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
# 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")
## 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) 1.84800 0.105300 0.111600 16.5700 0.00000
## cAGE -0.02492 0.052540 0.058530 -0.4258 0.67030
## cBLCGSMD 0.04747 0.006111 0.005162 9.1960 0.00000
## MI1 0.05612 0.116100 0.113600 0.4940 0.62130
## CBT1 -0.17070 0.109500 0.118500 -1.4400 0.14980
## TIME1 0.09425 0.045210 0.048540 1.9420 0.05218
##
## Estimated Correlation Parameter: 0.7353
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8569
##
## Number of GEE iterations: 6
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
## 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) 1.922000 0.127000 0.143800 13.37000 0.00000
## cAGE -0.023870 0.052770 0.058810 -0.40590 0.68480
## cBLCGSMD 0.047940 0.006148 0.005109 9.38300 0.00000
## MI1 -0.115900 0.182000 0.184500 -0.62850 0.52970
## CBT1 -0.267300 0.164200 0.188500 -1.41800 0.15610
## TIME1 0.002878 0.091190 0.094910 0.03032 0.97580
## MI1:CBT1 0.224000 0.234800 0.255400 0.87680 0.38060
## MI1:TIME1 0.243200 0.130000 0.138300 1.75900 0.07861
## CBT1:TIME1 0.089640 0.125600 0.124300 0.72100 0.47090
## MI1:CBT1:TIME1 -0.269700 0.178300 0.190000 -1.42000 0.15570
##
## Estimated Correlation Parameter: 0.7488
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8521
##
## Number of GEE iterations: 6
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
summary(AVCIG_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.925000 0.131400 0.130100 14.80000 0.00000
## cAGE -0.023870 0.052770 0.058810 -0.40590 0.68480
## cBLCGSMD 0.047940 0.006148 0.005109 9.38300 0.00000
## MI1 0.127300 0.187700 0.188600 0.67490 0.49980
## CBT1 -0.177700 0.168500 0.183600 -0.96750 0.33330
## TIME_ref10 -0.002878 0.091190 0.094910 -0.03032 0.97580
## MI1:CBT1 -0.045700 0.240300 0.261200 -0.17490 0.86110
## MI1:TIME_ref10 -0.243200 0.130000 0.138300 -1.75900 0.07861
## CBT1:TIME_ref10 -0.089640 0.125600 0.124300 -0.72100 0.47090
## MI1:CBT1:TIME_ref10 0.269700 0.178300 0.190000 1.42000 0.15570
##
## Estimated Correlation Parameter: 0.7488
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8521
##
## Number of GEE iterations: 7
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
summary(AVCIG_nobl_interaction_MIref1) #ref: MI=1, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.80600 0.129500 0.114500 15.7700 0.00000
## cAGE -0.02387 0.052770 0.058810 -0.4059 0.68480
## cBLCGSMD 0.04794 0.006148 0.005109 9.3830 0.00000
## MI_ref10 0.11590 0.182000 0.184500 0.6285 0.52970
## CBT1 -0.04336 0.166900 0.172600 -0.2512 0.80160
## TIME1 0.24610 0.092660 0.100200 2.4560 0.01404
## MI_ref10:CBT1 -0.22400 0.234800 0.255400 -0.8768 0.38060
## MI_ref10:TIME1 -0.24320 0.130000 0.138300 -1.7590 0.07861
## CBT1:TIME1 -0.18000 0.126500 0.143200 -1.2570 0.20880
## MI_ref10:CBT1:TIME1 0.26970 0.178300 0.190000 1.4200 0.15570
##
## Estimated Correlation Parameter: 0.7488
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8521
##
## Number of GEE iterations: 7
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
summary(AVCIG_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.05200 0.133000 0.133500 15.3700 0.00000
## cAGE -0.02387 0.052770 0.058810 -0.4059 0.68480
## cBLCGSMD 0.04794 0.006148 0.005109 9.3830 0.00000
## MI_ref10 -0.12730 0.187700 0.188600 -0.6749 0.49980
## CBT1 -0.22340 0.170100 0.184500 -1.2110 0.22590
## TIME_ref10 -0.24610 0.092660 0.100200 -2.4560 0.01404
## MI_ref10:CBT1 0.04570 0.240300 0.261200 0.1749 0.86110
## MI_ref10:TIME_ref10 0.24320 0.130000 0.138300 1.7590 0.07861
## CBT1:TIME_ref10 0.18000 0.126500 0.143200 1.2570 0.20880
## MI_ref10:CBT1:TIME_ref10 -0.26970 0.178300 0.190000 -1.4200 0.15570
##
## Estimated Correlation Parameter: 0.7488
## Correlation Structure: ar1
## Est. Scale Parameter: 0.8521
##
## Number of GEE iterations: 7
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
summary(AVCIG_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.643000 0.118200 0.130600 12.59000 0.00000
## cAGE -0.024580 0.052480 0.058960 -0.41680 0.67680
## cBLCGSMD 0.049330 0.006084 0.005194 9.49800 0.00000
## MI1 0.222400 0.166300 0.176100 1.26300 0.20650
## CBT_ref10 0.293000 0.161400 0.230800 1.27000 0.20410
## TIME1 0.051300 0.085740 0.091560 0.56030 0.57530
## MI1:CBT_ref10 -0.488700 0.230500 0.287100 -1.70200 0.08869
## MI1:TIME1 0.020050 0.120600 0.131900 0.15200 0.87920
## CBT_ref10:TIME1 0.008267 0.127700 0.110800 0.07463 0.94050
## MI1:CBT_ref10:TIME1 0.152900 0.181600 0.184900 0.82700 0.40820
##
## Estimated Correlation Parameter: 0.7399
## Correlation Structure: ar1
## Est. Scale Parameter: 0.846
##
## Number of GEE iterations: 5
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
