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Regression Tables from ‘GLM’, ‘GEE’, ‘GLMM’, ‘Cox’ and ‘survey’ Results for Publication.

Install

install.packages("jstable")


## From github: latest version
remotes::install_github('jinseob2kim/jstable')
library(jstable)

GLM Table

## Gaussian
glm_gaussian <- glm(mpg~cyl + disp, data = mtcars)
glmshow.display(glm_gaussian, decimal = 2)
## $first.line
## [1] "Linear regression predicting mpg\n"
## 
## $table
##      crude coeff.(95%CI)   crude P value adj. coeff.(95%CI)   
## cyl  "-2.88 (-3.51,-2.24)" "< 0.001"     "-1.59 (-2.98,-0.19)"
## disp "-0.04 (-0.05,-0.03)" "< 0.001"     "-0.02 (-0.04,0)"    
##      adj. P value
## cyl  "0.034"     
## disp "0.054"     
## 
## $last.lines
## [1] "No. of observations = 32\nR-squared = 0.7596\nAIC value = 167.1456\n\n"
## 
## attr(,"class")
## [1] "display" "list"
## Binomial
glm_binomial <- glm(vs~cyl + disp, data = mtcars, family = binomial)
glmshow.display(glm_binomial, decimal = 2)
## $first.line
## [1] "Logistic regression predicting vs\n"
## 
## $table
##      crude OR.(95%CI)   crude P value adj. OR.(95%CI)    adj. P value
## cyl  "0.2 (0.08,0.56)"  "0.002"       "0.15 (0.02,1.02)" "0.053"     
## disp "0.98 (0.97,0.99)" "0.002"       "1 (0.98,1.03)"    "0.715"     
## 
## $last.lines
## [1] "No. of observations = 32\nAIC value = 23.8304\n\n"
## 
## attr(,"class")
## [1] "display" "list"

GEE Table: from geeglm object from geepack package

library(geepack)  ## for dietox data
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
dietox$ddn = as.numeric(rnorm(nrow(dietox)) > 0)
gee01 <- geeglm (Weight ~ Time + Cu , id = Pig, data = dietox, family = gaussian, corstr = "ex")
geeglm.display(gee01)
## $caption
## [1] "GEE(gaussian) predicting Weight by Time, Cu - Group Pig"
## 
## $table
##            crude coeff(95%CI)   crude P value adj. coeff(95%CI)  
## Time       "6.94 (6.79,7.1)"    "< 0.001"     "6.94 (6.79,7.1)"  
## Cu: ref.=1 NA                   NA            NA                 
##    2       "-0.59 (-3.73,2.54)" "0.711"       "-0.84 (-3.9,2.23)"
##    3       "1.9 (-1.87,5.66)"   "0.324"       "1.77 (-1.9,5.45)" 
##            adj. P value
## Time       "< 0.001"   
## Cu: ref.=1 NA          
##    2       "0.593"     
##    3       "0.345"     
## 
## $metric
##                                  crude coeff(95%CI) crude P value
##                                  NA                 NA           
## Estimated correlation parameters "0.775"            NA           
## No. of clusters                  "72"               NA           
## No. of observations              "861"              NA           
##                                  adj. coeff(95%CI) adj. P value
##                                  NA                NA          
## Estimated correlation parameters NA                NA          
## No. of clusters                  NA                NA          
## No. of observations              NA                NA
gee02 <- geeglm (ddn ~ Time + Cu , id = Pig, data = dietox, family = binomial, corstr = "ex")
geeglm.display(gee02)
## $caption
## [1] "GEE(binomial) predicting ddn by Time, Cu - Group Pig"
## 
## $table
##            crude OR(95%CI)    crude P value adj. OR(95%CI)    
## Time       "0.99 (0.96,1.03)" "0.785"       "0.99 (0.96,1.03)"
## Cu: ref.=1 NA                 NA            NA                
##    2       "1.14 (0.79,1.65)" "0.485"       "1.14 (0.79,1.65)"
##    3       "1.06 (0.72,1.57)" "0.76"        "1.06 (0.72,1.57)"
##            adj. P value
## Time       "0.783"     
## Cu: ref.=1 NA          
##    2       "0.484"     
##    3       "0.76"      
## 
## $metric
##                                  crude OR(95%CI) crude P value
##                                  NA              NA           
## Estimated correlation parameters "0.026"         NA           
## No. of clusters                  "72"            NA           
## No. of observations              "861"           NA           
##                                  adj. OR(95%CI) adj. P value
##                                  NA             NA          
## Estimated correlation parameters NA             NA          
## No. of clusters                  NA             NA          
## No. of observations              NA             NA

