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
)
# DV V: SMKD ---------------------------
## create dataframe
SMDK_dt <- as.data.frame(
cbind.data.frame(SMDK=raw_dt_ncigs_smkd$SMDK,
covariates_dt)
)
class(SMDK_dt)
## [1] "data.frame"
dim(SMDK_dt)
## [1] 729 13
summary(SMDK_dt)
## SMDK cAGE MI CBT TIME M3ENVDAYS
## Min. : 0.00 Min. :-2.8828 0:366 0:327 0:243 Min. : 0.00
## 1st Qu.:23.00 1st Qu.:-0.8253 1:363 1:402 1:243 1st Qu.: 0.00
## Median :30.00 Median : 0.1144 2:243 Median : 1.00
## Mean :32.21 Mean : 0.0000 Mean :17.71
## 3rd Qu.:50.00 3rd Qu.: 0.8103 3rd Qu.:35.00
## Max. :60.00 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(SMDK_dt)
## [1] "SMDK" "cAGE" "MI" "CBT"
## [5] "TIME" "M3ENVDAYS" "M6ENVDAYS" "MIBYCBT"
## [9] "MIBYTIME" "CBTBYTIME" "MIBYCBTBYTIME" "id"
## [13] "log_unctrldays"
## restrict to complete cases
SMDK_dt_na.omit <- na.omit(SMDK_dt)
dim(SMDK_dt_na.omit)
## [1] 729 13
summary(SMDK_dt_na.omit)
## SMDK cAGE MI CBT TIME M3ENVDAYS
## Min. : 0.00 Min. :-2.8828 0:366 0:327 0:243 Min. : 0.00
## 1st Qu.:23.00 1st Qu.:-0.8253 1:363 1:402 1:243 1st Qu.: 0.00
## Median :30.00 Median : 0.1144 2:243 Median : 1.00
## Mean :32.21 Mean : 0.0000 Mean :17.71
## 3rd Qu.:50.00 3rd Qu.: 0.8103 3rd Qu.:35.00
## Max. :60.00 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(SMDK_dt_na.omit)
## [1] "SMDK" "cAGE" "MI" "CBT"
## [5] "TIME" "M3ENVDAYS" "M6ENVDAYS" "MIBYCBT"
## [9] "MIBYTIME" "CBTBYTIME" "MIBYCBTBYTIME" "id"
## [13] "log_unctrldays"
# DV: SMDK ---------------------------
#relevel MI and CBT
SMDK_dt_na.omit$MI_ref1 <- SMDK_dt_na.omit$MI %>% relevel("1")
SMDK_dt_na.omit$CBT_ref1 <- SMDK_dt_na.omit$CBT %>% relevel("1")
SMDK_dt_na.omit$TIME_ref1 <- SMDK_dt_na.omit$TIME %>% relevel("1")
SMDK_dt_na.omit$TIME_ref2 <- SMDK_dt_na.omit$TIME %>% relevel("2")
# ignore missing values
## create an indicator column for any missing values
SMDK_dt_na.omit$any_na <- SMDK_dt_na.omit %>% apply(1, function(x){any(is.na(x))})
## left join the dataset by the id column using the group_by() function
SMDK_dt_na.omit %<>% left_join(SMDK_dt_na.omit %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
## filter out rows with any NAs
SMDK_dt_na.omit %<>% filter(any_na2 != T)
# modeling
## fit main model
SMDK_main <- geem(formula =
SMDK~
cAGE +
MI +
CBT +
TIME +
offset( log_unctrldays),
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) -0.09342 0.03467 0.02591 -3.606 0.000311
## cAGE 0.02584 0.01571 0.01412 1.830 0.067220
## MI1 0.07370 0.03531 0.03118 2.363 0.018110
## CBT1 -0.03342 0.03546 0.03130 -1.068 0.285700
## TIME1 -0.20850 0.02321 0.02811 -7.417 0.000000
## TIME2 -0.17740 0.02825 0.02706 -6.559 0.000000
##
## Estimated Correlation Parameter: 0.4875
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1225
