3.546000 3.917e-04
## TIME_ref12 0.0691600 0.04770 0.05043 1.371000 1.702e-01
## MI1:CBT1 -0.0262500 0.08856 0.10580 -0.248100 8.041e-01
## MI1:TIME_ref10 -0.0311200 0.06584 0.07422 -0.419300 6.750e-01
## MI1:TIME_ref12 0.0002409 0.06835 0.06348 0.003795 9.970e-01
## CBT1:TIME_ref10 0.0304000 0.06265 0.07843 0.387600 6.983e-01
## CBT1:TIME_ref12 -0.1738000 0.06493 0.06856 -2.535000 1.124e-02
## MI1:CBT1:TIME_ref10 0.0008054 0.08935 0.10550 0.007637 9.939e-01
## MI1:CBT1:TIME_ref12 0.1554000 0.09225 0.09248 1.680000 9.296e-02
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_TIMEref2) #ref: MI=0, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.2320000 0.04656 0.05286 -4.390000 1.133e-05
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI1 0.0524900 0.06671 0.06560 0.800200 4.236e-01
## CBT1 -0.1743000 0.06342 0.08520 -2.046000 4.076e-02
## TIME_ref20 0.1404000 0.05625 0.05370 2.614000 8.961e-03
## TIME_ref21 -0.0691600 0.04770 0.05043 -1.371000 1.702e-01
## MI1:CBT1 0.1291000 0.09004 0.10470 1.233000 2.176e-01
## MI1:TIME_ref20 -0.0313600 0.08037 0.06463 -0.485200 6.275e-01
## MI1:TIME_ref21 -0.0002409 0.06835 0.06348 -0.003795 9.970e-01
## CBT1:TIME_ref20 0.2042000 0.07665 0.08605 2.373000 1.765e-02
## CBT1:TIME_ref21 0.1738000 0.06493 0.06856 2.535000 1.124e-02
## MI1:CBT1:TIME_ref20 -0.1546000 0.10890 0.10650 -1.451000 1.468e-01
## MI1:CBT1:TIME_ref21 -0.1554000 0.09225 0.09248 -1.680000 9.296e-02
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) -0.0705500 0.04494 0.01407 -5.014000 5.300e-07
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI_ref10 -0.0211400 0.06328 0.02733 -0.773500 4.392e-01
## CBT1 0.0044220 0.06109 0.02328 0.190000 8.493e-01
## TIME1 -0.1784000 0.04721 0.04483 -3.980000 6.905e-05
## TIME2 -0.1090000 0.05741 0.03597 -3.030000 2.446e-03
## MI_ref10:CBT1 0.0254400 0.08595 0.03711 0.685600 4.930e-01
## MI_ref10:TIME1 -0.0311200 0.06584 0.07422 -0.419300 6.750e-01
## MI_ref10:TIME2 -0.0313600 0.08037 0.06463 -0.485200 6.275e-01
## CBT1:TIME1 -0.0312000 0.06371 0.07048 -0.442700 6.580e-01
## CBT1:TIME2 -0.0496200 0.07738 0.06280 -0.790300 4.294e-01
## MI_ref10:CBT1:TIME1 0.0008054 0.08935 0.10550 0.007637 9.939e-01
## MI_ref10:CBT1:TIME2 -0.1546000 0.10890 0.10650 -1.451000 1.468e-01
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.2490000 0.04680 0.04807 -5.179000 2.200e-07
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI_ref10 -0.0522500 0.06541 0.07627 -0.685100 4.933e-01
## CBT1 -0.0267800 0.06305 0.07063 -0.379200 7.046e-01
## TIME_ref10 0.1784000 0.04721 0.04483 3.980000 6.905e-05
## TIME_ref12 0.0694000 0.04896 0.03854 1.801000 7.170e-02
## MI_ref10:CBT1 0.0262500 0.08856 0.10580 0.248100 8.041e-01
## MI_ref10:TIME_ref10 0.0311200 0.06584 0.07422 0.419300 6.750e-01
## MI_ref10:TIME_ref12 -0.0002409 0.06835 0.06348 -0.003795 9.970e-01
## CBT1:TIME_ref10 0.0312000 0.06371 0.07048 0.442700 6.580e-01
## CBT1:TIME_ref12 -0.0184200 0.06553 0.06205 -0.296900 7.666e-01
## MI_ref10:CBT1:TIME_ref10 -0.0008054 0.08935 0.10550 -0.007637 9.939e-01
## MI_ref10:CBT1:TIME_ref12 -0.1554000 0.09225 0.09248 -1.680000 9.296e-02
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_MIref1_TIMEref2) #ref: MI=1, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.1795000 0.04761 0.03848 -4.665000 3.080e-06