summary(AVCIG_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.695000 0.120100 0.155000 10.93000 0.0000
## cAGE -0.024580 0.052480 0.058960 -0.41680 0.6768
## cBLCGSMD 0.049330 0.006084 0.005194 9.49800 0.0000
## MI1 0.242500 0.168500 0.196300 1.23500 0.2167
## CBT_ref10 0.301300 0.165400 0.226300 1.33200 0.1830
## TIME_ref10 -0.051300 0.085740 0.091560 -0.56030 0.5753
## MI1:CBT_ref10 -0.335800 0.235600 0.293000 -1.14600 0.2517
## MI1:TIME_ref10 -0.020050 0.120600 0.131900 -0.15200 0.8792
## CBT_ref10:TIME_ref10 -0.008267 0.127700 0.110800 -0.07463 0.9405
## MI1:CBT_ref10:TIME_ref10 -0.152900 0.181600 0.184900 -0.82700 0.4082
##
## Estimated Correlation Parameter: 0.7399
## Correlation Structure: ar1
## Est. Scale Parameter: 0.846
##
## Number of GEE iterations: 5
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
summary(AVCIG_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.86600 0.117100 0.118600 15.7300 0.00000
## cAGE -0.02458 0.052480 0.058960 -0.4168 0.67680
## cBLCGSMD 0.04933 0.006084 0.005194 9.4980 0.00000
## MI_ref10 -0.22240 0.166300 0.176100 -1.2630 0.20650
## CBT_ref10 -0.19570 0.164000 0.170000 -1.1510 0.24980
## TIME1 0.07135 0.084860 0.094900 0.7518 0.45220
## MI_ref10:CBT_ref10 0.48870 0.230500 0.287100 1.7020 0.08869
## MI_ref10:TIME1 -0.02005 0.120600 0.131900 -0.1520 0.87920
## CBT_ref10:TIME1 0.16120 0.129000 0.147800 1.0900 0.27550
## MI_ref10:CBT_ref10:TIME1 -0.15290 0.181600 0.184900 -0.8270 0.40820
##
## Estimated Correlation Parameter: 0.7399
## Correlation Structure: ar1
## Est. Scale Parameter: 0.846
##
## Number of GEE iterations: 5
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
summary(AVCIG_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.93700 0.118200 0.121500 15.9500 0.0000
## cAGE -0.02458 0.052480 0.058960 -0.4168 0.6768
## cBLCGSMD 0.04933 0.006084 0.005194 9.4980 0.0000
## MI_ref10 -0.24250 0.168500 0.196300 -1.2350 0.2167
## CBT_ref10 -0.03453 0.167100 0.183800 -0.1878 0.8510
## TIME_ref10 -0.07135 0.084860 0.094900 -0.7518 0.4522
## MI_ref10:CBT_ref10 0.33580 0.235600 0.293000 1.1460 0.2517
## MI_ref10:TIME_ref10 0.02005 0.120600 0.131900 0.1520 0.8792
## CBT_ref10:TIME_ref10 -0.16120 0.129000 0.147800 -1.0900 0.2755
## MI_ref10:CBT_ref10:TIME_ref10 0.15290 0.181600 0.184900 0.8270 0.4082
##
## Estimated Correlation Parameter: 0.7399
## Correlation Structure: ar1
## Est. Scale Parameter: 0.846
##
## Number of GEE iterations: 5
## Number of Clusters: 274 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 520
# 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")
## 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) 2.085000 0.088400 0.094870 21.98000 0.00000
## cAGE -0.002086 0.044150 0.048260 -0.04322 0.96550
## cBLCGSMD 0.041330 0.005246 0.004316 9.57400 0.00000
## MI1 0.002836 0.097180 0.093440 0.03035 0.97580
## CBT1 -0.221100 0.083500 0.105900 -2.08800 0.03681
## TIME1 0.101000 0.036460 0.041910 2.40900 0.01599
##
## Estimated Correlation Parameter: 0.7525
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5691
##
## Number of GEE iterations: 8
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
## 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) 2.1950000 0.104600 0.11920 18.42000 0.00000
## cAGE 0.0005014 0.044090 0.04842 0.01036 0.99170
## cBLCGSMD 0.0424200 0.005259 0.00428 9.91000 0.00000
## MI1 -0.2359000 0.149500 0.16350 -1.44300 0.14910
## CBT1 -0.4099000 0.122000 0.16020 -2.55900 0.01051
## TIME1 0.0156900 0.073150 0.07804 0.20100 0.84070
## MI1:CBT1 0.4026000 0.179700 0.22660 1.77600 0.07567
## MI1:TIME1 0.1964000 0.103600 0.12340 1.59200 0.11140
## CBT1:TIME1 0.1435000 0.104800 0.10140 1.41600 0.15690
## MI1:CBT1:TIME1 -0.3240000 0.146100 0.16110 -2.01100 0.04427
##
## Estimated Correlation Parameter: 0.7632
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5636
##
## Number of GEE iterations: 8
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
summary(CGSMD_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.2110000 0.108300 0.11770 18.77000 0.00000
## cAGE 0.0005014 0.044090 0.04842 0.01036 0.99170
## cBLCGSMD 0.0424200 0.005259 0.00428 9.91000 0.00000
## MI1 -0.0394700 0.152800 0.17190 -0.22960 0.81840
## CBT1 -0.2664000 0.129000 0.16050 -1.66000 0.09695
## TIME_ref10 -0.0156900 0.073150 0.07804 -0.20100 0.84070
## MI1:CBT1 0.0785200 0.184600 0.23410 0.33540 0.73730
## MI1:TIME_ref10 -0.1964000 0.103600 0.12340 -1.59200 0.11140
## CBT1:TIME_ref10 -0.1435000 0.104800 0.10140 -1.41600 0.15690
## MI1:CBT1:TIME_ref10 0.3240000 0.146100 0.16110 2.01100 0.04427
##
## Estimated Correlation Parameter: 0.7632
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5636
