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(magrittr)
library(geeM)
## Loading required package: Matrix
# Read data ---------------------------
gee_nobl_dt <- read_sas("/Volumes/caas/MERITS/RESOURCES/Data Processing/Data Analysis/Outcome Analyses 2016/Manuscript Revisions_2020/Aditya Khanna Analyses 2022/RESOURCES-AK-R/../GEEOUTCOME2021NOBL-frombackup.sas7bdat") #no baseline data
dim(gee_nobl_dt)
## [1] 604 37
# Specify corstr = ar1 ---------------------------
corstr = "ar1"
# Create covariate data objects ---------------------------
covariates_nobl_dt <-
cbind.data.frame(
cAGE = gee_nobl_dt$AGE-mean(gee_nobl_dt$AGE),
cBLCGSMD = gee_nobl_dt$BLCGSMD - mean(gee_nobl_dt$BLCGSMD),
MI = as.factor(gee_nobl_dt$MI),
CBT = as.factor(gee_nobl_dt$CBT),
TIME=as.factor(gee_nobl_dt$TIME),
#CBT=as.factor(gee_nobl_dt$CBT),
M3ENVDAYS = gee_nobl_dt$M3ENVDAYS,
M6ENVDAYS = gee_nobl_dt$M6ENVDAYS ,
MIBYCBT = as.factor(gee_nobl_dt$MIBYCBT),
MIBYTIME = as.factor(gee_nobl_dt$MIBYTIME),
CBTBYTIME = as.factor(gee_nobl_dt$CBTBYTIME),
MIBYCBTBYTIME = as.factor(gee_nobl_dt$MIBYCBTBYTIME),
id = gee_nobl_dt$id,
log_unctrldays = gee_nobl_dt$log_unctrldays
)
# DV = LONGABS ---------------------------
## check outcome variable(s) distributions
summary(gee_nobl_dt$CLONGABS)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 6.178 3.000 60.000 42
hist(gee_nobl_dt$CLONGABS)
summary(gee_nobl_dt$LCLONGABS)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.3622 0.6021 1.7853 42
hist(gee_nobl_dt$LCLONGABS)
## create dataframe
LONGABS_nobl_dt <- as.data.frame(
cbind.data.frame(CLONGABS = gee_nobl_dt$CLONGABS,
LCLONGABS = gee_nobl_dt$LCLONGABS,
covariates_nobl_dt)
)
class(LONGABS_nobl_dt)
## [1] "data.frame"
dim(LONGABS_nobl_dt)
## [1] 604 15
summary(LONGABS_nobl_dt)
## CLONGABS LCLONGABS cAGE cBLCGSMD MI
## Min. : 0.000 Min. :0.0000 Min. :-2.8880 Min. :-10.926 0:302
## 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:-0.7921 1st Qu.: -7.029 1:302
## Median : 0.000 Median :0.0000 Median : 0.1148 Median : -1.926
## Mean : 6.178 Mean :0.3622 Mean : 0.0000 Mean : 0.000
## 3rd Qu.: 3.000 3rd Qu.:0.6021 3rd Qu.: 0.7997 3rd Qu.: 5.860
## Max. :60.000 Max. :1.7853 Max. : 2.3312 Max. : 49.419
## NA's :42 NA's :42
## CBT TIME M3ENVDAYS M6ENVDAYS MIBYCBT MIBYTIME
## 0 :274 0:302 Min. : 0.00 Min. : 0.00 0 :438 0:453
## 1 :318 1:302 1st Qu.: 0.00 1st Qu.: 0.00 1 :158 1:151
## NA's: 12 Median :10.00 Median : 0.00 NA's: 8
## Mean :21.26 Mean :19.87
## 3rd Qu.:41.00 3rd Qu.:36.00
## Max. :60.00 Max. :60.00
## NA's :38 NA's :46
## CBTBYTIME MIBYCBTBYTIME id log_unctrldays
## 0 :439 0 :521 Min. : 1.0 Min. :0.000
## 1 :159 1 : 79 1st Qu.: 78.0 1st Qu.:2.833
## NA's: 6 NA's: 4 Median :154.5 Median :3.496
## Mean :157.1 Mean :3.094
## 3rd Qu.:238.0 3rd Qu.:4.111
## Max. :316.0 Max. :4.111
##
colnames(LONGABS_nobl_dt)
## [1] "CLONGABS" "LCLONGABS" "cAGE" "cBLCGSMD"
## [5] "MI" "CBT" "TIME" "M3ENVDAYS"
## [9] "M6ENVDAYS" "MIBYCBT" "MIBYTIME" "CBTBYTIME"
## [13] "MIBYCBTBYTIME" "id" "log_unctrldays"
## restrict to complete cases
LONGABS_nobl_dt_na.omit <- na.omit(LONGABS_nobl_dt)
dim(LONGABS_nobl_dt_na.omit)
## [1] 556 15
summary(LONGABS_nobl_dt_na.omit)
