survfit_confint: Confidence intervals for survival curves

In survival: Survival Analysis

Confidence intervals for survival curves

Description

Compute the confidence intervals for a survfit object, using one of several approximations.

Usage

survfit_confint(p, se, logse = TRUE, conf.type, conf.int = 0.95, selow, ulimit = TRUE)

Arguments

p	the estimate, i.e., the surv, pstate or cumhaz component of a survfit object
se	vector or matrix of standard errors
logse	if TRUE, the provided se component is the std of log(p)
conf.type	one of "plain", "log", "log-log", "logit", or "arcsin"
conf.int	the confidence level, a value strictly between 0 and 1
selow	alternate standard error for the lower limit
ulimit	if TRUE, constrain the result to be between 0 and 1

Details

This routine is used internally to create confidence intervals and would rarely be called by a user (but perhaps by another package). As such it does very little checking of the input arguments.

"Plain" intervals of p +- 1.96*se have been shown by many authors to perform poorly. Which of the other choices is "best" depends on the details of the simulation study used to evaluate the method, all do quite well overall.

For log intervals, the CI at p=0 is exp(log(0) + se) which becomes NA in R, and likewise for p= 0 or 1 when using log-log or logit. If the se is 0, however, the routine returns p as the upper and lower limit. (One side effect of the NA is that the plotted se bands for a Kaplan-Meier whose final time point is a death will stop at the death time and not plunge to 0; i.e., not have the final stair step, a graphical aspect we prefer.)

Value

a list with components lower and upper.

See Also

survfit.object

survival documentation built on Jan. 16, 2026, 5:07 p.m.