##
## Number of GEE iterations: 8
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
summary(CGSMD_nobl_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.9590000 0.106600 0.11190 17.51000 0.00000
## cAGE 0.0005014 0.044090 0.04842 0.01036 0.99170
## cBLCGSMD 0.0424200 0.005259 0.00428 9.91000 0.00000
## MI_ref10 0.2359000 0.149500 0.16350 1.44300 0.14910
## CBT1 -0.0073280 0.131600 0.16100 -0.04551 0.96370
## TIME1 0.2121000 0.073440 0.09512 2.23000 0.02575
## MI_ref10:CBT1 -0.4026000 0.179700 0.22660 -1.77600 0.07567
## MI_ref10:TIME1 -0.1964000 0.103600 0.12340 -1.59200 0.11140
## CBT1:TIME1 -0.1805000 0.101900 0.12470 -1.44800 0.14770
## MI_ref10:CBT1:TIME1 0.3240000 0.146100 0.16110 2.01100 0.04427
##
## Estimated Correlation Parameter: 0.7632
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5636
##
## Number of GEE iterations: 9
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
summary(CGSMD_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.1710000 0.107200 0.12370 17.55000 0.00000
## cAGE 0.0005014 0.044090 0.04842 0.01036 0.99170
## cBLCGSMD 0.0424200 0.005259 0.00428 9.91000 0.00000
## MI_ref10 0.0394700 0.152800 0.17190 0.22960 0.81840
## CBT1 -0.1879000 0.131400 0.16990 -1.10600 0.26890
## TIME_ref10 -0.2121000 0.073440 0.09512 -2.23000 0.02575
## MI_ref10:CBT1 -0.0785200 0.184600 0.23410 -0.33540 0.73730
## MI_ref10:TIME_ref10 0.1964000 0.103600 0.12340 1.59200 0.11140
## CBT1:TIME_ref10 0.1805000 0.101900 0.12470 1.44800 0.14770
## MI_ref10:CBT1:TIME_ref10 -0.3240000 0.146100 0.16110 -2.01100 0.04427
##
## Estimated Correlation Parameter: 0.7632
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5636
##
## Number of GEE iterations: 8
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
summary(CGSMD_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.909000 0.096660 0.104400 18.29000 0.00000
## cAGE -0.003254 0.043760 0.048720 -0.06679 0.94670
## cBLCGSMD 0.041540 0.005173 0.004349 9.55200 0.00000
## MI1 0.109500 0.137700 0.153400 0.71340 0.47560
## CBT_ref10 0.122500 0.128100 0.196100 0.62490 0.53210
## TIME1 0.154700 0.071760 0.076060 2.03300 0.04201
## MI1:CBT_ref10 -0.264900 0.184100 0.255600 -1.03600 0.30000
## MI1:TIME1 -0.107600 0.100200 0.111300 -0.96700 0.33350
## CBT_ref10:TIME1 -0.126300 0.107400 0.093820 -1.34600 0.17830
## MI1:CBT_ref10:TIME1 0.263800 0.150500 0.157500 1.67500 0.09384
##
## Estimated Correlation Parameter: 0.7437
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5571
##
## Number of GEE iterations: 4
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
summary(CGSMD_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.064000 0.104300 0.126600 16.310000 0.00000
## cAGE -0.003254 0.043760 0.048720 -0.066790 0.94670
## cBLCGSMD 0.041540 0.005173 0.004349 9.552000 0.00000
## MI1 0.001852 0.142500 0.165600 0.011180 0.99110
## CBT_ref10 -0.003770 0.135100 0.193600 -0.019480 0.98450
## TIME_ref10 -0.154700 0.071760 0.076060 -2.033000 0.04201
## MI1:CBT_ref10 -0.001065 0.189000 0.261500 -0.004075 0.99670
## MI1:TIME_ref10 0.107600 0.100200 0.111300 0.967000 0.33350
## CBT_ref10:TIME_ref10 0.126300 0.107400 0.093820 1.346000 0.17830
## MI1:CBT_ref10:TIME_ref10 -0.263800 0.150500 0.157500 -1.675000 0.09384
##
## Estimated Correlation Parameter: 0.7437
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5571
##
## Number of GEE iterations: 5
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
summary(CGSMD_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.019000 0.098120 0.112700 17.91000 0.00000
## cAGE -0.003254 0.043760 0.048720 -0.06679 0.94670
## cBLCGSMD 0.041540 0.005173 0.004349 9.55200 0.00000
## MI_ref10 -0.109500 0.137700 0.153400 -0.71340 0.47560
## CBT_ref10 -0.142400 0.131700 0.164400 -0.86590 0.38650
## TIME1 0.047050 0.069920 0.081220 0.57930 0.56240
## MI_ref10:CBT_ref10 0.264900 0.184100 0.255600 1.03600 0.30000
## MI_ref10:TIME1 0.107600 0.100200 0.111300 0.96700 0.33350
## CBT_ref10:TIME1 0.137600 0.105500 0.126400 1.08800 0.27640
## MI_ref10:CBT_ref10:TIME1 -0.263800 0.150500 0.157500 -1.67500 0.09384
##
## Estimated Correlation Parameter: 0.7437
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5571
##
## Number of GEE iterations: 4
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
