jstable
Create Tables from Different Types of Regression
Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
jstable
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) adj. P value
## cyl "-2.88 (-3.51,-2.24)" "< 0.001" "-1.59 (-2.98,-0.19)" "0.034"
## disp "-0.04 (-0.05,-0.03)" "< 0.001" "-0.02 (-0.04,0)" "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.=Cu000 NA NA NA
## 035 "-0.59 (-3.73,2.54)" "0.711" "-0.84 (-3.9,2.23)"
## 175 "1.9 (-1.87,5.66)" "0.324" "1.77 (-1.9,5.45)"
## adj. P value
## Time "< 0.001"
## Cu: ref.=Cu000 NA
## 035 "0.593"
## 175 "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) adj. P value
## Time "0.99 (0.96,1.03)" "0.729" "0.99 (0.96,1.03)" "0.727"
## Cu: ref.=Cu000 NA NA NA NA
## 035 "1.2 (0.81,1.78)" "0.364" "1.2 (0.81,1.78)" "0.364"
## 175 "1.03 (0.71,1.48)" "0.889" "1.03 (0.71,1.48)" "0.889"
##
## $metric
## crude OR(95%CI) crude P value adj. OR(95%CI)
## NA NA NA
## Estimated correlation parameters "0.031" NA NA
## No. of clusters "72" NA NA
## No. of observations "861" NA NA
## adj. P value
## NA
## Estimated correlation parameters NA
## No. of clusters NA
## No. of observations NA
Mixed model Table: lmerMod or glmerMod object from lme4 package
library(lme4)
l1 <- lmer(Weight ~ Time + Cu + (1|Pig), 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.=Cu000 <NA> NA <NA>
## 035 -0.58 (-4.67,3.51) 0.7811327 -0.84 (-4.47,2.8)
## 175 1.9 (-2.23,6.04) 0.3670740 1.77 (-1.9,5.45)
## Random effects <NA> NA <NA>
## Pig 40.34 (28.08,54.95) NA <NA>
## Residual 11.37 (10.3,12.55) NA <NA>
## Metrics <NA> NA <NA>
## No. of groups (Pig) 72 NA <NA>
## No. of observations 861 NA <NA>
## Log-likelihood -2400.8 NA <NA>
## AIC value 4801.6 NA <NA>
## adj. P value
## Time 0.0000000
## Cu: ref.=Cu000 NA
## 035 0.6527264
## 175 0.3442309
## Random effects NA
## Pig NA
## Residual NA
## Metrics NA
## No. of groups (Pig) NA
## No. of observations NA
## Log-likelihood NA
## AIC value NA
##
## $caption
## [1] "Linear mixed model fit by REML : Weight ~ Time + Cu + (1 | Pig)"
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) 0.5477787 0.99 (0.97,1.01)
## Time 0.99 (0.96,1.03) 0.7532531 1.09 (0.93,1.27)
## Random effects <NA> NA <NA>
## Pig 0.11 NA <NA>
## Metrics <NA> NA <NA>
## No. of groups (Pig) 72 NA <NA>
## No. of observations 861 NA <NA>
## Log-likelihood -594.08 NA <NA>
## AIC value 1196.16 NA <NA>
## adj. P value
## Weight 0.2256157
## Time 0.2754273
## 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) adj. P value
## ell "1.02 (1,1.05)" "0.047" "1.11 (1.03,1.21)" "0.009"
## meals "1.01 (0.99,1.03)" "0.255" "0.95 (0.91,1)" "0.068"
## mobility "1.01 (0.98,1.03)" "0.506" "1.1 (0.98,1.23)" "0.114"
## tt2: 1 vs 0 "0 (0,0)" "< 0.001" "0 (0,0)" "< 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 1480.29 NA NA NA
##
## $caption
## [1] "Survey cox model on time ('time') to event ('status > 0')"
Sub-group analysis for Cox/svycox model
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 = TRUE)
## Variable Count Percent Point Estimate Lower Upper 1 2 P value
## 1 Overall 228 100 1.91 1.14 3.2 100 100 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 16.9 2.88 0.31 26.49 10 100 0.35
## 5 1 187 83.1 1.84 1.08 3.14 100 100 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 3.6 <NA> <NA> <NA> 0 100 <NA>
## 9 1 217 96.4 1.88 1.12 3.17 100 100 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 = FALSE)
## 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 1 2 P value
## 1 Overall 228 100 1.91 1.14 3.19 100 100 0.013
## 2 kk <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 0 38 16.9 2.88 0.31 27.1 10 100 0.355
## 4 1 187 83.1 1.84 1.08 3.11 100 100 0.024
## 5 kk1 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 6 0 <NA> <NA> <NA> <NA> <NA> 0 100 <NA>
## 7 1 217 <NA> 1.88 1.12 3.15 100 100 0.017
## P for interaction
## 1 <NA>
## 2 0.523
## 3 <NA>
## 4 <NA>
## 5 <0.001
## 6 <NA>
## 7 <NA>
Sub-group analysis for GLM
TableSubgroupMultiGLM(status ~ sex, var_subgroups = c("kk", "kk1"), data = lung, family = "binomial")
## Variable Count Percent OR Lower Upper P value P for interaction
## 1 Overall 228 100 3.01 1.66 5.52 <0.001 <NA>
## 2 kk <NA> <NA> <NA> <NA> <NA> <NA> 0.476
## 3 0 38 16.9 7 0.91 145.62 0.098 <NA>
## 4 1 187 83.1 2.94 1.56 5.62 0.001 <NA>
## 5 kk1 <NA> <NA> <NA> <NA> <NA> <NA> 0.984
## 6 0 8 3.6 314366015.19 0 <NA> 0.997 <NA>
## 7 1 217 96.4 2.85 1.56 5.29 0.001 <NA>
## Survey data
TableSubgroupMultiGLM(pat.karno ~ sex, var_subgroups = c("kk", "kk1"), data = data.design, family = "gaussian", line = TRUE)
## Variable Count Percent Point.Estimate Lower Upper P value P for interaction
## 1 Overall 225 100 1.37 -2.58 5.33 0.496 <NA>
## 2 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 3 kk <NA> <NA> <NA> <NA> <NA> <NA> 0.231
## 4 0 38 16.9 -1.19 -6.5 4.11 0.662 <NA>
## 5 1 187 83.1 2.53 -0.42 5.47 0.094 <NA>
## 6 <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 7 kk1 <NA> <NA> <NA> <NA> <NA> <NA> 0.738
## 8 0 8 3.6 0 -11.52 11.52 1 <NA>
## 9 1 217 96.4 2.06 -1.43 5.55 0.249 <NA>