Mixed model Table: lmerMod or glmerMod object from lme4 package

library(lme4)
l1 = lmer(Weight ~ Time + Cu + (1|Pig) + (1|Evit), data = dietox) 
lmer.display(l1, ci.ranef = T)
## $table
##                       crude coeff(95%CI) crude P value adj. coeff(95%CI)
## Time                    6.94 (6.88,7.01)     0.0000000  6.94 (6.88,7.01)
## Cu: ref.=1                          <NA>            NA              <NA>
##    2                  -0.57 (-4.66,3.52)     0.7837028 -0.81 (-4.42,2.8)
##    3                    1.9 (-2.23,6.04)     0.3666829 1.78 (-1.87,5.43)
## Random effects                      <NA>            NA              <NA>
## Pig                  39.71 (27.82,54.93)            NA              <NA>
## Evit                       0.9 (0,13.45)            NA              <NA>
## Residual              11.37 (10.3,12.55)            NA              <NA>
## Metrics                             <NA>            NA              <NA>
## No. of groups (Pig)                   72            NA              <NA>
## No. of groups (Evit)                   3            NA              <NA>
## No. of observations                  861            NA              <NA>
## Log-likelihood                  -2400.69            NA              <NA>
## AIC value                        4801.38            NA              <NA>
##                      adj. P value
## Time                    0.0000000
## Cu: ref.=1                     NA
##    2                    0.6598522
##    3                    0.3393579
## Random effects                 NA
## Pig                            NA
## Evit                           NA
## Residual                       NA
## Metrics                        NA
## No. of groups (Pig)            NA
## No. of groups (Evit)           NA
## No. of observations            NA
## Log-likelihood                 NA
## AIC value                      NA
## 
## $caption
## [1] "Linear mixed model fit by REML : Weight ~ Time + Cu + (1 | Pig) + (1 | Evit)"
l2 = glmer(ddn ~ Weight + Time + (1|Pig), data= dietox, family= "binomial")
lmer.display(l2)
## $table
##                      crude OR(95%CI) crude P value   adj. OR(95%CI)
## Weight                 1 (0.99,1.01)     0.9358643 1.01 (0.99,1.03)
## Time                0.99 (0.96,1.03)     0.7905865  0.95 (0.81,1.1)
## Random effects                  <NA>            NA             <NA>
## Pig                              0.1            NA             <NA>
## Metrics                         <NA>            NA             <NA>
## No. of groups (Pig)               72            NA             <NA>
## No. of observations              861            NA             <NA>
## Log-likelihood                -594.2            NA             <NA>
## AIC value                     1196.4            NA             <NA>
##                     adj. P value
## Weight                 0.4925311
## Time                   0.4642975
## Random effects                NA
## Pig                           NA
## Metrics                       NA
## No. of groups (Pig)           NA
## No. of observations           NA
## Log-likelihood                NA
## AIC value                     NA
## 
## $caption
## [1] "Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) : ddn ~ Weight + Time + (1 | Pig)"

Cox model with frailty or cluster options

library(survival)
fit1 <- coxph(Surv(time, status) ~ ph.ecog + age + cluster(inst), lung, model = T)  ## model = T: to extract original data
fit2 <- coxph(Surv(time, status) ~ ph.ecog + age + frailty(inst), lung, model = T)
cox2.display(fit1)
## $table
##         crude HR(95%CI)    crude P value adj. HR(95%CI)  adj. P value
## ph.ecog "1.61 (1.25,2.08)" "< 0.001"     "1.56 (1.22,2)" "< 0.001"   
## age     "1.02 (1.01,1.03)" "0.007"       "1.01 (1,1.02)" "0.085"     
## 
## $ranef
##         [,1] [,2] [,3] [,4]
## cluster   NA   NA   NA   NA
## inst      NA   NA   NA   NA
## 
## $metric
##                     [,1] [,2] [,3] [,4]
## <NA>                  NA   NA   NA   NA
## No. of observations  226   NA   NA   NA
## No. of events        163   NA   NA   NA
## 
## $caption
## [1] "Marginal Cox model on time ('time') to event ('status') - Group inst"
cox2.display(fit2)
## $table
##         crude HR(95%CI)    crude P value adj. HR(95%CI)     adj. P value
## ph.ecog "1.64 (1.31,2.05)" "< 0.001"     "1.58 (1.26,1.99)" "< 0.001"   
## age     "1.02 (1,1.04)"    "0.041"       "1.01 (0.99,1.03)" "0.225"     
## 
## $ranef
##         [,1] [,2] [,3] [,4]
## frailty   NA   NA   NA   NA
## inst      NA   NA   NA   NA
## 
## $metric
##                     [,1] [,2] [,3] [,4]
## <NA>                  NA   NA   NA   NA
## No. of observations  226   NA   NA   NA
## No. of events        163   NA   NA   NA
## 
## $caption
## [1] "Frailty Cox model on time ('time') to event ('status') - Group inst"

