rm_survdiff {reportRmd}

Display event counts, expected event counts and logrank test of differences

Description

This is a wrapper function around the survdiff function to display overall event rates and group-specific rates along with the log-rank test of a difference in survival between groups in a single table suitable for markdown output. Median survival times are included by default but can be removed setting median=FALSE

Usage

rm_survdiff(
  data,
  time,
  status,
  covs,
  strata,
  includeVarNames = FALSE,
  digits = 1,
  showCols = c("N", "Observed", "Expected"),
  CIwidth = 0.95,
  conf.type = "log",
  caption = NULL,
  tableOnly = FALSE,
  fontsize
)

Arguments

data

data frame containing survival data

time

string indicating survival time variable

status

string indicating event status variable

covs

character vector indicating variables to group observations by

strata

string indicating the variable to stratify observations by

includeVarNames

boolean indicating if the variable names should be included in the output table, default is FALSE

digits

the number of digits in the survival rate

showCols

character vector indicating which of the optional columns to display, defaults to c('N','Observed','Expected')

CIwidth

width of the median survival estimates, default is 95%

conf.type

type of confidence interval see survfit for details. Default is 'log'.

caption

table caption

tableOnly

should a dataframe or a formatted object be returned

fontsize

PDF/HTML output only, manually set the table fontsize

Value

A character vector of the survival table source code, unless tableOnly=TRUE in which case a data frame is returned

See Also

survdiff

Examples

#' # Differences between sex
data("pembrolizumab")
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs='sex',digits=1)

# Differences between sex, stratified by cohort
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs='sex',strata='cohort',digits=1)
# Differences between sex/cohort groups
rm_survdiff(data=pembrolizumab,time='os_time',status='os_status',
covs=c('sex','cohort'),digits=1)