## CLONGABS LCLONGABS cAGE cBLCGSMD
## Min. : 0.000 Min. :0.0000 Min. :-2.88795 Min. :-10.9258
## 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.:-0.83042 1st Qu.: -7.4924
## Median : 0.000 Median :0.0000 Median : 0.01890 Median : -1.9258
## Mean : 6.237 Mean :0.3648 Mean :-0.02631 Mean : -0.1177
## 3rd Qu.: 3.000 3rd Qu.:0.6021 3rd Qu.: 0.79424 3rd Qu.: 5.6075
## Max. :60.000 Max. :1.7853 Max. : 2.33123 Max. : 49.4190
## MI CBT TIME M3ENVDAYS M6ENVDAYS MIBYCBT MIBYTIME
## 0:282 0:256 0:278 Min. : 0.00 Min. : 0.00 0:408 0:419
## 1:274 1:300 1:278 1st Qu.: 0.00 1st Qu.: 0.00 1:148 1:137
## Median : 9.50 Median : 0.00
## Mean :21.14 Mean :19.94
## 3rd Qu.:41.00 3rd Qu.:36.00
## Max. :60.00 Max. :60.00
## CBTBYTIME MIBYCBTBYTIME id log_unctrldays
## 0:406 0:482 Min. : 1.0 Min. :0.000
## 1:150 1: 74 1st Qu.: 77.0 1st Qu.:3.178
## Median :152.5 Median :3.998
## Mean :154.7 Mean :3.328
## 3rd Qu.:235.0 3rd Qu.:4.111
## Max. :316.0 Max. :4.111
colnames(LONGABS_nobl_dt_na.omit)
## [1] "CLONGABS" "LCLONGABS" "cAGE" "cBLCGSMD"
## [5] "MI" "CBT" "TIME" "M3ENVDAYS"
## [9] "M6ENVDAYS" "MIBYCBT" "MIBYTIME" "CBTBYTIME"
## [13] "MIBYCBTBYTIME" "id" "log_unctrldays"
## define formulae
LCLONGABS.nobl.main =
LCLONGABS~ #logged
cAGE +
cBLCGSMD+
MI +
CBT +
TIME +
offset( log_unctrldays)
CLONGABS.nobl.main =
CLONGABS~ #unlogged
cAGE +
cBLCGSMD+
MI +
CBT +
TIME +
offset( log_unctrldays)
LCLONGABS.nobl.interaction =
LCLONGABS~ #logged
cAGE +
cBLCGSMD+
MI +
CBT +
TIME +
MI*CBT+
MI*TIME+
CBT*TIME+
MI*CBT*TIME+
offset( log_unctrldays)
CLONGABS.nobl.interaction =
CLONGABS~ #unlogged
cAGE +
cBLCGSMD+
MI +
CBT +
TIME +
MI*CBT+
MI*TIME+
CBT*TIME+
MI*CBT*TIME+
offset( log_unctrldays)
# DV = LONGABS ---------------------------
# modeling
## fit main model
#relevel MI and CBT
LONGABS_nobl_dt$MI_ref1 <- LONGABS_nobl_dt$MI %>% relevel("1")
LONGABS_nobl_dt$CBT_ref1 <- LONGABS_nobl_dt$CBT %>% relevel("1")
LONGABS_nobl_dt$TIME_ref1 <- LONGABS_nobl_dt$TIME %>% relevel("1")
CLONGABS_nobl_main <- geem(formula =
CLONGABS ~ cAGE + cBLCGSMD + MI + CBT + TIME,
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print main model summary
summary(CLONGABS_nobl_main) #unlogged
## Estimates Model SE Robust SE wald p
## (Intercept) 1.725000 0.23570 0.22090 7.80600 0.000000
## cAGE -0.128900 0.11610 0.11420 -1.12900 0.258900
## cBLCGSMD -0.001244 0.01455 0.01597 -0.07790 0.937900
## MI1 -0.747300 0.25370 0.24840 -3.00800 0.002628
## CBT1 0.666200 0.25430 0.24460 2.72400 0.006453
## TIME1 0.008899 0.13590 0.13410 0.06635 0.947100
##
## Estimated Correlation Parameter: 0.5516
## Correlation Structure: ar1
## Est. Scale Parameter: 4.821
##
## Number of GEE iterations: 4
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
## fit interaction models
CLONGABS_nobl_interaction <- geem(formula = CLONGABS.nobl.interaction,
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction models' summary
summary(CLONGABS_nobl_interaction) #unlogged
