rm(list=ls())
# Load libraries ---------------------------
library(haven)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(geeM)
## Loading required package: Matrix
library(magrittr)
# Data location for reading datasets and storing data ---------------------------
data_loc <- "/Volumes/caas/MERITS/RESOURCES/Data Processing/Data Analysis/Outcome Analyses 2016/Manuscript Revisions_2020/Aditya Khanna Analyses 2022/RESOURCES-AK-R"
# Read Data ---------------------------
raw_dt_ncigs_smkd <- read.csv("/Volumes/caas/MERITS/RESOURCES/Data Processing/Data Analysis/Outcome Analyses 2016/Manuscript Revisions_2020/Aditya Khanna Analyses 2022/NCIGSSMDK.csv")
# Specify corstr = ar1 ---------------------------
corstr = "ar1"
# Create covariate data objects ---------------------------
covariates_dt <-
cbind.data.frame(
cAGE = raw_dt_ncigs_smkd$AGE-mean(raw_dt_ncigs_smkd$AGE),
MI = as.factor(raw_dt_ncigs_smkd$MI),
CBT = as.factor(raw_dt_ncigs_smkd$CBT),
TIME=as.factor(raw_dt_ncigs_smkd$TIME),
#CBT=as.factor(raw_dt_ncigs_smkd$CBT),
M3ENVDAYS = raw_dt_ncigs_smkd$M3ENVDAYS,
M6ENVDAYS = raw_dt_ncigs_smkd$M6ENVDAYS ,
MIBYCBT = as.factor(raw_dt_ncigs_smkd$MIBYCBT),
MIBYTIME = as.factor(raw_dt_ncigs_smkd$MIBYTIME),
CBTBYTIME = as.factor(raw_dt_ncigs_smkd$CBTBYTIME),
MIBYCBTBYTIME = as.factor(raw_dt_ncigs_smkd$MIBYCBTBYTIME),
id = raw_dt_ncigs_smkd$id,
log_unctrldays = raw_dt_ncigs_smkd$log_unctrldays
)
# Create dataframe ---------------------------
ncigs_dt <- as.data.frame(
cbind.data.frame(NCIGS=raw_dt_ncigs_smkd$NCIGS,
covariates_dt)
)
class(ncigs_dt)
## [1] "data.frame"
dim(ncigs_dt)
## [1] 729 13
summary(ncigs_dt)
## NCIGS cAGE MI CBT TIME M3ENVDAYS
## Min. : 0.0 Min. :-2.8828 0:366 0:327 0:243 Min. : 0.00
## 1st Qu.: 85.0 1st Qu.:-0.8253 1:363 1:402 1:243 1st Qu.: 0.00
## Median : 213.0 Median : 0.1144 2:243 Median : 1.00
## Mean : 329.4 Mean : 0.0000 Mean :17.71
## 3rd Qu.: 460.0 3rd Qu.: 0.8103 3rd Qu.:35.00
## Max. :2400.0 Max. : 2.3363 Max. :58.00
## M6ENVDAYS MIBYCBT MIBYTIME CBTBYTIME MIBYCBTBYTIME id
## Min. : 0.00 0:525 0:487 0:461 0:593 Min. : 1.0
## 1st Qu.: 0.00 1:204 1:121 1:134 1: 68 1st Qu.: 86.0
## Median : 0.00 2:121 2:134 2: 68 Median :163.0
## Mean :15.23 Mean :162.8
## 3rd Qu.:34.00 3rd Qu.:240.0
## Max. :58.00 Max. :316.0
## log_unctrldays
## Min. :1.099
## 1st Qu.:3.332
## Median :3.434
## Mean :3.560
## 3rd Qu.:4.111
## Max. :4.111
colnames(ncigs_dt)
## [1] "NCIGS" "cAGE" "MI" "CBT"
## [5] "TIME" "M3ENVDAYS" "M6ENVDAYS" "MIBYCBT"
## [9] "MIBYTIME" "CBTBYTIME" "MIBYCBTBYTIME" "id"
## [13] "log_unctrldays"
## restrict to complete cases
ncigs_dt_na.omit <- na.omit(ncigs_dt)