summary(CGSMD_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.066000 0.097150 0.107500 19.210000 0.00000
## cAGE -0.003254 0.043760 0.048720 -0.066790 0.94670
## cBLCGSMD 0.041540 0.005173 0.004349 9.552000 0.00000
## MI_ref10 -0.001852 0.142500 0.165600 -0.011180 0.99110
## CBT_ref10 -0.004835 0.131600 0.174700 -0.027680 0.97790
## TIME_ref10 -0.047050 0.069920 0.081220 -0.579300 0.56240
## MI_ref10:CBT_ref10 0.001065 0.189000 0.261500 0.004075 0.99670
## MI_ref10:TIME_ref10 -0.107600 0.100200 0.111300 -0.967000 0.33350
## CBT_ref10:TIME_ref10 -0.137600 0.105500 0.126400 -1.088000 0.27640
## MI_ref10:CBT_ref10:TIME_ref10 0.263800 0.150500 0.157500 1.675000 0.09384
##
## Estimated Correlation Parameter: 0.7437
## Correlation Structure: ar1
## Est. Scale Parameter: 0.5571
##
## Number of GEE iterations: 5
## Number of Clusters: 268 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 484
# 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
## 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) 4.371000 0.044350 0.046030 94.9600 0.00000
## cAGE 0.016090 0.021920 0.022860 0.7041 0.48140
## cBLCGSMD 0.006992 0.002615 0.002987 2.3410 0.01923
## MI1 0.089370 0.048310 0.046880 1.9060 0.05660
## CBT1 -0.099800 0.046680 0.052030 -1.9180 0.05507
## TIME1 0.010360 0.023770 0.024890 0.4160 0.67740
##
## Estimated Correlation Parameter: 0.609
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1839
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
## 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) 4.393000 0.045800 0.046890 93.6800 0.00000
## cAGE 0.015850 0.021980 0.022910 0.6916 0.48920
## cBLCGSMD 0.006978 0.002622 0.002994 2.3300 0.01978
## MI1 0.046730 0.053210 0.052550 0.8893 0.37390
## CBT1 -0.102000 0.046790 0.051990 -1.9610 0.04989
## TIME1 -0.033300 0.033450 0.036840 -0.9039 0.36610
## MI1:TIME1 0.087810 0.047470 0.049750 1.7650 0.07758
##
## Estimated Correlation Parameter: 0.6118
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1846
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_MI_interactions_MIref1_CBT_ref0)
## Estimates Model SE Robust SE wald p
## (Intercept) 4.440000 0.046250 0.043540 102.0000 0.00000
## cAGE 0.015850 0.021980 0.022910 0.6916 0.48920
## cBLCGSMD 0.006978 0.002622 0.002994 2.3300 0.01978
## MI_ref10 -0.046730 0.053210 0.052550 -0.8893 0.37390
## TIME1 0.054510 0.033680 0.033440 1.6300 0.10310
## CBT1 -0.102000 0.046790 0.051990 -1.9610 0.04989
## MI_ref10:TIME1 -0.087810 0.047470 0.049750 -1.7650 0.07758
##
## Estimated Correlation Parameter: 0.6118
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1846
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_CBT_interactions)
## Estimates Model SE Robust SE wald p
## (Intercept) 4.352000 0.045990 0.048840 89.1000 0.00000
## cAGE 0.015810 0.021980 0.022870 0.6913 0.48930
## cBLCGSMD 0.007024 0.002622 0.002988 2.3510 0.01874
## MI1 0.090370 0.048440 0.046950 1.9250 0.05422
## CBT1 -0.066120 0.051890 0.057360 -1.1530 0.24910
## TIME1 0.051110 0.035230 0.034360 1.4880 0.13680
## CBT1:TIME1 -0.073710 0.048120 0.047900 -1.5390 0.12380
##
## Estimated Correlation Parameter: 0.612
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1846
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
## 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) 4.363000 0.05542 0.060770 71.80000 0.00000
## cAGE 0.016260 0.02218 0.023020 0.70640 0.48000
## cBLCGSMD 0.007137 0.00265 0.002996 2.38200 0.01720
## MI1 0.068330 0.07937 0.079370 0.86080 0.38930
## CBT1 -0.048210 0.07311 0.083860 -0.57490 0.56530
## TIME1 0.049990 0.04884 0.053920 0.92700 0.35390
## MI1:CBT1 -0.039660 0.10450 0.114300 -0.34700 0.72860
## MI1:TIME1 0.002832 0.06995 0.067920 0.04171 0.96670
## CBT1:TIME1 -0.155800 0.06733 0.071500 -2.17900 0.02930
## MI1:CBT1:TIME1 0.158500 0.09555 0.095300 1.66300 0.09628
##
## Estimated Correlation Parameter: 0.6209
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.413000 0.05729 0.055950 78.88000 0.00000
## cAGE 0.016260 0.02218 0.023020 0.70640 0.48000
## cBLCGSMD 0.007137 0.00265 0.002996 2.38200 0.01720
## MI1 0.071160 0.08222 0.070150 1.01400 0.31040
## CBT1 -0.204000 0.07548 0.090500 -2.25500 0.02416
## TIME_ref10 -0.049990 0.04884 0.053920 -0.92700 0.35390
## MI1:CBT1 0.118800 0.10770 0.114900 1.03400 0.30100
## MI1:TIME_ref10 -0.002832 0.06995 0.067920 -0.04171 0.96670
## CBT1:TIME_ref10 0.155800 0.06733 0.071500 2.17900 0.02930
## MI1:CBT1:TIME_ref10 -0.158500 0.09555 0.095300 -1.66300 0.09628
##
## Estimated Correlation Parameter: 0.6209
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_nobl_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.432000 0.05655 0.051110 86.72000 0.00000
