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TableSubgroupMultiGLM: Multiple sub-group analysis table for GLM.

Source: R/forestglm.R
TableSubgroupMultiGLM.Rd

Multiple sub-group analysis table for GLM.

Usage

TableSubgroupMultiGLM(
  formula,
  var_subgroups = NULL,
  var_cov = NULL,
  data,
  family = "binomial",
  decimal.estimate = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  line = F,
  labeldata = NULL,
  count_by = NULL,
  event = FALSE
)

Arguments

formula

formula with survival analysis.

var_subgroups

Multiple sub-group variables 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

line

Include new-line between sub-group variables, Default: F

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.

Value

Multiple sub-group analysis table.

Details

This result is used to make forestplot.

See also

map bind

Examples

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
TableSubgroupMultiGLM(status ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = lung, line = TRUE, 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>
#> 1        <NA>  <NA>    <NA>         <NA>  <NA>  <NA>    <NA>              <NA>
#> 2          kk  <NA>    <NA>         <NA>  <NA>  <NA>    <NA>             0.476
#> 3           0    38    16.9            7   0.7 70.03   0.098              <NA>
#> 4           1   187    83.1         2.94  1.55  5.57   0.001              <NA>
#> 5        <NA>  <NA>    <NA>         <NA>  <NA>  <NA>    <NA>              <NA>
#> 6         kk1  <NA>    <NA>         <NA>  <NA>  <NA>    <NA>             0.984
#> 7           0     8     3.6 314366015.19     0   Inf   0.997              <NA>
#> 8           1   217    96.4         2.85  1.55  5.25   0.001              <NA>

## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
#> Warning: No weights or probabilities supplied, assuming equal probability
TableSubgroupMultiGLM(status ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = data.design, family = "binomial"
)
#>      Variable Count Percent           OR       Lower         Upper P value
#> sex2  Overall   228     100         3.01        1.65          5.48  <0.001
#> 1          kk  <NA>    <NA>         <NA>        <NA>          <NA>    <NA>
#> 2           0    38    16.9            7         0.7          70.4   0.107
#> 3           1   187    83.1         2.94        1.55          5.58   0.001
#> 4         kk1  <NA>    <NA>         <NA>        <NA>          <NA>    <NA>
#> 5           0     8     3.6 314366015.19 35102996.37 2815314979.23  <0.001
#> 6           1   217    96.4         2.85        1.55          5.25   0.001
#>      P for interaction
#> sex2              <NA>
#> 1                0.478
#> 2                 <NA>
#> 3                 <NA>
#> 4               <0.001
#> 5                 <NA>
#> 6                 <NA>