TableSubgroupGLM: Sub-group analysis table for GLM.

Sub-group analysis table for GLM.

TableSubgroupGLM(
  formula,
  var_subgroup = NULL,
  var_cov = NULL,
  data,
  family = "binomial",
  decimal.estimate = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  labeldata = NULL
)

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

Value

Sub-group analysis table.

Details

This result is used to make forestplot.

See also

safely,map,map2 glm svyglm

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>