dim(ncigs_dt_na.omit)
## [1] 729 13
summary(ncigs_dt_na.omit)
## NCIGS cAGE MI CBT TIME M3ENVDAYS
## Min. : 0.0 Min. :-2.8828 0:366 0:327 0:243 Min. : 0.00
## 1st Qu.: 85.0 1st Qu.:-0.8253 1:363 1:402 1:243 1st Qu.: 0.00
## Median : 213.0 Median : 0.1144 2:243 Median : 1.00
## Mean : 329.4 Mean : 0.0000 Mean :17.71
## 3rd Qu.: 460.0 3rd Qu.: 0.8103 3rd Qu.:35.00
## Max. :2400.0 Max. : 2.3363 Max. :58.00
## M6ENVDAYS MIBYCBT MIBYTIME CBTBYTIME MIBYCBTBYTIME id
## Min. : 0.00 0:525 0:487 0:461 0:593 Min. : 1.0
## 1st Qu.: 0.00 1:204 1:121 1:134 1: 68 1st Qu.: 86.0
## Median : 0.00 2:121 2:134 2: 68 Median :163.0
## Mean :15.23 Mean :162.8
## 3rd Qu.:34.00 3rd Qu.:240.0
## Max. :58.00 Max. :316.0
## log_unctrldays
## Min. :1.099
## 1st Qu.:3.332
## Median :3.434
## Mean :3.560
## 3rd Qu.:4.111
## Max. :4.111
colnames(ncigs_dt_na.omit)
## [1] "NCIGS" "cAGE" "MI" "CBT"
## [5] "TIME" "M3ENVDAYS" "M6ENVDAYS" "MIBYCBT"
## [9] "MIBYTIME" "CBTBYTIME" "MIBYCBTBYTIME" "id"
## [13] "log_unctrldays"
# DV: NCIGS ---------------------------
#relevel MI and CBT
ncigs_dt_na.omit$MI_ref1 <- ncigs_dt_na.omit$MI %>% relevel("1")
ncigs_dt_na.omit$CBT_ref1 <- ncigs_dt_na.omit$CBT %>% relevel("1")
ncigs_dt_na.omit$TIME_ref1 <- ncigs_dt_na.omit$TIME %>% relevel("1")
ncigs_dt_na.omit$TIME_ref2 <- ncigs_dt_na.omit$TIME %>% relevel("2")
# ignore missing values
## create an indicator column for any missing values
ncigs_dt_na.omit$any_na <- ncigs_dt_na.omit %>% apply(1, function(x){any(is.na(x))})
# ## left join the dataset by the id column using the group_by() function
ncigs_dt_na.omit %<>% left_join(ncigs_dt_na.omit %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
# ## filter out rows with any NAs
ncigs_dt_na.omit %<>% filter(any_na2 != T)
# modeling
## fit main model
ncigs_main <- geem(formula =
NCIGS~
cAGE +
MI +
CBT +
TIME +
offset( log_unctrldays),
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) 2.369000 0.10060 0.08185 28.94000 0.0000
## cAGE 0.104500 0.04729 0.04654 2.24500 0.0248
## MI1 0.082880 0.10640 0.09894 0.83770 0.4022
## CBT1 0.003578 0.10680 0.10200 0.03509 0.9720
## TIME1 -0.540100 0.05169 0.06700 -8.06100 0.0000
## TIME2 -0.448700 0.06663 0.06756 -6.64200 0.0000
##
## Estimated Correlation Parameter: 0.6627
## Correlation Structure: ar1
## Est. Scale Parameter: 0.958
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
## fit 3-way interaction model
ncigs_interaction <- geem(formula =
NCIGS~
cAGE +
MI +
CBT +
TIME +
MI*CBT+
MI*TIME+
CBT*TIME+
MI*CBT*TIME+
offset( log_unctrldays),
