TableSubgroupMultiGLM: Multiple sub-group analysis table for GLM.

Multiple sub-group analysis table for GLM.

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

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"
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

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")
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: collapsing to unique 'x' values
#> Waiting for profiling to be done...
#>          Variable Count Percent           OR Lower  Upper P value
#> sex2      Overall   228     100         3.01  1.66   5.52  <0.001
#> 1...1        <NA>  <NA>    <NA>         <NA>  <NA>   <NA>    <NA>
#> 11...2         kk  <NA>    <NA>         <NA>  <NA>   <NA>    <NA>
#> init...3        0    38    16.9            7  0.91 145.62   0.098
#> ...4            1   187    83.1         2.94  1.56   5.62   0.001
#> 1...5        <NA>  <NA>    <NA>         <NA>  <NA>   <NA>    <NA>
#> 11...6        kk1  <NA>    <NA>         <NA>  <NA>   <NA>    <NA>
#> init...7        0     8     3.6 314366015.19     0   <NA>   0.997
#> ...8            1   217    96.4         2.85  1.56   5.29   0.001
#>          P for interaction
#> sex2                  <NA>
#> 1...1                 <NA>
#> 11...2               0.476
#> init...3              <NA>
#> ...4                  <NA>
#> 1...5                 <NA>
#> 11...6               0.984
#> init...7              <NA>
#> ...8                  <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          5.5  <0.001
#> 1...1          kk  <NA>    <NA>         <NA>        <NA>         <NA>    <NA>
#> init...2        0    38    16.9            7        0.64        76.28   0.107
#> ...3            1   187    83.1         2.94        1.54          5.6   0.001
#> 1...4         kk1  <NA>    <NA>         <NA>        <NA>         <NA>    <NA>
#> init...5        0     8     3.6 314366015.19 20361023.24 4853684923.4  <0.001
#> ...6            1   217    96.4         2.85        1.55         5.27   0.001
#>          P for interaction
#> sex2                  <NA>
#> 1...1                0.478
#> init...2              <NA>
#> ...3                  <NA>
#> 1...4               <0.001
#> init...5              <NA>
#> ...6                  <NA>