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI_ref10 -0.0524900 0.06671 0.06560 -0.800200 4.236e-01
## CBT1 -0.0452000 0.06381 0.06037 -0.748800 4.540e-01
## TIME_ref20 0.1090000 0.05741 0.03597 3.030000 2.446e-03
## TIME_ref21 -0.0694000 0.04896 0.03854 -1.801000 7.170e-02
## MI_ref10:CBT1 -0.1291000 0.09004 0.10470 -1.233000 2.176e-01
## MI_ref10:TIME_ref20 0.0313600 0.08037 0.06463 0.485200 6.275e-01
## MI_ref10:TIME_ref21 0.0002409 0.06835 0.06348 0.003795 9.970e-01
## CBT1:TIME_ref20 0.0496200 0.07738 0.06280 0.790300 4.294e-01
## CBT1:TIME_ref21 0.0184200 0.06553 0.06205 0.296900 7.666e-01
## MI_ref10:CBT1:TIME_ref20 0.1546000 0.10890 0.10650 1.451000 1.468e-01
## MI_ref10:CBT1:TIME_ref21 0.1554000 0.09225 0.09248 1.680000 9.296e-02
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) -0.0618200 0.04087 0.01628 -3.797000 1.464e-04
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI1 -0.0043090 0.05816 0.02469 -0.174500 8.615e-01
## CBT_ref10 -0.0298700 0.06032 0.02805 -1.065000 2.870e-01
## TIME1 -0.2399000 0.04265 0.05164 -4.646000 3.390e-06
## TIME2 -0.3445000 0.05207 0.06732 -5.118000 3.100e-07
## MI1:CBT_ref10 0.0254400 0.08595 0.03711 0.685600 4.930e-01
## MI1:TIME1 0.0303100 0.06041 0.07497 0.404300 6.860e-01
## MI1:TIME2 0.1859000 0.07350 0.08471 2.195000 2.818e-02
## CBT_ref10:TIME1 0.0304000 0.06265 0.07843 0.387600 6.983e-01
## CBT_ref10:TIME2 0.2042000 0.07665 0.08605 2.373000 1.765e-02
## MI1:CBT_ref10:TIME1 0.0008054 0.08935 0.10550 0.007637 9.939e-01
## MI1:CBT_ref10:TIME2 -0.1546000 0.10890 0.10650 -1.451000 1.468e-01
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.3017000 0.04215 0.05122 -5.891000 0.000e+00
## cAGE 0.0265900 0.01515 0.01375 1.933000 5.321e-02
## MI1 0.0260000 0.05969 0.07283 0.357000 7.211e-01
## CBT_ref10 0.0005307 0.06209 0.07857 0.006755 9.946e-01
## TIME_ref10 0.2399000 0.04265 0.05164 4.646000 3.390e-06
## TIME_ref12 -0.1046000 0.04405 0.04644 -2.253000 2.425e-02
## MI1:CBT_ref10 0.0262500 0.08856 0.10580 0.248100 8.041e-01
## MI1:TIME_ref10 -0.0303100 0.06041 0.07497 -0.404300 6.860e-01
## MI1:TIME_ref12 0.1556000 0.06195 0.06724 2.314000 2.066e-02
## CBT_ref10:TIME_ref10 -0.0304000 0.06265 0.07843 -0.387600 6.983e-01
## CBT_ref10:TIME_ref12 0.1738000 0.06493 0.06856 2.535000 1.124e-02
## MI1:CBT_ref10:TIME_ref10 -0.0008054 0.08935 0.10550 -0.007637 9.939e-01
## MI1:CBT_ref10:TIME_ref12 -0.1554000 0.09225 0.09248 -1.680000 9.296e-02
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_CBTref1_TIMEref2) #ref: MI=0, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.40640 0.04301 0.06625 -6.134 0.000e+00
## cAGE 0.02659 0.01515 0.01375 1.933 5.321e-02
## MI1 0.18160 0.06046 0.08095 2.243 2.487e-02
## CBT_ref10 0.17430 0.06342 0.08520 2.046 4.076e-02
## TIME_ref20 0.34450 0.05207 0.06732 5.118 3.100e-07
## TIME_ref21 0.10460 0.04405 0.04644 2.253 2.425e-02
## MI1:CBT_ref10 -0.12910 0.09004 0.10470 -1.233 2.176e-01
## MI1:TIME_ref20 -0.18590 0.07350 0.08471 -2.195 2.818e-02
## MI1:TIME_ref21 -0.15560 0.06195 0.06724 -2.314 2.066e-02
## CBT_ref10:TIME_ref20 -0.20420 0.07665 0.08605 -2.373 1.765e-02
## CBT_ref10:TIME_ref21 -0.17380 0.06493 0.06856 -2.535 1.124e-02
## MI1:CBT_ref10:TIME_ref20 0.15460 0.10890 0.10650 1.451 1.468e-01
## MI1:CBT_ref10:TIME_ref21 0.15540 0.09225 0.09248 1.680 9.296e-02
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 3