#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) 2.29100 0.13100 0.10190 22.48000 0.000000
## cAGE 0.10340 0.04746 0.04657 2.22000 0.026430
## MI1 0.19110 0.18790 0.14440 1.32300 0.185700
## CBT1 0.19190 0.17800 0.15030 1.27700 0.201500
## TIME1 -0.49560 0.10610 0.15700 -3.15800 0.001591
## TIME2 -0.40790 0.13710 0.14550 -2.80400 0.005054
## MI1:CBT1 -0.29790 0.25270 0.20160 -1.47800 0.139400
## MI1:TIME1 -0.06360 0.15210 0.20490 -0.31030 0.756300
## MI1:TIME2 0.01873 0.19650 0.19490 0.09607 0.923500
## CBT1:TIME1 -0.16070 0.14420 0.20590 -0.78070 0.435000
## CBT1:TIME2 -0.16360 0.18630 0.20460 -0.79970 0.423900
## MI1:CBT1:TIME1 0.28210 0.20480 0.26930 1.04700 0.295000
## MI1:CBT1:TIME2 0.15070 0.26460 0.26780 0.56290 0.573500
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_TIMEref1) #ref: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.796000 0.13120 0.15090 11.90000 0.000000
## cAGE 0.103400 0.04746 0.04657 2.22000 0.026430
## MI1 0.127500 0.18820 0.22660 0.56270 0.573600
## CBT1 0.031210 0.17830 0.20120 0.15510 0.876700
## TIME_ref10 0.495600 0.10610 0.15700 3.15800 0.001591
## TIME_ref12 0.087670 0.10610 0.06703 1.30800 0.190900
## MI1:CBT1 -0.015880 0.25300 0.28630 -0.05547 0.955800
## MI1:TIME_ref10 0.063600 0.15210 0.20490 0.31030 0.756300
## MI1:TIME_ref12 0.082330 0.15220 0.12680 0.64950 0.516000
## CBT1:TIME_ref10 0.160700 0.14420 0.20590 0.78070 0.435000
## CBT1:TIME_ref12 -0.002899 0.14430 0.09798 -0.02959 0.976400
## MI1:CBT1:TIME_ref10 -0.282100 0.20480 0.26930 -1.04700 0.295000
## MI1:CBT1:TIME_ref12 -0.131300 0.20490 0.16750 -0.78410 0.433000
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_TIMEref2) #ref: MI=0, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 1.883000 0.13110 0.13190 14.28000 0.000000
## cAGE 0.103400 0.04746 0.04657 2.22000 0.026430
## MI1 0.209800 0.18800 0.20490 1.02400 0.305700
## CBT1 0.028310 0.17810 0.18950 0.14940 0.881200
## TIME_ref20 0.407900 0.13710 0.14550 2.80400 0.005054
## TIME_ref21 -0.087670 0.10610 0.06703 -1.30800 0.190900
## MI1:CBT1 -0.147200 0.25280 0.26910 -0.54710 0.584300
## MI1:TIME_ref20 -0.018730 0.19650 0.19490 -0.09607 0.923500
## MI1:TIME_ref21 -0.082330 0.15220 0.12680 -0.64950 0.516000
## CBT1:TIME_ref20 0.163600 0.18630 0.20460 0.79970 0.423900
## CBT1:TIME_ref21 0.002899 0.14430 0.09798 0.02959 0.976400
## MI1:CBT1:TIME_ref20 -0.150700 0.26460 0.26780 -0.56290 0.573500
## MI1:CBT1:TIME_ref21 0.131300 0.20490 0.16750 0.78410 0.433000
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.48200 0.13430 0.10250 24.21000 0.000e+00