## Estimates Model SE Robust SE wald p
## (Intercept) -1.93700 0.24060 0.22920 -8.4500 0.00000
## cAGE -0.17350 0.09237 0.10340 -1.6780 0.09328
## cBLCGSMD -0.01200 0.01158 0.01789 -0.6710 0.50220
## MI1 -0.54560 0.35440 0.36740 -1.4850 0.13750
## CBT1 0.31990 0.32360 0.29100 1.0990 0.27160
## TIME1 -0.07626 0.24310 0.23590 -0.3233 0.74650
## MI1:CBT1 0.32140 0.46990 0.47210 0.6809 0.49600
## MI1:TIME1 -0.47490 0.37020 0.40590 -1.1700 0.24200
## CBT1:TIME1 0.32360 0.32630 0.27670 1.1690 0.24220
## MI1:CBT1:TIME1 -0.06697 0.48430 0.50950 -0.1315 0.89540
##
## Estimated Correlation Parameter: 0.5029
## Correlation Structure: ar1
## Est. Scale Parameter: 2.94
##
## Number of GEE iterations: 17
## Number of Clusters: 282 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 560
# DV = LONGABS ---------------------------
# #relevel MI and CBT
LONGABS_nobl_dt$MI_ref1 <- LONGABS_nobl_dt$MI %>% relevel("1")
LONGABS_nobl_dt$CBT_ref1 <- LONGABS_nobl_dt$CBT %>% relevel("1")
LONGABS_nobl_dt$TIME_ref1 <- LONGABS_nobl_dt$TIME %>% relevel("1")
# ignore missing values
## create an indicator column for any missing values
LONGABS_nobl_dt$any_na <- LONGABS_nobl_dt %>% apply(1, function(x){any(is.na(x))})
## left join the dataset by the id column using the group_by() function
LONGABS_nobl_dt %<>% left_join(LONGABS_nobl_dt %>% group_by(id) %>%
summarise(any_na2 = any(any_na)), by="id")
## filter out rows with any NAs
LONGABS_nobl_dt %<>% filter(any_na2 != T)
# modeling
## main model
LONGABS_nobl_main <- geem(formula = CLONGABS.nobl.main,
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## Warning in geem(formula = CLONGABS.nobl.main, data = LONGABS_nobl_dt, id = id,
## : Did not converge
## print main model summary
summary(LONGABS_nobl_main)
## Estimates Model SE Robust SE wald p
## (Intercept) -1.97100 0.18990 0.20800 -9.4740 0.000000
## cAGE -0.14630 0.09258 0.10710 -1.3660 0.171900
## cBLCGSMD -0.01598 0.01151 0.02026 -0.7886 0.430400
## MI1 -0.59970 0.20440 0.21530 -2.7860 0.005342
## CBT1 0.60390 0.20600 0.22100 2.7330 0.006283
## TIME1 -0.12680 0.11960 0.11920 -1.0640 0.287500
##
## Estimated Correlation Parameter: 0.4962
## Correlation Structure: ar1
## Est. Scale Parameter: 2.955
##
## Number of GEE iterations: 20
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
## interaction model
LONGABS_nobl_interaction <- geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI + CBT + TIME + MI * CBT + MI * TIME +
CBT * TIME + MI * CBT * TIME,
#mi=0, cbt=0, time=0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_TIMEref1 <- geem(formula =
CLONGABS ~ cAGE + cBLCGSMD + MI + CBT + TIME_ref1 + MI * CBT + MI * TIME_ref1 +
CBT * TIME_ref1 + MI * CBT * TIME_ref1,
#mi=0, cbt=0, time=1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME + MI_ref1 * CBT + MI_ref1 * TIME +
CBT * TIME + MI_ref1 * CBT * TIME,
#ref: Mi=1, Cbt=0, Time=0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1_TIMEref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT + TIME_ref1 +
MI_ref1 * CBT + MI_ref1 * TIME_ref1 +
CBT * TIME_ref1 + MI_ref1 * CBT * TIME_ref1,
#ref: Mi=1, Cbt=0, Time=1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_CBTref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME + MI * CBT_ref1 + MI *
TIME + CBT_ref1 * TIME + MI * CBT_ref1 * TIME,
