boot_lognormal {EquiSurv}

Parametric Bootstrap of time-to-event data following a lognormal distribution

Description

Function generating bootstrap data according to a lognormal distribution (specified by a model parameter \theta), assuming exponentially distributed right-censoring (specified by a rate C). After data generation again a model is fitted and evaluated at a pre-specified time point t_0 yielding the response vector.

Usage

boot_lognormal(t0, B = 1000, theta, C, N)

Arguments

t0

time point of interest

B

number of bootstrap repetitions. The default is B=1000

theta

parameter of the lognormal distribution, theta=(meanlog,sdlog)

C

rate of the exponential distribution specifiying the censoring

N

size of the dataset = number of observations

Value

A vector of length B containing the estimated survival at t0

Examples

t0<-2
N<-30
C<-1
boot_lognormal(t0=t0,theta=c(0.6,1),C=C,N=N)