TableSubgroupMultiCox: Multiple sub-group analysis table for Cox/svycox model.

Multiple sub-group analysis table for Cox/svycox model.

TableSubgroupMultiCox(
  formula,
  var_subgroups = NULL,
  var_cov = NULL,
  data,
  time_eventrate = 3 * 365,
  decimal.hr = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  line = F,
  cluster = NULL,
  strata = NULL,
  weights = NULL,
  event = FALSE,
  count_by = NULL,
  labeldata = NULL
)

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.
time_eventrate	Time for kaplan-meier based event rate calculation, Default = 365 * 3
decimal.hr	Decimal for hazard ratio, 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
cluster	Cluster variable for coxph, Default: NULL
strata	Strata variable for coxph, Default: NULL
weights	Weights variable for coxph, Default: NULL
event	Show number and rates of event in survival analysis default:F
count_by	Select variables to count by subgroup, Default: NULL
labeldata	Label info, made by `mk.lev` function, Default: NULL

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
TableSubgroupMultiCox(Surv(time, status) ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = lung, time_eventrate = 100, line = TRUE
)
#> Warning: Loglik converged before variable  1,2,3 ; coefficient may be infinite. 
#>     Variable Count Percent Point Estimate Lower Upper sex=1 sex=2 P value
#> sex  Overall   228     100           1.91  1.14   3.2     0   1.2   0.014
#> 1       <NA>  <NA>    <NA>           <NA>  <NA>  <NA>  <NA>  <NA>    <NA>
#> 2         kk  <NA>    <NA>           <NA>  <NA>  <NA>  <NA>  <NA>    <NA>
#> 3          0    38    16.9           2.88  0.31 26.49     0     0    0.35
#> 4          1   187    83.1           1.84  1.08  3.14     0   1.5   0.026
#> 5       <NA>  <NA>    <NA>           <NA>  <NA>  <NA>  <NA>  <NA>    <NA>
#> 6        kk1  <NA>    <NA>           <NA>  <NA>  <NA>  <NA>  <NA>    <NA>
#> 7          0     8     3.6           <NA>  <NA>  <NA>     0     0    <NA>
#> 8          1   217    96.4           1.88  1.12  3.17     0   1.3   0.018
#>     P for interaction
#> sex              <NA>
#> 1                <NA>
#> 2               0.525
#> 3                <NA>
#> 4                <NA>
#> 5                <NA>
#> 6               0.997
#> 7                <NA>
#> 8                <NA>

## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
#> Warning: No weights or probabilities supplied, assuming equal probability
TableSubgroupMultiCox(Surv(time, status) ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = data.design, time_eventrate = 100
)
#> Independent Sampling design (with replacement)
#> svydesign(id = ~1, data = lung)
#> Independent Sampling design (with replacement)
#> svydesign(id = ~1, data = lung)
#> New names:
#> Independent Sampling design (with replacement)
#> subset(data, get(var_subgroup) == .)
#> Independent Sampling design (with replacement)
#> subset(data, get(var_subgroup) == .)
#> Warning: Loglik converged before variable  1,2,3 ; coefficient may be infinite. 
#> Independent Sampling design (with replacement)
#> svydesign(id = ~1, data = lung)
#> Warning: Loglik converged before variable  1,2,3 ; coefficient may be infinite. 
#> New names:
#> Independent Sampling design (with replacement)
#> subset(data, get(var_subgroup) == .)
#>     Variable Count Percent Point Estimate Lower Upper sex=1 sex=2 P value
#> sex  Overall   228     100           1.91  1.14  3.19     0   1.2   0.013
#> 1         kk  <NA>    <NA>           <NA>  <NA>  <NA>  <NA>  <NA>    <NA>
#> 2          0    38    16.9           2.88  0.31  27.1     0     0   0.355
#> 3          1   187    83.1           1.84  1.08  3.11     0   2.9   0.024
#> 4        kk1  <NA>    <NA>           <NA>  <NA>  <NA>  <NA>  <NA>    <NA>
#> 5          0  <NA>    <NA>           <NA>  <NA>  <NA>     0     0    <NA>
#> 6          1   217    <NA>           1.88  1.12  3.15     0   1.3   0.017
#>     P for interaction
#> sex              <NA>
#> 1               0.523
#> 2                <NA>
#> 3                <NA>
#> 4              <0.001
#> 5                <NA>
#> 6                <NA>