cuminc_ci: Calculate cumulative incidence & confidence interval

In hild0146/hildenp: Convenience Functions

Description

This function can be used to calculated cumulative incidence estimates for survival data with competing risks. Confidence intervals can be specified for a given level.

Usage

cuminc_ci(ftime, fstatus, group, times = c(12, 24), print_event = 1,
  print_var = F, strata, rho = 0, cencode = 0, subset,
  na.action = na.omit, conf.level = 0.95, digits = 3)

Arguments

ftime	failure time variable
fstatus	variable with distinct codes for different causes of failure and also a distinct code for censored observations
group	estimates will be calculated within groups given by distinct values of this variable. Tests will compare these groups. If missing then treated as all one group (no test statistics)
times	timepoint(s) for which cumulative incidence estimates and confidence intervals are desired
print_event	identifies which event codes should be presented in the results. "all" can be used to request all event types
print_var	logical indicator for whether or not the variance should be listed in the results
strata	stratification variable. Has no effect on estimates. Tests will be stratified on this variable. (all data in 1 stratum, if missing)
rho	Power of the weight function used in the tests.
cencode	value of fstatus variable which indicates the failure time is censored.
subset	a logical vector specifying a subset of cases to include in the analysis
na.action	a function specifying the action to take for any cases missing any of ftime, fstatus, group, strata, or subset.
conf.level	the confidence level for confidence intervals. default is 0.95
digits	number of decimal places that the results should be rounded to

Value

returns a list containing

test	resulting test statistics and p-values
time1	cumulative incidence estimates and ci's for the resquested time
...	...
timeN	estimates for final time point

References

Scrucca L., Santucci A., Aversa F. (2007) Competing risks analysis using

Examples

df <-
  data.frame(
    fail_time = 12 * rexp(100),
    group = paste0('group_', sample(1:3, 100, replace = TRUE)) ,
    fail_status = sample(0:2, 100, replace = TRUE)
  )

cuminc_ci(
  ftime = df$fail_time,
  fstatus = df$fail_status,
  group = df$group,
  times = 12
)

hild0146/hildenp documentation built on May 17, 2019, 3:58 p.m.