TableSubgroupGLM: Sub-group analysis table for GLM and GLMM(lme4 package).
Source:R/forestglm.R
TableSubgroupGLM.Rd
Sub-group analysis table for GLM.
Usage
TableSubgroupGLM(
formula,
var_subgroup = NULL,
var_cov = NULL,
data,
family = "binomial",
decimal.estimate = 2,
decimal.percent = 1,
decimal.pvalue = 3,
labeldata = NULL,
count_by = NULL,
event = FALSE
)
Arguments
- formula
formula with survival analysis.
- var_subgroup
1 sub-group variable for analysis, Default: NULL
- var_cov
Variables for additional adjust, Default: NULL
- data
Data or svydesign in survey package.
- family
family, "gaussian" or "binomial" or 'poisson' or 'quasipoisson'
- decimal.estimate
Decimal for estimate, Default: 2
- decimal.percent
Decimal for percent, Default: 1
- decimal.pvalue
Decimal for pvalue, Default: 3
- labeldata
Label info, made by `mk.lev` function, Default: NULL
- count_by
Variable name to stratify counts by (string). Default: NULL.
- event
If `TRUE`, show counts/metrics instead of only model estimates. Default: FALSE.
Examples
library(survival)
library(dplyr)
lung %>%
mutate(
status = as.integer(status == 1),
sex = factor(sex),
kk = factor(as.integer(pat.karno >= 70))
) -> lung
TableSubgroupGLM(status ~ sex, data = lung, family = "binomial")
#> Variable Count Percent OR Lower Upper P value P for interaction
#> sex2 Overall 228 100 3.01 1.65 5.47 <0.001 NA
TableSubgroupGLM(status ~ sex, var_subgroup = "kk", data = lung, family = "binomial")
#> Variable Count Percent OR Lower Upper P value P for interaction
#> 1 kk <NA> <NA> <NA> <NA> <NA> NA 0.476
#> 2 0 38 16.9 7 0.7 70.03 0.098 <NA>
#> 3 1 187 83.1 2.94 1.55 5.57 0.001 <NA>
## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
#> Warning: No weights or probabilities supplied, assuming equal probability
TableSubgroupGLM(status ~ sex, data = data.design, family = "binomial")
#> Variable Count Percent OR Lower Upper P value P for interaction
#> sex2 Overall 228 100 3.01 1.65 5.48 <0.001 NA
TableSubgroupGLM(status ~ sex, var_subgroup = "kk", data = data.design, family = "binomial")
#> Variable Count Percent OR Lower Upper P value P for interaction
#> 1 kk <NA> <NA> <NA> <NA> <NA> NA 0.478
#> 2 0 38 16.9 7 0.7 70.4 0.107 <NA>
#> 3 1 187 83.1 2.94 1.55 5.58 0.001 <NA>