## cAGE 0.016260 0.02218 0.023020 0.70640 0.48000
## cBLCGSMD 0.007137 0.00265 0.002996 2.38200 0.01720
## MI_ref10 -0.068330 0.07937 0.079370 -0.86080 0.38930
## CBT1 -0.087880 0.07439 0.077790 -1.13000 0.25860
## TIME1 0.052820 0.05008 0.041290 1.27900 0.20080
## MI_ref10:CBT1 0.039660 0.10450 0.114300 0.34700 0.72860
## MI_ref10:TIME1 -0.002832 0.06995 0.067920 -0.04171 0.96670
## CBT1:TIME1 0.002679 0.06780 0.062990 0.04253 0.96610
## MI_ref10:CBT1:TIME1 -0.158500 0.09555 0.095300 -1.66300 0.09628
##
## Estimated Correlation Parameter: 0.6209
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.485000 0.05861 0.041730 107.50000 0.00000
## cAGE 0.016260 0.02218 0.023020 0.70640 0.48000
## cBLCGSMD 0.007137 0.00265 0.002996 2.38200 0.01720
## MI_ref10 -0.071160 0.08222 0.070150 -1.01400 0.31040
## CBT1 -0.085200 0.07635 0.070400 -1.21000 0.22620
## TIME_ref10 -0.052820 0.05008 0.041290 -1.27900 0.20080
## MI_ref10:CBT1 -0.118800 0.10770 0.114900 -1.03400 0.30100
## MI_ref10:TIME_ref10 0.002832 0.06995 0.067920 0.04171 0.96670
## CBT1:TIME_ref10 -0.002679 0.06780 0.062990 -0.04253 0.96610
## MI_ref10:CBT1:TIME_ref10 0.158500 0.09555 0.095300 1.66300 0.09628
##
## Estimated Correlation Parameter: 0.6209
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.31200 0.051220 0.054080 79.7300 0.000000
## cAGE 0.01720 0.022190 0.023090 0.7447 0.456400
## cBLCGSMD 0.00732 0.002645 0.003005 2.4360 0.014860
## MI1 0.05612 0.072420 0.074520 0.7530 0.451400
## CBT_ref10 0.05516 0.072720 0.078140 0.7059 0.480200
## TIME1 -0.12420 0.045270 0.051600 -2.4070 0.016080
## MI1:CBT_ref10 -0.02230 0.103400 0.106700 -0.2090 0.834400
## MI1:TIME1 0.17490 0.063760 0.071940 2.4320 0.015020
## CBT_ref10:TIME1 0.19610 0.067500 0.073700 2.6610 0.007781
## MI1:CBT_ref10:TIME1 -0.18910 0.095970 0.097380 -1.9420 0.052160
##
## Estimated Correlation Parameter: 0.6204
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.18800 0.052750 0.070750 59.1900 0.000000
## cAGE 0.01720 0.022190 0.023090 0.7447 0.456400
## cBLCGSMD 0.00732 0.002645 0.003005 2.4360 0.014860
## MI1 0.23110 0.074020 0.085630 2.6980 0.006966
## CBT_ref10 0.25130 0.075040 0.086550 2.9040 0.003688
## TIME_ref10 0.12420 0.045270 0.051600 2.4070 0.016080
## MI1:CBT_ref10 -0.21140 0.106400 0.108000 -1.9580 0.050250
## MI1:TIME_ref10 -0.17490 0.063760 0.071940 -2.4320 0.015020
## CBT_ref10:TIME_ref10 -0.19610 0.067500 0.073700 -2.6610 0.007781
## MI1:CBT_ref10:TIME_ref10 0.18910 0.095970 0.097380 1.9420 0.052160
##
## Estimated Correlation Parameter: 0.6204
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 4.36800 0.051170 0.051460 84.8900 0.00000
## cAGE 0.01720 0.022190 0.023090 0.7447 0.45640
## cBLCGSMD 0.00732 0.002645 0.003005 2.4360 0.01486
## MI_ref10 -0.05612 0.072420 0.074520 -0.7530 0.45140
## CBT_ref10 0.03287 0.073260 0.072440 0.4537 0.65000
## TIME1 0.05073 0.044890 0.050150 1.0110 0.31180
## MI_ref10:CBT_ref10 0.02230 0.103400 0.106700 0.2090 0.83440
## MI_ref10:TIME1 -0.17490 0.063760 0.071940 -2.4320 0.01502
## CBT_ref10:TIME1 0.00705 0.068210 0.063670 0.1107 0.91180
## MI_ref10:CBT_ref10:TIME1 0.18910 0.095970 0.097380 1.9420 0.05216
##
## Estimated Correlation Parameter: 0.6204
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
summary(PCSMKD_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 4.41900 0.051890 0.048630 90.8700 0.000000
## cAGE 0.01720 0.022190 0.023090 0.7447 0.456400
## cBLCGSMD 0.00732 0.002645 0.003005 2.4360 0.014860
## MI_ref10 -0.23110 0.074020 0.085630 -2.6980 0.006966
## CBT_ref10 0.03992 0.075170 0.064020 0.6235 0.532900
## TIME_ref10 -0.05073 0.044890 0.050150 -1.0110 0.311800
## MI_ref10:CBT_ref10 0.21140 0.106400 0.108000 1.9580 0.050250
## MI_ref10:TIME_ref10 0.17490 0.063760 0.071940 2.4320 0.015020
## CBT_ref10:TIME_ref10 -0.00705 0.068210 0.063670 -0.1107 0.911800
## MI_ref10:CBT_ref10:TIME_ref10 -0.18910 0.095970 0.097380 -1.9420 0.052160
##
## Estimated Correlation Parameter: 0.6204
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1861
##
## Number of GEE iterations: 4
## Number of Clusters: 273 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 516
# 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")
## fit main model
PPVABS_nobl_main <- geem(formula = PPVABS.nobl.main,
data = PPVABS_nobl_dt,
id=id,
family = binomial,
corstr = corstr
)
## print model summary
summary(PPVABS_nobl_main)