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) -0.0661300 0.04138 0.01856 -3.563000 0.0003670
## cAGE 0.0265900 0.01515 0.01375 1.933000 0.0532100
## MI_ref10 0.0043090 0.05816 0.02469 0.174500 0.8615000
## CBT_ref10 -0.0044220 0.06109 0.02328 -0.190000 0.8493000
## TIME1 -0.2096000 0.04278 0.05438 -3.855000 0.0001159
## TIME2 -0.1586000 0.05188 0.05149 -3.080000 0.0020670
## MI_ref10:CBT_ref10 -0.0254400 0.08595 0.03711 -0.685600 0.4930000
## MI_ref10:TIME1 -0.0303100 0.06041 0.07497 -0.404300 0.6860000
## MI_ref10:TIME2 -0.1859000 0.07350 0.08471 -2.195000 0.0281800
## CBT_ref10:TIME1 0.0312000 0.06371 0.07048 0.442700 0.6580000
## CBT_ref10:TIME2 0.0496200 0.07738 0.06280 0.790300 0.4294000
## MI_ref10:CBT_ref10:TIME1 -0.0008054 0.08935 0.10550 -0.007637 0.9939000
## MI_ref10:CBT_ref10:TIME2 0.1546000 0.10890 0.10650 1.451000 0.1468000
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.2757000 0.04227 0.05182 -5.321000 0.0000001
## cAGE 0.0265900 0.01515 0.01375 1.933000 0.0532100
## MI_ref10 -0.0260000 0.05969 0.07283 -0.357000 0.7211000
## CBT_ref10 0.0267800 0.06305 0.07063 0.379200 0.7046000
## TIME_ref10 0.2096000 0.04278 0.05438 3.855000 0.0001159
## TIME_ref12 0.0509800 0.04356 0.04864 1.048000 0.2946000
## MI_ref10:CBT_ref10 -0.0262500 0.08856 0.10580 -0.248100 0.8041000
## MI_ref10:TIME_ref10 0.0303100 0.06041 0.07497 0.404300 0.6860000
## MI_ref10:TIME_ref12 -0.1556000 0.06195 0.06724 -2.314000 0.0206600
## CBT_ref10:TIME_ref10 -0.0312000 0.06371 0.07048 -0.442700 0.6580000
## CBT_ref10:TIME_ref12 0.0184200 0.06553 0.06205 0.296900 0.7666000
## MI_ref10:CBT_ref10:TIME_ref10 0.0008054 0.08935 0.10550 0.007637 0.9939000
## MI_ref10:CBT_ref10:TIME_ref12 0.1554000 0.09225 0.09248 1.680000 0.0929600
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
summary(SMDK_interaction_MIref1_CBTref1_TIMEref2) #ref: MI=1, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.22470 0.04250 0.04663 -4.8200 1.430e-06
## cAGE 0.02659 0.01515 0.01375 1.9330 5.321e-02
## MI_ref10 -0.18160 0.06046 0.08095 -2.2430 2.487e-02
## CBT_ref10 0.04520 0.06381 0.06037 0.7488 4.540e-01
## TIME_ref20 0.15860 0.05188 0.05149 3.0800 2.067e-03
## TIME_ref21 -0.05098 0.04356 0.04864 -1.0480 2.946e-01
## MI_ref10:CBT_ref10 0.12910 0.09004 0.10470 1.2330 2.176e-01
## MI_ref10:TIME_ref20 0.18590 0.07350 0.08471 2.1950 2.818e-02
## MI_ref10:TIME_ref21 0.15560 0.06195 0.06724 2.3140 2.066e-02
## CBT_ref10:TIME_ref20 -0.04962 0.07738 0.06280 -0.7903 4.294e-01
## CBT_ref10:TIME_ref21 -0.01842 0.06553 0.06205 -0.2969 7.666e-01
## MI_ref10:CBT_ref10:TIME_ref20 -0.15460 0.10890 0.10650 -1.4510 1.468e-01
## MI_ref10:CBT_ref10:TIME_ref21 -0.15540 0.09225 0.09248 -1.6800 9.296e-02
##
## Estimated Correlation Parameter: 0.4817
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1228
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 791
# DV = LONGABS ---------------------------
## fit main model
#relevel MI and CBT
LONGABS_nobl_dt$MI_ref1 <- LONGABS_nobl_dt$MI %>% relevel("1")