#ref: Mi=0, Cbt=1, Time = 0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_CBTref1_TIMEref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI + CBT_ref1 + TIME_ref1 +
MI * CBT_ref1 + MI * TIME_ref1 + CBT_ref1 * TIME_ref1 +
MI * CBT_ref1 * TIME_ref1,
#ref: Mi=0, Cbt=1, Time = 1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1_CBTref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME + MI_ref1 * CBT_ref1 + MI_ref1 *
TIME + CBT_ref1 * TIME + MI_ref1 * CBT_ref1 * TIME,
#ref: Mi=1, Cbt=1, Time=0
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
LONGABS_nobl_interaction_MIref1_CBTref1_TIMEref1 <-
geem(formula = CLONGABS ~ cAGE + cBLCGSMD + MI_ref1 + CBT_ref1 + TIME_ref1 +
MI_ref1 * CBT_ref1 + MI_ref1 * TIME_ref1 +
CBT_ref1 * TIME_ref1 + MI_ref1 * CBT_ref1 * TIME_ref1,
#ref: Mi=1, Cbt=1, Time=1
data = LONGABS_nobl_dt,
id=id,
family = MASS::negative.binomial(1),
corstr = corstr
)
## print interaction model summary
summary(LONGABS_nobl_interaction) #base: MI=0, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.6550000 0.29820 0.25250 6.55500 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -0.5604000 0.43700 0.37890 -1.47900 0.13920
## CBT1 0.5545000 0.40080 0.31870 1.74000 0.08184
## TIME1 0.2209000 0.28000 0.26050 0.84810 0.39640
## MI1:CBT1 0.2036000 0.58100 0.51850 0.39270 0.69460
## MI1:TIME1 -0.6803000 0.42240 0.47810 -1.42300 0.15470
## CBT1:TIME1 0.0351800 0.37770 0.30920 0.11380 0.90940
## MI1:CBT1:TIME1 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_TIMEref1) #base: MI=0, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.8760000 0.29340 0.30820 6.08800 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -1.2410000 0.45210 0.53510 -2.31900 0.02041
## CBT1 0.5897000 0.39510 0.35560 1.65800 0.09723
## TIME_ref10 -0.2209000 0.28000 0.26050 -0.84810 0.39640
## MI1:CBT1 0.4054000 0.59250 0.66560 0.60900 0.54250
## MI1:TIME_ref10 0.6803000 0.42240 0.47810 1.42300 0.15470
## CBT1:TIME_ref10 -0.0351800 0.37770 0.30920 -0.11380 0.90940
## MI1:CBT1:TIME_ref10 -0.2018000 0.55760 0.58310 -0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1) #ref: MI=1, CBT=0, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.0950000 0.31880 0.27900 3.92400 8.727e-05
## cAGE -0.1379000 0.11640 0.11550 -1.19500 2.323e-01
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 9.633e-01
## MI_ref10 0.5604000 0.43700 0.37890 1.47900 1.392e-01
## CBT1 0.7581000 0.41910 0.40670 1.86400 6.228e-02
## TIME1 -0.4594000 0.31630 0.40090 -1.14600 2.518e-01
## MI_ref10:CBT1 -0.2036000 0.58100 0.51850 -0.39270 6.946e-01
## MI_ref10:TIME1 0.6803000 0.42240 0.47810 1.42300 1.547e-01
## CBT1:TIME1 0.2369000 0.41020 0.49430 0.47930 6.317e-01
## MI_ref10:CBT1:TIME1 -0.2018000 0.55760 0.58310 -0.34600 7.293e-01
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1_TIMEref1) #ref: MI=1, CBT=0, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 0.6352000 0.34350 0.42150 1.50700 0.13180
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI_ref10 1.2410000 0.45210 0.53510 2.31900 0.02041