## Estimates Model SE Robust SE wald p
## (Intercept) -1.37800 0.24030 0.24270 -5.6780 1.000e-08
## cAGE -0.35530 0.11790 0.09902 -3.5880 3.328e-04
## cBLCGSMD -0.02397 0.01528 0.01857 -1.2910 1.968e-01
## MI1 -0.09669 0.25680 0.25360 -0.3812 7.031e-01
## CBT1 0.17600 0.25800 0.26010 0.6767 4.986e-01
## TIME1 -0.04155 0.13650 0.13470 -0.3085 7.577e-01
##
## Estimated Correlation Parameter: 0.5563
## Correlation Structure: ar1
## Est. Scale Parameter: 1.011
##
## Number of GEE iterations: 3
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
## 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) -1.35800 0.30110 0.30090 -4.51400 6.370e-06
## cAGE -0.36400 0.11900 0.09947 -3.65900 2.529e-04
## cBLCGSMD -0.02180 0.01515 0.01825 -1.19500 2.321e-01
## MI1 -0.40040 0.45840 0.46050 -0.86940 3.847e-01
## CBT1 0.17370 0.40170 0.40410 0.42980 6.674e-01
## TIME1 -0.08657 0.27550 0.26100 -0.33170 7.401e-01
## MI1:CBT1 0.44670 0.59550 0.59450 0.75140 4.524e-01
## MI1:TIME1 0.58750 0.41040 0.45100 1.30300 1.927e-01
## CBT1:TIME1 0.01296 0.37410 0.35690 0.03632 9.710e-01
## MI1:CBT1:TIME1 -0.90330 0.54820 0.56060 -1.61100 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
summary(PPVABS_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.44500 0.30770 0.31250 -4.62400 3.760e-06
## cAGE -0.36400 0.11900 0.09947 -3.65900 2.529e-04
## cBLCGSMD -0.02180 0.01515 0.01825 -1.19500 2.321e-01
## MI1 0.18720 0.42790 0.43120 0.43400 6.643e-01
## CBT1 0.18660 0.40980 0.41380 0.45090 6.520e-01
## TIME_ref10 0.08657 0.27550 0.26100 0.33170 7.401e-01
## MI1:CBT1 -0.45660 0.58880 0.58670 -0.77830 4.364e-01
## MI1:TIME_ref10 -0.58750 0.41040 0.45100 -1.30300 1.927e-01
## CBT1:TIME_ref10 -0.01296 0.37410 0.35690 -0.03632 9.710e-01
## MI1:CBT1:TIME_ref10 0.90330 0.54820 0.56060 1.61100 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
summary(PPVABS_nobl_interaction_MIref1) #ref: MI=1, CBT=0, TIME=0
## Estimates Model SE Robust SE wald p
## (Intercept) -1.7590 0.34920 0.35790 -4.9140 8.900e-07
## cAGE -0.3640 0.11900 0.09947 -3.6590 2.529e-04
## cBLCGSMD -0.0218 0.01515 0.01825 -1.1950 2.321e-01
## MI_ref10 0.4004 0.45840 0.46050 0.8694 3.847e-01
## CBT1 0.6203 0.43820 0.43840 1.4150 1.570e-01
## TIME1 0.5010 0.30420 0.36760 1.3630 1.729e-01
## MI_ref10:CBT1 -0.4467 0.59550 0.59450 -0.7514 4.524e-01
## MI_ref10:TIME1 -0.5875 0.41040 0.45100 -1.3030 1.927e-01
## CBT1:TIME1 -0.8903 0.40070 0.43190 -2.0610 3.926e-02
## MI_ref10:CBT1:TIME1 0.9033 0.54820 0.56060 1.6110 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
summary(PPVABS_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.2580 0.30030 0.30800 -4.0840 4.421e-05
## cAGE -0.3640 0.11900 0.09947 -3.6590 2.529e-04
## cBLCGSMD -0.0218 0.01515 0.01825 -1.1950 2.321e-01
## MI_ref10 -0.1872 0.42790 0.43120 -0.4340 6.643e-01
## CBT1 -0.2700 0.42040 0.41810 -0.6457 5.185e-01
## TIME_ref10 -0.5010 0.30420 0.36760 -1.3630 1.729e-01
## MI_ref10:CBT1 0.4566 0.58880 0.58670 0.7783 4.364e-01
## MI_ref10:TIME_ref10 0.5875 0.41040 0.45100 1.3030 1.927e-01
## CBT1:TIME_ref10 0.8903 0.40070 0.43190 2.0610 3.926e-02
## MI_ref10:CBT1:TIME_ref10 -0.9033 0.54820 0.56060 -1.6110 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
summary(PPVABS_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, TIME=0
## Estimates Model SE Robust SE wald p
## (Intercept) -1.18500 0.27110 0.26960 -4.39500 1.108e-05
## cAGE -0.36400 0.11900 0.09947 -3.65900 2.529e-04
## cBLCGSMD -0.02180 0.01515 0.01825 -1.19500 2.321e-01
## MI1 0.04630 0.37910 0.37260 0.12430 9.011e-01
## CBT_ref10 -0.17370 0.40170 0.40410 -0.42980 6.674e-01
## TIME1 -0.07361 0.25310 0.24340 -0.30240 7.624e-01
## MI1:CBT_ref10 -0.44670 0.59550 0.59450 -0.75140 4.524e-01
## MI1:TIME1 -0.31580 0.36330 0.33280 -0.94890 3.427e-01
## CBT_ref10:TIME1 -0.01296 0.37410 0.35690 -0.03632 9.710e-01
## MI1:CBT_ref10:TIME1 0.90330 0.54820 0.56060 1.61100 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
summary(PPVABS_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.25800 0.27610 0.27090 -4.64600 3.390e-06
## cAGE -0.36400 0.11900 0.09947 -3.65900 2.529e-04
## cBLCGSMD -0.02180 0.01515 0.01825 -1.19500 2.321e-01
## MI1 -0.26950 0.40290 0.39420 -0.68360 4.942e-01
## CBT_ref10 -0.18660 0.40980 0.41380 -0.45090 6.520e-01
## TIME_ref10 0.07361 0.25310 0.24340 0.30240 7.624e-01
## MI1:CBT_ref10 0.45660 0.58880 0.58670 0.77830 4.364e-01
## MI1:TIME_ref10 0.31580 0.36330 0.33280 0.94890 3.427e-01
## CBT_ref10:TIME_ref10 0.01296 0.37410 0.35690 0.03632 9.710e-01
## MI1:CBT_ref10:TIME_ref10 -0.90330 0.54820 0.56060 -1.61100 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
summary(PPVABS_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, TIME=0