LONGABS_nobl_dt$CBT_ref1 <- LONGABS_nobl_dt$CBT %>% relevel("1")
LONGABS_nobl_dt$TIME_ref1 <- LONGABS_nobl_dt$TIME %>% relevel("1")
CLONGABS_nobl_main <- geem(formula =
CLONGABS ~ cAGE + cBLCGSMD + MI + CBT + TIME,
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print main model summary
summary(CLONGABS_nobl_main) #unlogged
## Estimates Model SE Robust SE wald p
## (Intercept) 1.725000 0.23570 0.22090 7.80600 0.000000
## cAGE -0.128900 0.11610 0.11420 -1.12900 0.258900
## cBLCGSMD -0.001244 0.01455 0.01597 -0.07790 0.937900
## MI1 -0.747300 0.25370 0.24840 -3.00800 0.002628
## CBT1 0.666200 0.25430 0.24460 2.72400 0.006453
## TIME1 0.008899 0.13590 0.13410 0.06635 0.947100
##
## Estimated Correlation Parameter: 0.5516
## Correlation Structure: ar1
## Est. Scale Parameter: 4.821
##
## Number of GEE iterations: 4
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
## fit interaction models
CLONGABS_nobl_interaction <- geem(formula = CLONGABS.nobl.interaction,
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction models' summary
summary(CLONGABS_nobl_interaction) #unlogged
## Estimates Model SE Robust SE wald p
## (Intercept) -1.93700 0.24060 0.22920 -8.4500 0.00000
## cAGE -0.17350 0.09237 0.10340 -1.6780 0.09328
## cBLCGSMD -0.01200 0.01158 0.01789 -0.6710 0.50220
## MI1 -0.54560 0.35440 0.36740 -1.4850 0.13750
## CBT1 0.31990 0.32360 0.29100 1.0990 0.27160
## TIME1 -0.07626 0.24310 0.23590 -0.3233 0.74650
## MI1:CBT1 0.32140 0.46990 0.47210 0.6809 0.49600
## MI1:TIME1 -0.47490 0.37020 0.40590 -1.1700 0.24200
## CBT1:TIME1 0.32360 0.32630 0.27670 1.1690 0.24220
## MI1:CBT1:TIME1 -0.06697 0.48430 0.50950 -0.1315 0.89540
##
## Estimated Correlation Parameter: 0.5029
## Correlation Structure: ar1
## Est. Scale Parameter: 2.94
##
## Number of GEE iterations: 17
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
# DV = LONGABS ---------------------------
# #relevel MI and CBT
LONGABS_nobl_dt$MI_ref1 <- LONGABS_nobl_dt$MI %>% relevel("1")
LONGABS_nobl_dt$CBT_ref1 <- LONGABS_nobl_dt$CBT %>% relevel("1")
LONGABS_nobl_dt$TIME_ref1 <- LONGABS_nobl_dt$TIME %>% relevel("1")
# ignore missing values
## create an indicator column for any missing values
LONGABS_nobl_dt$any_na <- LONGABS_nobl_dt %>% apply(1, function(x){any(is.na(x))})
## left join the dataset by the id column using the group_by() function
LONGABS_nobl_dt %<>% left_join(LONGABS_nobl_dt %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
## filter out rows with any NAs
LONGABS_nobl_dt %<>% filter(any_na2 != T)
## main model
LONGABS_nobl_main <- geem(formula = CLONGABS.nobl.main,
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## Warning in geem(formula = CLONGABS.nobl.main, data = LONGABS_nobl_dt, id = id,
## : Did not converge
## print main model summary
summary(LONGABS_nobl_main)
## Estimates Model SE Robust SE wald p
## (Intercept) -1.97100 0.18990 0.20800 -9.4740 0.000000
## cAGE -0.14630 0.09258 0.10710 -1.3660 0.171900
## cBLCGSMD -0.01598 0.01151 0.02026 -0.7886 0.430400
## MI1 -0.59970 0.20440 0.21530 -2.7860 0.005342
## CBT1 0.60390 0.20600 0.22100 2.7330 0.006283
## TIME1 -0.12680 0.11960 0.11920 -1.0640 0.287500
##
## Estimated Correlation Parameter: 0.4962
## Correlation Structure: ar1
## Est. Scale Parameter: 2.955
##
## Number of GEE iterations: 20
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
