frailtySurv (version 1.3.8)

summary.fitfrail: Summary of the survival curve

Description

Returns a data.frame summarizing the survival curve of the fitted model. If specified, this function uses a weighted bootstrap procedure to calculate SE of the survival curve estimates. Subsequente calls with the same arguments will use the cached SE and avoid performing the weighted bootstrap again.

Usage

# S3 method for fitfrail
summary(object, type = "survival", Lambda.times = NULL, 
                           censored = FALSE, se = FALSE, CI = 0.95, ...)

Value

A data.frame summarizing the survival curve with the following columns.

time

the time points

surv/cumhaz

survival/cumulative hazard estimate at time t+

n.risk

number of subjects at risk at time t-

n.event

the number of failures that occured from the last time point to time t+

std.err

the SE of the survival estimate

lower.ci

lower bound on the specified confidence interval

upper.ci

upper bound on the specified confidence interval

Arguments

object

a fitfrail object

type

string indicating the type of summary: either "survival" for a summary of the survival curve, or "cumhaz" for a summary of the cumulative baseline hazard.

Lambda.times

vector of times where the curve should be evaluated. The resulting data.frame will have 1 row for each time. If NULL and censored=TRUE, all observed times are used by default. If NULL and censored=FALSE, only the failure times are including in the results.

censored

logical value, whether the survival curve should contain the censored times. Ignored if Lambda.times is not NULL.

se

logical value, whether the survival SE should be included with the results. If se=TRUE, a weighted bootstrap procedure is used to determine estimated survival SE.

CI

numeric, the confidence interval to evaluate upper and lower limits for the survival estimate at each time point

...

extra arguments will be passed to vcov.fitfrail

See Also

fitfrail, vcov.fitfrail

Examples

if (FALSE) {
dat <- genfrail(N=200, K=2, beta=c(log(2),log(3)), 
                frailty="gamma", theta=2,
                censor.rate=0.35,
                Lambda_0=function(t, tau=4.6, C=0.01) (C*t)^tau)

fit <- fitfrail(Surv(time, status) ~ Z1 + Z2 + cluster(family), 
                dat, frailty="gamma")

surv <- summary(fitfrail, B=50, se=TRUE, CI=0.95)
head(surv)
}