## Estimates Model SE Robust SE wald p
## (Intercept) -1.1380 0.26720 0.25640 -4.4400 9.010e-06
## cAGE -0.3640 0.11900 0.09947 -3.6590 2.529e-04
## cBLCGSMD -0.0218 0.01515 0.01825 -1.1950 2.321e-01
## MI_ref10 -0.0463 0.37910 0.37260 -0.1243 9.011e-01
## CBT_ref10 -0.6203 0.43820 0.43840 -1.4150 1.570e-01
## TIME1 -0.3894 0.26070 0.22680 -1.7170 8.595e-02
## MI_ref10:CBT_ref10 0.4467 0.59550 0.59450 0.7514 4.524e-01
## MI_ref10:TIME1 0.3158 0.36330 0.33280 0.9489 3.427e-01
## CBT_ref10:TIME1 0.8903 0.40070 0.43190 2.0610 3.926e-02
## MI_ref10:CBT_ref10:TIME1 -0.9033 0.54820 0.56060 -1.6110 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
summary(PPVABS_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, TIME=1
## Estimates Model SE Robust SE wald p
## (Intercept) -1.5280 0.29590 0.28530 -5.3560 9.000e-08
## cAGE -0.3640 0.11900 0.09947 -3.6590 2.529e-04
## cBLCGSMD -0.0218 0.01515 0.01825 -1.1950 2.321e-01
## MI_ref10 0.2695 0.40290 0.39420 0.6836 4.942e-01
## CBT_ref10 0.2700 0.42040 0.41810 0.6457 5.185e-01
## TIME_ref10 0.3894 0.26070 0.22680 1.7170 8.595e-02
## MI_ref10:CBT_ref10 -0.4566 0.58880 0.58670 -0.7783 4.364e-01
## MI_ref10:TIME_ref10 -0.3158 0.36330 0.33280 -0.9489 3.427e-01
## CBT_ref10:TIME_ref10 -0.8903 0.40070 0.43190 -2.0610 3.926e-02
## MI_ref10:CBT_ref10:TIME_ref10 0.9033 0.54820 0.56060 1.6110 1.071e-01
##
## Estimated Correlation Parameter: 0.5693
## Correlation Structure: ar1
## Est. Scale Parameter: 1.012
##
## Number of GEE iterations: 4
## Number of Clusters: 295 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 590
# 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")
## 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")
## 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")
## 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) -1.97800 0.19070 0.20860 -9.4820 0.000000
## cAGE -0.14990 0.09251 0.10720 -1.3980 0.162000
## cBLCGSMD -0.01688 0.01152 0.02085 -0.8097 0.418100
## MI1 -0.60010 0.20470 0.21560 -2.7830 0.005387
## CBT1 0.59740 0.20630 0.22100 2.7030 0.006866
## TIME1 -0.12070 0.12000 0.11970 -1.0080 0.313400
##
## Estimated Correlation Parameter: 0.4972
## Correlation Structure: ar1
## Est. Scale Parameter: 2.986
##
## Number of GEE iterations: 20
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
## 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) 1.658000 0.30040 0.2523 6.57100 0.00000
## cAGE -0.142800 0.11730 0.1151 -1.24000 0.21490
## cBLCGSMD 0.001266 0.01465 0.0151 0.08382 0.93320
## MI1 -0.555400 0.43860 0.3758 -1.47800 0.13940
## CBT1 0.551400 0.40170 0.3158 1.74600 0.08081
## TIME1 0.222800 0.27930 0.2609 0.85390 0.39310
## MI1:CBT1 0.148400 0.57940 0.5181 0.28640 0.77460
## MI1:TIME1 -0.653700 0.42030 0.4683 -1.39600 0.16270
## CBT1:TIME1 0.036570 0.37680 0.3096 0.11810 0.90600
## MI1:CBT1:TIME1 0.193400 0.55550 0.5800 0.33350 0.73880
##
## Estimated Correlation Parameter: 0.5538
## Correlation Structure: ar1
## Est. Scale Parameter: 4.871
##
## Number of GEE iterations: 5
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
summary(LONGABS_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.881000 0.29560 0.3079 6.10700 0.00000
## cAGE -0.142800 0.11730 0.1151 -1.24000 0.21490
## cBLCGSMD 0.001266 0.01465 0.0151 0.08382 0.93320
## MI1 -1.209000 0.45460 0.5246 -2.30500 0.02118
## CBT1 0.588000 0.39710 0.3539 1.66100 0.09662
## TIME_ref10 -0.222800 0.27930 0.2609 -0.85390 0.39310
## MI1:CBT1 0.341800 0.59390 0.6597 0.51810 0.60440
## MI1:TIME_ref10 0.653700 0.42030 0.4683 1.39600 0.16270
## CBT1:TIME_ref10 -0.036570 0.37680 0.3096 -0.11810 0.90600
## MI1:CBT1:TIME_ref10 -0.193400 0.55550 0.5800 -0.33350 0.73880
##
## Estimated Correlation Parameter: 0.5538
## Correlation Structure: ar1
## Est. Scale Parameter: 4.871
##
## Number of GEE iterations: 5
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
summary(LONGABS_nobl_interaction_MIref1) #ref: MI=1, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.103000 0.31910 0.2746 4.01500 5.946e-05
## cAGE -0.142800 0.11730 0.1151 -1.24000 2.149e-01
## cBLCGSMD 0.001266 0.01465 0.0151 0.08382 9.332e-01
## MI_ref10 0.555400 0.43860 0.3758 1.47800 1.394e-01
## CBT1 0.699800 0.41640 0.4074 1.71800 8.583e-02
## TIME1 -0.430900 0.31410 0.3887 -1.10800 2.677e-01
## MI_ref10:CBT1 -0.148400 0.57940 0.5181 -0.28640 7.746e-01
## MI_ref10:TIME1 0.653700 0.42030 0.4683 1.39600 1.627e-01
## CBT1:TIME1 0.230000 0.40810 0.4908 0.46860 6.393e-01
## MI_ref10:CBT1:TIME1 -0.193400 0.55550 0.5800 -0.33350 7.388e-01
##
## Estimated Correlation Parameter: 0.5538
## Correlation Structure: ar1
## Est. Scale Parameter: 4.871
##
## Number of GEE iterations: 5
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