## cAGE 0.10340 0.04746 0.04657 2.22000 2.643e-02
## MI_ref10 -0.19110 0.18790 0.14440 -1.32300 1.857e-01
## CBT1 -0.10600 0.17910 0.13400 -0.79110 4.289e-01
## TIME1 -0.55920 0.10900 0.13180 -4.24400 2.193e-05
## TIME2 -0.38920 0.14080 0.12970 -3.00000 2.699e-03
## MI_ref10:CBT1 0.29790 0.25270 0.20160 1.47800 1.394e-01
## MI_ref10:TIME1 0.06360 0.15210 0.20490 0.31030 7.563e-01
## MI_ref10:TIME2 -0.01873 0.19650 0.19490 -0.09607 9.235e-01
## CBT1:TIME1 0.12130 0.14540 0.17370 0.69860 4.848e-01
## CBT1:TIME2 -0.01291 0.18790 0.17270 -0.07474 9.404e-01
## MI_ref10:CBT1:TIME1 -0.28210 0.20480 0.26930 -1.04700 2.950e-01
## MI_ref10:CBT1:TIME2 -0.15070 0.26460 0.26780 -0.56290 5.735e-01
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.92300 0.13450 0.16940 11.36000 0.000e+00
## cAGE 0.10340 0.04746 0.04657 2.22000 2.643e-02
## MI_ref10 -0.12750 0.18820 0.22660 -0.56270 5.736e-01
## CBT1 0.01533 0.17930 0.20380 0.07525 9.400e-01
## TIME_ref10 0.55920 0.10900 0.13180 4.24400 2.193e-05
## TIME_ref12 0.17000 0.10900 0.10760 1.58000 1.140e-01
## MI_ref10:CBT1 0.01588 0.25300 0.28630 0.05547 9.558e-01
## MI_ref10:TIME_ref10 -0.06360 0.15210 0.20490 -0.31030 7.563e-01
## MI_ref10:TIME_ref12 -0.08233 0.15220 0.12680 -0.64950 5.160e-01
## CBT1:TIME_ref10 -0.12130 0.14540 0.17370 -0.69860 4.848e-01
## CBT1:TIME_ref12 -0.13420 0.14550 0.13590 -0.98810 3.231e-01
## MI_ref10:CBT1:TIME_ref10 0.28210 0.20480 0.26930 1.04700 2.950e-01
## MI_ref10:CBT1:TIME_ref12 0.13130 0.20490 0.16750 0.78410 4.330e-01
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_MIref1_TIMEref2) #ref: MI=1, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 2.09300 0.13440 0.15600 13.42000 0.000000
## cAGE 0.10340 0.04746 0.04657 2.22000 0.026430
## MI_ref10 -0.20980 0.18800 0.20490 -1.02400 0.305700
## CBT1 -0.11890 0.17920 0.18980 -0.62660 0.530900
## TIME_ref20 0.38920 0.14080 0.12970 3.00000 0.002699
## TIME_ref21 -0.17000 0.10900 0.10760 -1.58000 0.114000
## MI_ref10:CBT1 0.14720 0.25280 0.26910 0.54710 0.584300
## MI_ref10:TIME_ref20 0.01873 0.19650 0.19490 0.09607 0.923500
## MI_ref10:TIME_ref21 0.08233 0.15220 0.12680 0.64950 0.516000
## CBT1:TIME_ref20 0.01291 0.18790 0.17270 0.07474 0.940400
## CBT1:TIME_ref21 0.13420 0.14550 0.13590 0.98810 0.323100
## MI_ref10:CBT1:TIME_ref20 0.15070 0.26460 0.26780 0.56290 0.573500
## MI_ref10:CBT1:TIME_ref21 -0.13130 0.20490 0.16750 -0.78410 0.433000
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.4830 0.12030 0.10810 22.9800 0.000e+00