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
## fit interaction model
SMDK_interaction <- geem(formula =
SMDK~
cAGE +
MI +
CBT +
TIME +
MI*CBT+
MI*TIME+
CBT*TIME+
MI*CBT*TIME+
offset( log_unctrldays),
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) -0.09526 0.04812 0.02427 -3.9250 8.684e-05
## cAGE 0.02696 0.01594 0.01444 1.8670 6.191e-02
## MI1 0.03962 0.06903 0.02776 1.4270 1.536e-01
## CBT1 0.02362 0.06539 0.03073 0.7689 4.420e-01
## TIME1 -0.22940 0.04847 0.06525 -3.5150 4.398e-04
## TIME2 -0.14630 0.05901 0.05720 -2.5580 1.054e-02
## MI1:CBT1 -0.04718 0.09281 0.03847 -1.2260 2.201e-01
## MI1:TIME1 0.05159 0.06945 0.08027 0.6427 5.204e-01
## MI1:TIME2 0.02514 0.08462 0.06868 0.3660 7.143e-01
## CBT1:TIME1 -0.01412 0.06581 0.08549 -0.1652 8.688e-01
## CBT1:TIME2 -0.17110 0.08028 0.08717 -1.9620 4.973e-02
## MI1:CBT1:TIME1 0.01216 0.09340 0.11150 0.1090 9.132e-01
## MI1:CBT1:TIME2 0.17840 0.11400 0.10560 1.6890 9.119e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_TIMEref1) #ref: MI=0, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.324600 0.04850 0.06553 -4.9540 7.300e-07
## cAGE 0.026960 0.01594 0.01444 1.8670 6.191e-02
## MI1 0.091210 0.06949 0.08152 1.1190 2.632e-01
## CBT1 0.009504 0.06579 0.08600 0.1105 9.120e-01
## TIME_ref10 0.229400 0.04847 0.06525 3.5150 4.398e-04
## TIME_ref12 0.083070 0.04847 0.05293 1.5690 1.166e-01
## MI1:CBT1 -0.035020 0.09330 0.11180 -0.3133 7.541e-01
## MI1:TIME_ref10 -0.051590 0.06945 0.08027 -0.6427 5.204e-01
## MI1:TIME_ref12 -0.026450 0.06947 0.06539 -0.4045 6.858e-01
## CBT1:TIME_ref10 0.014120 0.06581 0.08549 0.1652 8.688e-01
## CBT1:TIME_ref12 -0.156900 0.06587 0.06913 -2.2700 2.320e-02
## MI1:CBT1:TIME_ref10 -0.012160 0.09340 0.11150 -0.1090 9.132e-01
## MI1:CBT1:TIME_ref12 0.166200 0.09348 0.09164 1.8140 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_TIMEref2) #ref: MI=0, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.24160 0.04813 0.05618 -4.3000 1.711e-05
## cAGE 0.02696 0.01594 0.01444 1.8670 6.191e-02
## MI1 0.06476 0.06906 0.06917 0.9361 3.492e-01
## CBT1 -0.14740 0.06552 0.08690 -1.6960 8.980e-02
## TIME_ref20 0.14630 0.05901 0.05720 2.5580 1.054e-02
## TIME_ref21 -0.08307 0.04847 0.05293 -1.5690 1.166e-01
## MI1:CBT1 0.13120 0.09295 0.10510 1.2490 2.118e-01
## MI1:TIME_ref20 -0.02514 0.08462 0.06868 -0.3660 7.143e-01
## MI1:TIME_ref21 0.02645 0.06947 0.06539 0.4045 6.858e-01
## CBT1:TIME_ref20 0.17110 0.08028 0.08717 1.9620 4.973e-02
## CBT1:TIME_ref21 0.15690 0.06587 0.06913 2.2700 2.320e-02
## MI1:CBT1:TIME_ref20 -0.17840 0.11400 0.10560 -1.6890 9.119e-02
## MI1:CBT1:TIME_ref21 -0.16620 0.09348 0.09164 -1.8140 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_MIref1) #ref: MI=1, CBT=0, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) -0.055640 0.04936 0.01224 -4.54600 5.460e-06
## cAGE 0.026960 0.01594 0.01444 1.86700 6.191e-02
## MI_ref10 -0.039620 0.06903 0.02776 -1.42700 1.536e-01
## CBT1 -0.023550 0.06579 0.02266 -1.03900 2.987e-01
## TIME1 -0.177800 0.04974 0.04673 -3.80400 1.425e-04
## TIME2 -0.121200 0.06065 0.03802 -3.18700 1.439e-03
## MI_ref10:CBT1 0.047180 0.09281 0.03847 1.22600 2.201e-01
## MI_ref10:TIME1 -0.051590 0.06945 0.08027 -0.64270 5.204e-01
## MI_ref10:TIME2 -0.025140 0.08462 0.06868 -0.36600 7.143e-01
## CBT1:TIME1 -0.001961 0.06628 0.07162 -0.02739 9.782e-01
## CBT1:TIME2 0.007311 0.08089 0.05958 0.12270 9.023e-01
## MI_ref10:CBT1:TIME1 -0.012160 0.09340 0.11150 -0.10900 9.132e-01
## MI_ref10:CBT1:TIME2 -0.178400 0.11400 0.10560 -1.68900 9.119e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.233400 0.04965 0.04844 -4.81900 1.440e-06
## cAGE 0.026960 0.01594 0.01444 1.86700 6.191e-02
## MI_ref10 -0.091210 0.06949 0.08152 -1.11900 2.632e-01
## CBT1 -0.025510 0.06610 0.07123 -0.35820 7.202e-01
## TIME_ref10 0.177800 0.04974 0.04673 3.80400 1.425e-04
## TIME_ref12 0.056620 0.04976 0.03837 1.47600 1.401e-01
## MI_ref10:CBT1 0.035020 0.09330 0.11180 0.31330 7.541e-01
## MI_ref10:TIME_ref10 0.051590 0.06945 0.08027 0.64270 5.204e-01
## MI_ref10:TIME_ref12 0.026450 0.06947 0.06539 0.40450 6.858e-01
## CBT1:TIME_ref10 0.001961 0.06628 0.07162 0.02739 9.782e-01
## CBT1:TIME_ref12 0.009272 0.06632 0.06015 0.15420 8.775e-01
## MI_ref10:CBT1:TIME_ref10 0.012160 0.09340 0.11150 0.10900 9.132e-01
## MI_ref10:CBT1:TIME_ref12 -0.166200 0.09348 0.09164 -1.81400 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_MIref1_TIMEref2) #ref: MI=1, CBT=0, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.176800 0.04940 0.04008 -4.4110 1.031e-05
## cAGE 0.026960 0.01594 0.01444 1.8670 6.191e-02
## MI_ref10 -0.064760 0.06906 0.06917 -0.9361 3.492e-01
## CBT1 -0.016240 0.06586 0.05858 -0.2773 7.816e-01
## TIME_ref20 0.121200 0.06065 0.03802 3.1870 1.439e-03
## TIME_ref21 -0.056620 0.04976 0.03837 -1.4760 1.401e-01
## MI_ref10:CBT1 -0.131200 0.09295 0.10510 -1.2490 2.118e-01
## MI_ref10:TIME_ref20 0.025140 0.08462 0.06868 0.3660 7.143e-01
## MI_ref10:TIME_ref21 -0.026450 0.06947 0.06539 -0.4045 6.858e-01
## CBT1:TIME_ref20 -0.007311 0.08089 0.05958 -0.1227 9.023e-01
## CBT1:TIME_ref21 -0.009272 0.06632 0.06015 -0.1542 8.775e-01
## MI_ref10:CBT1:TIME_ref20 0.178400 0.11400 0.10560 1.6890 9.119e-02
## MI_ref10:CBT1:TIME_ref21 0.166200 0.09348 0.09164 1.8140 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_CBTref1) #ref: MI=0, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) -0.07164 0.04421 0.01806 -3.9680 