## CBT1 0.9951000 0.44020 0.55310 1.79900 0.07202
## TIME_ref10 0.4594000 0.31630 0.40090 1.14600 0.25180
## MI_ref10:CBT1 -0.4054000 0.59250 0.66560 -0.60900 0.54250
## MI_ref10:TIME_ref10 -0.6803000 0.42240 0.47810 -1.42300 0.15470
## CBT1:TIME_ref10 -0.2369000 0.41020 0.49430 -0.47930 0.63170
## MI_ref10:CBT1:TIME_ref10 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_CBTref1) #ref: MI=0, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 2.2100000 0.26730 0.19520 11.32000 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -0.3568000 0.38190 0.34760 -1.02700 0.30470
## CBT_ref10 -0.5545000 0.40080 0.31870 -1.74000 0.08184
## TIME1 0.2561000 0.25350 0.16670 1.53600 0.12450
## MI1:CBT_ref10 -0.2036000 0.58100 0.51850 -0.39270 0.69460
## MI1:TIME1 -0.4786000 0.36400 0.33300 -1.43700 0.15070
## CBT_ref10:TIME1 -0.0351800 0.37770 0.30920 -0.11380 0.90940
## MI1:CBT_ref10:TIME1 -0.2018000 0.55760 0.58310 -0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_CBTref1_TIMEref1) #ref: MI=0, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 2.4660000 0.26420 0.17860 13.81000 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI1 -0.8354000 0.38220 0.38850 -2.15000 0.03153
## CBT_ref10 -0.5897000 0.39510 0.35560 -1.65800 0.09723
## TIME_ref10 -0.2561000 0.25350 0.16670 -1.53600 0.12450
## MI1:CBT_ref10 -0.4054000 0.59250 0.66560 -0.60900 0.54250
## MI1:TIME_ref10 0.4786000 0.36400 0.33300 1.43700 0.15070
## CBT_ref10:TIME_ref10 0.0351800 0.37770 0.30920 0.11380 0.90940
## MI1:CBT_ref10:TIME_ref10 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1_CBTref1) #ref: MI=1, CBT=1, Time=0
## Estimates Model SE Robust SE wald p
## (Intercept) 1.8530000 0.27250 0.28890 6.41200 0.00000
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.23230
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.96330
## MI_ref10 0.3568000 0.38190 0.34760 1.02700 0.30470
## CBT_ref10 -0.7581000 0.41910 0.40670 -1.86400 0.06228
## TIME1 -0.2225000 0.26110 0.28850 -0.77130 0.44050
## MI_ref10:CBT_ref10 0.2036000 0.58100 0.51850 0.39270 0.69460
## MI_ref10:TIME1 0.4786000 0.36400 0.33300 1.43700 0.15070
## CBT_ref10:TIME1 -0.2369000 0.41020 0.49430 -0.47930 0.63170
## MI_ref10:CBT_ref10:TIME1 0.2018000 0.55760 0.58310 0.34600 0.72930
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556
summary(LONGABS_nobl_interaction_MIref1_CBTref1_TIMEref1) #ref: MI=1, CBT=1, Time=1
## Estimates Model SE Robust SE wald p
## (Intercept) 1.6300000 0.27590 0.34740 4.69300 0.0000027
## cAGE -0.1379000 0.11640 0.11550 -1.19500 0.2323000
## cBLCGSMD 0.0007003 0.01457 0.01521 0.04605 0.9633000
## MI_ref10 0.8354000 0.38220 0.38850 2.15000 0.0315300
## CBT_ref10 -0.9951000 0.44020 0.55310 -1.79900 0.0720200
## TIME_ref10 0.2225000 0.26110 0.28850 0.77130 0.4405000
## MI_ref10:CBT_ref10 0.4054000 0.59250 0.66560 0.60900 0.5425000
## MI_ref10:TIME_ref10 -0.4786000 0.36400 0.33300 -1.43700 0.1507000
## CBT_ref10:TIME_ref10 0.2369000 0.41020 0.49430 0.47930 0.6317000
## MI_ref10:CBT_ref10:TIME_ref10 -0.2018000 0.55760 0.58310 -0.34600 0.7293000
##
## Estimated Correlation Parameter: 0.545
## Correlation Structure: ar1
## Est. Scale Parameter: 4.796
##
## Number of GEE iterations: 4
## Number of Clusters: 278 Maximum Cluster Size: 2
## Number of observations with nonzero weight: 556