## cAGE 0.1034 0.04746 0.04657 2.2200 2.643e-02
## MI1 -0.1068 0.16880 0.13770 -0.7758 4.379e-01
## CBT_ref10 -0.1919 0.17800 0.15030 -1.2770 2.015e-01
## TIME1 -0.6563 0.09768 0.13320 -4.9260 8.400e-07
## TIME2 -0.5716 0.12620 0.14380 -3.9740 7.080e-05
## MI1:CBT_ref10 0.2979 0.25270 0.20160 1.4780 1.394e-01
## MI1:TIME1 0.2185 0.13710 0.17480 1.2500 2.114e-01
## MI1:TIME2 0.1694 0.17720 0.18350 0.9232 3.559e-01
## CBT_ref10:TIME1 0.1607 0.14420 0.20590 0.7807 4.350e-01
## CBT_ref10:TIME2 0.1636 0.18630 0.20460 0.7997 4.239e-01
## MI1:CBT_ref10:TIME1 -0.2821 0.20480 0.26930 -1.0470 2.950e-01
## MI1:CBT_ref10:TIME2 -0.1507 0.26460 0.26780 -0.5629 5.735e-01
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.827000 0.12050 0.13170 13.87000 0.000e+00
## cAGE 0.103400 0.04746 0.04657 2.22000 2.643e-02
## MI1 0.111600 0.16900 0.17340 0.64380 5.197e-01
## CBT_ref10 -0.031210 0.17830 0.20120 -0.15510 8.767e-01
## TIME_ref10 0.656300 0.09768 0.13320 4.92600 8.400e-07
## TIME_ref12 0.084770 0.09772 0.07145 1.18600 2.355e-01
## MI1:CBT_ref10 0.015880 0.25300 0.28630 0.05547 9.558e-01
## MI1:TIME_ref10 -0.218500 0.13710 0.17480 -1.25000 2.114e-01
## MI1:TIME_ref12 -0.049010 0.13720 0.10950 -0.44760 6.544e-01
## CBT_ref10:TIME_ref10 -0.160700 0.14420 0.20590 -0.78070 4.350e-01
## CBT_ref10:TIME_ref12 0.002899 0.14430 0.09798 0.02959 9.764e-01
## MI1:CBT_ref10:TIME_ref10 0.282100 0.20480 0.26930 1.04700 2.950e-01
## MI1:CBT_ref10:TIME_ref12 0.131300 0.20490 0.16750 0.78410 4.330e-01
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_CBTref1_TIMEref2) #ref: MI=0, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 1.912000 0.12040 0.13430 14.23000 0.0000000
## cAGE 0.103400 0.04746 0.04657 2.22000 0.0264300
## MI1 0.062610 0.16900 0.17210 0.36380 0.7160000
## CBT_ref10 -0.028310 0.17810 0.18950 -0.14940 0.8812000
## TIME_ref20 0.571600 0.12620 0.14380 3.97400 0.0000708
## TIME_ref21 -0.084770 0.09772 0.07145 -1.18600 0.2355000
## MI1:CBT_ref10 0.147200 0.25280 0.26910 0.54710 0.5843000
## MI1:TIME_ref20 -0.169400 0.17720 0.18350 -0.92320 0.3559000
## MI1:TIME_ref21 0.049010 0.13720 0.10950 0.44760 0.6544000
## CBT_ref10:TIME_ref20 -0.163600 0.18630 0.20460 -0.79970 0.4239000
## CBT_ref10:TIME_ref21 -0.002899 0.14430 0.09798 -0.02959 0.9764000
## MI1:CBT_ref10:TIME_ref20 0.150700 0.26460 0.26780 0.56290 0.5735000
## MI1:CBT_ref10:TIME_ref21 -0.131300 0.20490 0.16750 -0.78410 0.4330000
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.37600 0.11850 0.08570 27.73000 0.0000000