7.260e-05
## cAGE 0.02696 0.01594 0.01444 1.8670 6.191e-02
## MI1 -0.00756 0.06202 0.02627 -0.2878 7.735e-01
## CBT_ref10 -0.02362 0.06539 0.03073 -0.7689 4.420e-01
## TIME1 -0.24350 0.04452 0.05530 -4.4030 1.069e-05
## TIME2 -0.31730 0.05443 0.06581 -4.8220 1.420e-06
## MI1:CBT_ref10 0.04718 0.09281 0.03847 1.2260 2.201e-01
## MI1:TIME1 0.06375 0.06246 0.07747 0.8229 4.106e-01
## MI1:TIME2 0.20350 0.07634 0.08021 2.5370 1.118e-02
## CBT_ref10:TIME1 0.01412 0.06581 0.08549 0.1652 8.688e-01
## CBT_ref10:TIME2 0.17110 0.08028 0.08717 1.9620 4.973e-02
## MI1:CBT_ref10:TIME1 -0.01216 0.09340 0.11150 -0.1090 9.132e-01
## MI1:CBT_ref10:TIME2 -0.17840 0.11400 0.10560 -1.6890 9.119e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.315100 0.04438 0.05522 -5.7060 1.000e-08
## cAGE 0.026960 0.01594 0.01444 1.8670 6.191e-02
## MI1 0.056190 0.06224 0.07596 0.7397 4.595e-01
## CBT_ref10 -0.009504 0.06579 0.08600 -0.1105 9.120e-01
## TIME_ref10 0.243500 0.04452 0.05530 4.4030 1.069e-05
## TIME_ref12 -0.073860 0.04460 0.04445 -1.6620 9.658e-02
## MI1:CBT_ref10 0.035020 0.09330 0.11180 0.3133 7.541e-01
## MI1:TIME_ref10 -0.063750 0.06246 0.07747 -0.8229 4.106e-01
## MI1:TIME_ref12 0.139800 0.06255 0.06420 2.1770 2.949e-02
## CBT_ref10:TIME_ref10 -0.014120 0.06581 0.08549 -0.1652 8.688e-01
## CBT_ref10:TIME_ref12 0.156900 0.06587 0.06913 2.2700 2.320e-02
## MI1:CBT_ref10:TIME_ref10 0.012160 0.09340 0.11150 0.1090 9.132e-01
## MI1:CBT_ref10:TIME_ref12 -0.166200 0.09348 0.09164 -1.8140 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 4
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_CBTref1_TIMEref2) #ref: MI=0, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.38900 0.04439 0.06573 -5.918 0.000e+00
## cAGE 0.02696 0.01594 0.01444 1.867 6.191e-02
## MI1 0.19590 0.06220 0.07833 2.502 1.236e-02
## CBT_ref10 0.14740 0.06552 0.08690 1.696 8.980e-02
## TIME_ref20 0.31730 0.05443 0.06581 4.822 1.420e-06
## TIME_ref21 0.07386 0.04460 0.04445 1.662 9.658e-02
## MI1:CBT_ref10 -0.13120 0.09295 0.10510 -1.249 2.118e-01
## MI1:TIME_ref20 -0.20350 0.07634 0.08021 -2.537 1.118e-02
## MI1:TIME_ref21 -0.13980 0.06255 0.06420 -2.177 2.949e-02
## CBT_ref10:TIME_ref20 -0.17110 0.08028 0.08717 -1.962 4.973e-02
## CBT_ref10:TIME_ref21 -0.15690 0.06587 0.06913 -2.270 2.320e-02
## MI1:CBT_ref10:TIME_ref20 0.17840 0.11400 0.10560 1.689 9.119e-02
## MI1:CBT_ref10:TIME_ref21 0.16620 0.09348 0.09164 1.814 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, time=0
## Estimates Model SE Robust SE wald p
## (Intercept) -0.079200 0.04352 0.01921 -4.12300 3.747e-05
## cAGE 0.026960 0.01594 0.01444 1.86700 6.191e-02
## MI_ref10 0.007560 0.06202 0.02627 0.28780 7.735e-01
## CBT_ref10 0.023550 0.06579 0.02266 