summary.fitfrail function

Summary of the survival curve

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.

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

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

Returns

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

Note

Similar to summary.survfit function in the survival package.

See Also

fitfrail, vcov.fitfrail

Examples

## Not run:

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)
## End(Not run)