## cAGE 0.10340 0.04746 0.04657 2.22000 0.0264300
## MI_ref10 0.10680 0.16880 0.13770 0.77580 0.4379000
## CBT_ref10 0.10600 0.17910 0.13400 0.79110 0.4289000
## TIME1 -0.43790 0.09622 0.11310 -3.87000 0.0001089
## TIME2 -0.40210 0.12440 0.11400 -3.52700 0.0004204
## MI_ref10:CBT_ref10 -0.29790 0.25270 0.20160 -1.47800 0.1394000
## MI_ref10:TIME1 -0.21850 0.13710 0.17480 -1.25000 0.2114000
## MI_ref10:TIME2 -0.16940 0.17720 0.18350 -0.92320 0.3559000
## CBT_ref10:TIME1 -0.12130 0.14540 0.17370 -0.69860 0.4848000
## CBT_ref10:TIME2 0.01291 0.18790 0.17270 0.07474 0.9404000
## MI_ref10:CBT_ref10:TIME1 0.28210 0.20480 0.26930 1.04700 0.2950000
## MI_ref10:CBT_ref10:TIME2 0.15070 0.26460 0.26780 0.56290 0.5735000
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.93900 0.11860 0.11300 17.16000 0.0000000
## cAGE 0.10340 0.04746 0.04657 2.22000 0.0264300
## MI_ref10 -0.11160 0.16900 0.17340 -0.64380 0.5197000
## CBT_ref10 -0.01533 0.17930 0.20380 -0.07525 0.9400000
## TIME_ref10 0.43790 0.09622 0.11310 3.87000 0.0001089
## TIME_ref12 0.03575 0.09626 0.08297 0.43090 0.6665000
## MI_ref10:CBT_ref10 -0.01588 0.25300 0.28630 -0.05547 0.9558000
## MI_ref10:TIME_ref10 0.21850 0.13710 0.17480 1.25000 0.2114000
## MI_ref10:TIME_ref12 0.04901 0.13720 0.10950 0.44760 0.6544000
## CBT_ref10:TIME_ref10 0.12130 0.14540 0.17370 0.69860 0.4848000
## CBT_ref10:TIME_ref12 0.13420 0.14550 0.13590 0.98810 0.3231000
## MI_ref10:CBT_ref10:TIME_ref10 -0.28210 0.20480 0.26930 -1.04700 0.2950000
## MI_ref10:CBT_ref10:TIME_ref12 -0.13130 0.20490 0.16750 -0.78410 0.4330000
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(ncigs_interaction_MIref1_CBTref1_TIMEref2) #ref: MI=1, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) 1.97400 0.11860 0.10790 18.31000 0.0000000
## cAGE 0.10340 0.04746 0.04657 2.22000 0.0264300
## MI_ref10 -0.06261 0.16900 0.17210 -0.36380 0.7160000
## CBT_ref10 0.11890 0.17920 0.18980 0.62660 0.5309000
## TIME_ref20 0.40210 0.12440 0.11400 3.52700 0.0004204
## TIME_ref21 -0.03575 0.09626 0.08297 -0.43090 0.6665000
## MI_ref10:CBT_ref10 -0.14720 0.25280 0.26910 -0.54710 0.5843000
## MI_ref10:TIME_ref20 0.16940 0.17720 0.18350 0.92320 0.3559000
## MI_ref10:TIME_ref21 -0.04901 0.13720 0.10950 -0.44760 0.6544000
## CBT_ref10:TIME_ref20 -0.01291 0.18790 0.17270 -0.07474 0.9404000
## CBT_ref10:TIME_ref21 -0.13420 0.14550 0.13590 -0.98810 0.3231000
## MI_ref10:CBT_ref10:TIME_ref20 -0.15070 0.26460 0.26780 -0.56290 0.5735000
## MI_ref10:CBT_ref10:TIME_ref21 0.13130 0.20490 0.16750 0.78410 0.4330000
##
## Estimated Correlation Parameter: 0.6707
## Correlation Structure: ar1
## Est. Scale Parameter: 0.9519
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729