Cox mixed effect model Table: coxme object from coxme package

library(coxme)
fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1|inst), lung)
coxme.display(fit) 
## $table
##         crude HR(95%CI)    crude P value adj. HR(95%CI)     adj. P value
## ph.ecog "1.66 (1.32,2.09)" "< 0.001"     "1.61 (1.27,2.03)" "< 0.001"   
## age     "1.02 (1,1.04)"    "0.043"       "1.01 (0.99,1.03)" "0.227"     
## 
## $ranef
##                 [,1] [,2] [,3] [,4]
## Random effect     NA   NA   NA   NA
## inst(Intercept) 0.02   NA   NA   NA
## 
## $metric
##                     [,1] [,2] [,3] [,4]
## <NA>                  NA   NA   NA   NA
## No. of groups(inst)   18   NA   NA   NA
## No. of observations  226   NA   NA   NA
## No. of events        163   NA   NA   NA
## 
## $caption
## [1] "Mixed effects Cox model on time ('time') to event ('status') - Group inst"

GLM for survey data : svyglm object from survey package

library(survey)
data(api)
apistrat$tt = c(rep(1, 20), rep(0, nrow(apistrat) -20))
apistrat$tt2 = factor(c(rep(0, 40), rep(1, nrow(apistrat) -40)))

dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
ds <- svyglm(api00~ell+meals+mobility + tt2, design=dstrat)
ds2 <- svyglm(tt~ell+meals+mobility + tt2, design=dstrat, family = quasibinomial())
svyregress.display(ds)
## $first.line
## [1] "Linear regression predicting api00- weighted data\n"
## 
## $table
##             crude coeff.(95%CI)    crude P value adj. coeff.(95%CI)   
## ell         "-3.73 (-4.35,-3.11)"  "< 0.001"     "-0.48 (-1.25,0.29)" 
## meals       "-3.38 (-3.71,-3.05)"  "< 0.001"     "-3.14 (-3.69,-2.59)"
## mobility    "-1.43 (-3.3,0.44)"    "0.137"       "0.22 (-0.55,0.99)"  
## tt2: 1 vs 0 "10.98 (-34.16,56.12)" "0.634"       "6.13 (-17.89,30.15)"
##             adj. P value
## ell         "0.222"     
## meals       "< 0.001"   
## mobility    "0.573"     
## tt2: 1 vs 0 "0.618"     
## 
## $last.lines
## [1] "No. of observations = 200\nAIC value = 2309.8282\n\n"
## 
## attr(,"class")
## [1] "display" "list"
svyregress.display(ds2)
## $first.line
## [1] "Logistic regression predicting tt- weighted data\n"
## 
## $table
##             crude OR.(95%CI)   crude P value adj. OR.(95%CI)   
## ell         "1.02 (1,1.05)"    "0.047"       "1.11 (1.03,1.21)"
## meals       "1.01 (0.99,1.03)" "0.255"       "0.95 (0.91,1)"   
## mobility    "1.01 (0.98,1.03)" "0.506"       "1.1 (0.98,1.23)" 
## tt2: 1 vs 0 "0 (0,0)"          "< 0.001"     "0 (0,0)"         
##             adj. P value
## ell         "0.009"     
## meals       "0.068"     
## mobility    "0.114"     
## tt2: 1 vs 0 "< 0.001"   
## 
## $last.lines
## [1] "No. of observations = 200\n\n"
## 
## attr(,"class")
## [1] "display" "list"

Cox model for survey data :svycoxph object from survey package

data(pbc, package="survival")
pbc$sex = factor(pbc$sex)
pbc$stage = factor(pbc$stage)
pbc$randomized<-with(pbc, !is.na(trt) & trt>0)
biasmodel<-glm(randomized~age*edema,data=pbc,family=binomial)
pbc$randprob<-fitted(biasmodel)

if (is.null(pbc$albumin)) pbc$albumin<-pbc$alb ##pre2.9.0

dpbc <- svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized))

model <- svycoxph(Surv(time,status>0)~ sex + protime + albumin + stage,design=dpbc)
svycox.display(model)
## Stratified Independent Sampling design (with replacement)
## svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, 
##     randomized))
## Stratified Independent Sampling design (with replacement)
## svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, 
##     randomized))
## Stratified Independent Sampling design (with replacement)
## svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, 
##     randomized))
## Stratified Independent Sampling design (with replacement)
## svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, 
##     randomized))
## Stratified Independent Sampling design (with replacement)
## svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, 
##     randomized))