summary(LONGABS_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 0.671700 0.34520 0.4084 1.64500 0.10010
## cAGE -0.142800 0.11730 0.1151 -1.24000 0.21490
## cBLCGSMD 0.001266 0.01465 0.0151 0.08382 0.93320
## MI_ref10 1.209000 0.45460 0.5246 2.30500 0.02118
## CBT1 0.929800 0.44070 0.5465 1.70100 0.08885
## TIME_ref10 0.430900 0.31410 0.3887 1.10800 0.26770
## MI_ref10:CBT1 -0.341800 0.59390 0.6597 -0.51810 0.60440
## MI_ref10:TIME_ref10 -0.653700 0.42030 0.4683 -1.39600 0.16270
## CBT1:TIME_ref10 -0.230000 0.40810 0.4908 -0.46860 0.63930
## MI_ref10:CBT1:TIME_ref10 0.193400 0.55550 0.5800 0.33350 0.73880
##
## Estimated Correlation Parameter: 0.5538
## Correlation Structure: ar1
## Est. Scale Parameter: 4.871
##
## Number of GEE iterations: 5
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
summary(LONGABS_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.2080000 0.26660 0.19320 11.43000 0.00000
## cAGE -0.1429000 0.11660 0.11540 -1.23900 0.21540
## cBLCGSMD 0.0008395 0.01458 0.01514 0.05545 0.95580
## MI1 -0.3857000 0.37940 0.34980 -1.10200 0.27030
## CBT_ref10 -0.5545000 0.40110 0.31780 -1.74500 0.08101
## TIME1 0.2601000 0.25240 0.16560 1.57000 0.11630
## MI1:CBT_ref10 -0.1882000 0.57840 0.51970 -0.36210 0.71730
## MI1:TIME1 -0.4446000 0.36180 0.33120 -1.34200 0.17950
## CBT_ref10:TIME1 -0.0394600 0.37680 0.30960 -0.12750 0.89860
## MI1:CBT_ref10:TIME1 -0.2289000 0.55630 0.58540 -0.39100 0.69580
##
## Estimated Correlation Parameter: 0.5489
## Correlation Structure: ar1
## Est. Scale Parameter: 4.827
##
## Number of GEE iterations: 4
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
summary(LONGABS_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.4690000 0.26510 0.17800 13.87000 0.00000
## cAGE -0.1429000 0.11660 0.11540 -1.23900 0.21540
## cBLCGSMD 0.0008395 0.01458 0.01514 0.05545 0.95580
## MI1 -0.8302000 0.38320 0.38710 -2.14500 0.03197
## CBT_ref10 -0.5940000 0.39640 0.35610 -1.66800 0.09529
## TIME_ref10 -0.2601000 0.25240 0.16560 -1.57000 0.11630
## MI1:CBT_ref10 -0.4171000 0.59360 0.66640 -0.62580 0.53140
## MI1:TIME_ref10 0.4446000 0.36180 0.33120 1.34200 0.17950
## CBT_ref10:TIME_ref10 0.0394600 0.37680 0.30960 0.12750 0.89860
## MI1:CBT_ref10:TIME_ref10 0.2289000 0.55630 0.58540 0.39100 0.69580
##
## Estimated Correlation Parameter: 0.5489
## Correlation Structure: ar1
## Est. Scale Parameter: 4.827
##
## Number of GEE iterations: 4
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
summary(LONGABS_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.8230000 0.26990 0.29220 6.23700 0.00000
## cAGE -0.1429000 0.11660 0.11540 -1.23900 0.21540
## cBLCGSMD 0.0008395 0.01458 0.01514 0.05545 0.95580
## MI_ref10 0.3857000 0.37940 0.34980 1.10200 0.27030
## CBT_ref10 -0.7427000 0.41560 0.40820 -1.82000 0.06882
## TIME1 -0.1845000 0.25920 0.28710 -0.64260 0.52050
## MI_ref10:CBT_ref10 0.1882000 0.57840 0.51970 0.36210 0.71730
## MI_ref10:TIME1 0.4446000 0.36180 0.33120 1.34200 0.17950
## CBT_ref10:TIME1 -0.2684000 0.40930 0.49750 -0.53940 0.58960
## MI_ref10:CBT_ref10:TIME1 0.2289000 0.55630 0.58540 0.39100 0.69580
##
## Estimated Correlation Parameter: 0.5489
## Correlation Structure: ar1
## Est. Scale Parameter: 4.827
##
## Number of GEE iterations: 4
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
summary(LONGABS_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.6380000 0.27640 0.34600 4.73500 2.190e-06
## cAGE -0.1429000 0.11660 0.11540 -1.23900 2.154e-01
## cBLCGSMD 0.0008395 0.01458 0.01514 0.05545 9.558e-01
## MI_ref10 0.8302000 0.38320 0.38710 2.14500 3.197e-02
## CBT_ref10 -1.0110000 0.44070 0.55330 -1.82700 6.765e-02
## TIME_ref10 0.1845000 0.25920 0.28710 0.64260 5.205e-01
## MI_ref10:CBT_ref10 0.4171000 0.59360 0.66640 0.62580 5.314e-01
## MI_ref10:TIME_ref10 -0.4446000 0.36180 0.33120 -1.34200 1.795e-01
## CBT_ref10:TIME_ref10 0.2684000 0.40930 0.49750 0.53940 5.896e-01
## MI_ref10:CBT_ref10:TIME_ref10 -0.2289000 0.55630 0.58540 -0.39100 6.958e-01
##
## Estimated Correlation Parameter: 0.5489
## Correlation Structure: ar1
## Est. Scale Parameter: 4.827
##
## Number of GEE iterations: 4
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
# Save ---------------------------
save.image(file="gee-main-interaction-models.RData")