## interaction model
LONGABS_nobl_interaction <- geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI + CBT + TIME + MI * CBT + MI * TIME +
CBT * TIME + MI * CBT * TIME,
#mi=0, cbt=0, time=0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_TIMEref1 <- geem(formula =
CLONGABS ~ cAGE + cBLCGSMD + MI + CBT + TIME_ref1 + MI * CBT + MI * TIME_ref1 +
CBT * TIME_ref1 + MI * CBT * TIME_ref1,
#mi=0, cbt=0, time=1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 * TIME +
CBT * TIME + MI_ref1 * CBT * TIME,
#ref: Mi=1, Cbt=0, Time=0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1_TIMEref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME_ref1 +
MI_ref1 * CBT + MI_ref1 * TIME_ref1 +
CBT * TIME_ref1 + MI_ref1 * CBT * TIME_ref1,
#ref: Mi=1, Cbt=0, Time=1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_CBTref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI *
TIME + CBT_ref1 * TIME + MI * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, Time = 0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_CBTref1_TIMEref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME_ref1 +
MI * CBT_ref1 + MI * TIME_ref1 + CBT_ref1 * TIME_ref1 +
MI * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, Time = 1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1_CBTref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME + CBT_ref1 * TIME + MI_ref1 * CBT_ref1 * TIME,
#ref: Mi=1, Cbt=1, Time=0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME_ref1 +
MI_ref1 * CBT_ref1 + MI_ref1 * TIME_ref1 +
CBT_ref1 * TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1,
#ref: Mi=1, Cbt=1, Time=1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction model summary
summary(LONGABS_nobl_interaction) #base: MI=0, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.6550000 0.29820 0.25250 6.55500 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -0.5604000 0.43700 0.37890 -1.47900 0.13920
## CBT1 0.5545000 0.40080 0.31870 1.74000 0.08184
## TIME1 0.2209000 0.28000 0.26050 0.84810 0.39640
## MI1:CBT1 0.2036000 0.58100 0.51850 0.39270 0.69460
## MI1:TIME1 -0.6803000 0.42240 0.47810 -1.42300 0.15470
## CBT1:TIME1 0.0351800 0.37770 0.30920 0.11380 0.90940
## MI1:CBT1:TIME1 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.8760000 0.29340 0.30820 6.08800 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -1.2410000 0.45210 0.53510 -2.31900 0.02041
## CBT1 0.5897000 0.39510 0.35560 1.65800 0.09723
## TIME_ref10 -0.2209000 0.28000 0.26050 -0.84810 0.39640
## MI1:CBT1 0.4054000 0.59250 0.66560 0.60900 0.54250
## MI1:TIME_ref10 0.6803000 0.42240 0.47810 1.42300 0.15470
## CBT1:TIME_ref10 -0.0351800 0.37770 0.30920 -0.11380 0.90940
## MI1:CBT1:TIME_ref10 -0.2018000 0.55760 0.58310 -0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1) #ref: MI=1, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.0950000 0.31880 0.27900 3.92400 8.727e-05
## cAGE -0.1379000 0.11640 0.11550 -1.19500 2.323e-01
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 9.633e-01