1.03900 2.987e-01
## TIME1 -0.179700 0.04381 0.05428 -3.31100 9.280e-04
## TIME2 -0.113800 0.05352 0.04589 -2.48100 1.311e-02
## MI_ref10:CBT_ref10 -0.047180 0.09281 0.03847 -1.22600 2.201e-01
## MI_ref10:TIME1 -0.063750 0.06246 0.07747 -0.82290 4.106e-01
## MI_ref10:TIME2 -0.203500 0.07634 0.08021 -2.53700 1.118e-02
## CBT_ref10:TIME1 0.001961 0.06628 0.07162 0.02739 9.782e-01
## CBT_ref10:TIME2 -0.007311 0.08089 0.05958 -0.12270 9.023e-01
## MI_ref10:CBT_ref10:TIME1 0.012160 0.09340 0.11150 0.10900 9.132e-01
## MI_ref10:CBT_ref10:TIME2 0.178400 0.11400 0.10560 1.68900 9.119e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, time=1
## Estimates Model SE Robust SE wald p
## (Intercept) -0.258900 0.04366 0.05235 -4.94600 7.600e-07
## cAGE 0.026960 0.01594 0.01444 1.86700 6.191e-02
## MI_ref10 -0.056190 0.06224 0.07596 -0.73970 4.595e-01
## CBT_ref10 0.025510 0.06610 0.07123 0.35820 7.202e-01
## TIME_ref10 0.179700 0.04381 0.05428 3.31100 9.280e-04
## TIME_ref12 0.065890 0.04385 0.04632 1.42200 1.549e-01
## MI_ref10:CBT_ref10 -0.035020 0.09330 0.11180 -0.31330 7.541e-01
## MI_ref10:TIME_ref10 0.063750 0.06246 0.07747 0.82290 4.106e-01
## MI_ref10:TIME_ref12 -0.139800 0.06255 0.06420 -2.17700 2.949e-02
## CBT_ref10:TIME_ref10 -0.001961 0.06628 0.07162 -0.02739 9.782e-01
## CBT_ref10:TIME_ref12 -0.009272 0.06632 0.06015 -0.15420 8.775e-01
## MI_ref10:CBT_ref10:TIME_ref10 -0.012160 0.09340 0.11150 -0.10900 9.132e-01
## MI_ref10:CBT_ref10:TIME_ref12 0.166200 0.09348 0.09164 1.81400 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729
summary(SMDK_interaction_MIref1_CBTref1_TIMEref2) #ref: MI=1, CBT=1, time=2
## Estimates Model SE Robust SE wald p
## (Intercept) -0.193000 0.04360 0.04285 -4.5050 6.620e-06
## cAGE 0.026960 0.01594 0.01444 1.8670 6.191e-02
## MI_ref10 -0.195900 0.06220 0.07833 -2.5020 1.236e-02
## CBT_ref10 0.016240 0.06586 0.05858 0.2773 7.816e-01
## TIME_ref20 0.113800 0.05352 0.04589 2.4810 1.311e-02
## TIME_ref21 -0.065890 0.04385 0.04632 -1.4220 1.549e-01
## MI_ref10:CBT_ref10 0.131200 0.09295 0.10510 1.2490 2.118e-01
## MI_ref10:TIME_ref20 0.203500 0.07634 0.08021 2.5370 1.118e-02
## MI_ref10:TIME_ref21 0.139800 0.06255 0.06420 2.1770 2.949e-02
## CBT_ref10:TIME_ref20 0.007311 0.08089 0.05958 0.1227 9.023e-01
## CBT_ref10:TIME_ref21 0.009272 0.06632 0.06015 0.1542 8.775e-01
## MI_ref10:CBT_ref10:TIME_ref20 -0.178400 0.11400 0.10560 -1.6890 9.119e-02
## MI_ref10:CBT_ref10:TIME_ref21 -0.166200 0.09348 0.09164 -1.8140 6.974e-02
##
## Estimated Correlation Parameter: 0.4949
## Correlation Structure: ar1
## Est. Scale Parameter: 0.1244
##
## Number of GEE iterations: 3
## Number of Clusters: 243 Maximum Cluster Size: 3
## Number of observations with nonzero weight: 729