## $table
##               crude HR(95%CI)      crude P value adj. HR(95%CI)       
## sex: f vs m   "0.62 (0.4,0.97)"    "0.038"       "0.55 (0.33,0.9)"    
## protime       "1.37 (1.09,1.72)"   "0.006"       "1.52 (1.2,1.91)"    
## albumin       "0.2 (0.14,0.29)"    "< 0.001"     "0.31 (0.2,0.47)"    
## stage: ref.=1 NA                   NA            NA                   
##    2          "5.67 (0.77,41.78)"  "0.089"       "10.94 (1.01,118.55)"
##    3          "9.78 (1.37,69.94)"  "0.023"       "17.03 (1.69,171.6)" 
##    4          "22.89 (3.2,163.48)" "0.002"       "22.56 (2.25,226.42)"
##               adj. P value
## sex: f vs m   "0.017"     
## protime       "< 0.001"   
## albumin       "< 0.001"   
## stage: ref.=1 NA          
##    2          "0.049"     
##    3          "0.016"     
##    4          "0.008"     
## 
## $metric
##                        [,1] [,2] [,3] [,4]
## <NA>                     NA   NA   NA   NA
## No. of observations  312.00   NA   NA   NA
## No. of events        144.00   NA   NA   NA
## AIC                 1483.12   NA   NA   NA
## 
## $caption
## [1] "Survey cox model on time ('time') to event ('status > 0')"

Sub-group analysis for Cox/svycox model

library(survival); library(dplyr)
lung %>% 
mutate(status = as.integer(status == 1),
       sex = factor(sex),
       kk = factor(as.integer(pat.karno >= 70)),
       kk1 = factor(as.integer(pat.karno >= 60))) -> lung

TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data=lung, line = T)
##   Variable Count Percent Point Estimate Lower Upper    0    1 P value
## 1  Overall   228     100           1.91  1.14   3.2 41.3 58.7   0.014
## 2     <NA>  <NA>    <NA>           <NA>  <NA>  <NA> <NA> <NA>    <NA>
## 3       kk  <NA>    <NA>           <NA>  <NA>  <NA> <NA> <NA>    <NA>
## 4        0    38      38           2.88  0.31 26.49   20   80    0.35
## 5        1   187     187           1.84  1.08  3.14 43.1 56.9   0.026
## 6     <NA>  <NA>    <NA>           <NA>  <NA>  <NA> <NA> <NA>    <NA>
## 7      kk1  <NA>    <NA>           <NA>  <NA>  <NA> <NA> <NA>    <NA>
## 8        0     8       8           <NA>  <NA>  <NA>    0  100    <NA>
## 9        1   217     217           1.88  1.12  3.17 42.6 57.4   0.018
##   P for interaction
## 1              <NA>
## 2              <NA>
## 3             0.525
## 4              <NA>
## 5              <NA>
## 6              <NA>
## 7             0.997
## 8              <NA>
## 9              <NA>
## Survey data
library(survey)
data.design <- svydesign(id = ~1, data = lung)
TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = data.design, line = F)
## Independent Sampling design (with replacement)
## svydesign(id = ~1, data = lung)
## Independent Sampling design (with replacement)
## svydesign(id = ~1, data = lung)
## Independent Sampling design (with replacement)
## subset(data, get(var_subgroup) == .)
## Independent Sampling design (with replacement)
## subset(data, get(var_subgroup) == .)
## Independent Sampling design (with replacement)
## svydesign(id = ~1, data = lung)
## Independent Sampling design (with replacement)
## subset(data, get(var_subgroup) == .)

##   Variable Count Percent Point Estimate Lower Upper    0    1 P value
## 1  Overall   228     100           1.91  1.14  3.19 41.3 58.7   0.013
## 2       kk  <NA>    <NA>           <NA>  <NA>  <NA> <NA> <NA>    <NA>
## 3        0    38      38           2.88  0.31  27.1   20   80   0.355
## 4        1   187     187           1.84  1.08  3.11 43.1 56.9   0.024
## 5      kk1  <NA>    <NA>           <NA>  <NA>  <NA> <NA> <NA>    <NA>
## 6        0  <NA>    <NA>           <NA>  <NA>  <NA> <NA> <NA>    <NA>
## 7        1   217     217           1.88  1.12  3.15 42.6 57.4   0.017
##   P for interaction
## 1              <NA>
## 2             0.523
## 3              <NA>
## 4              <NA>
## 5            <0.001
## 6              <NA>
## 7              <NA>