## MI_ref10 0.5604000 0.43700 0.37890 1.47900 1.392e-01
## CBT1 0.7581000 0.41910 0.40670 1.86400 6.228e-02
## TIME1 -0.4594000 0.31630 0.40090 -1.14600 2.518e-01
## MI_ref10:CBT1 -0.2036000 0.58100 0.51850 -0.39270 6.946e-01
## MI_ref10:TIME1 0.6803000 0.42240 0.47810 1.42300 1.547e-01
## CBT1:TIME1 0.2369000 0.41020 0.49430 0.47930 6.317e-01
## MI_ref10:CBT1:TIME1 -0.2018000 0.55760 0.58310 -0.34600 7.293e-01
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 0.6352000 0.34350 0.42150 1.50700 0.13180
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI_ref10 1.2410000 0.45210 0.53510 2.31900 0.02041
## CBT1 0.9951000 0.44020 0.55310 1.79900 0.07202
## TIME_ref10 0.4594000 0.31630 0.40090 1.14600 0.25180
## MI_ref10:CBT1 -0.4054000 0.59250 0.66560 -0.60900 0.54250
## MI_ref10:TIME_ref10 -0.6803000 0.42240 0.47810 -1.42300 0.15470
## CBT1:TIME_ref10 -0.2369000 0.41020 0.49430 -0.47930 0.63170
## MI_ref10:CBT1:TIME_ref10 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.2100000 0.26730 0.19520 11.32000 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -0.3568000 0.38190 0.34760 -1.02700 0.30470
## CBT_ref10 -0.5545000 0.40080 0.31870 -1.74000 0.08184
## TIME1 0.2561000 0.25350 0.16670 1.53600 0.12450
## MI1:CBT_ref10 -0.2036000 0.58100 0.51850 -0.39270 0.69460
## MI1:TIME1 -0.4786000 0.36400 0.33300 -1.43700 0.15070
## CBT_ref10:TIME1 -0.0351800 0.37770 0.30920 -0.11380 0.90940
## MI1:CBT_ref10:TIME1 -0.2018000 0.55760 0.58310 -0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.4660000 0.26420 0.17860 13.81000 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -0.8354000 0.38220 0.38850 -2.15000 0.03153
## CBT_ref10 -0.5897000 0.39510 0.35560 -1.65800 0.09723
## TIME_ref10 -0.2561000 0.25350 0.16670 -1.53600 0.12450
## MI1:CBT_ref10 -0.4054000 0.59250 0.66560 -0.60900 0.54250
## MI1:TIME_ref10 0.4786000 0.36400 0.33300 1.43700 0.15070
## CBT_ref10:TIME_ref10 0.0351800 0.37770 0.30920 0.11380 0.90940
## MI1:CBT_ref10:TIME_ref10 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.8530000 0.27250 0.28890 6.41200 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI_ref10 0.3568000 0.38190 0.34760 1.02700 0.30470
## CBT_ref10 -0.7581000 0.41910 0.40670 -1.86400 0.06228
## TIME1 -0.2225000 0.26110 0.28850 -0.77130 0.44050
## MI_ref10:CBT_ref10 0.2036000 0.58100 0.51850 0.39270 0.69460
## MI_ref10:TIME1 0.4786000 0.36400 0.33300 1.43700 0.15070
## CBT_ref10:TIME1 -0.2369000 0.41020 0.49430 -0.47930 0.63170
## MI_ref10:CBT_ref10:TIME1 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.6300000 0.27590 0.34740 4.69300 0.0000027
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.2323000
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.9633000
## MI_ref10 0.8354000 0.38220 0.38850 2.15000 0.0315300
## CBT_ref10 -0.9951000 0.44020 0.55310 -1.79900 0.0720200
## TIME_ref10 0.2225000 0.26110 0.28850 0.77130 0.4405000
## MI_ref10:CBT_ref10 0.4054000 0.59250 0.66560 0.60900 0.5425000
## MI_ref10:TIME_ref10 -0.4786000 0.36400 0.33300 -1.43700 0.1507000
## CBT_ref10:TIME_ref10 0.2369000 0.41020 0.49430 0.47930 0.6317000
## MI_ref10:CBT_ref10:TIME_ref10 -0.2018000 0.55760 0.58310 -0.34600 0.7293000
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
# Save ---------------------------
save.image(file="gee-main-interaction-models.RData")