Simulation of two-stage recurrent events data based on Cox/Cox or Cox/Ghosh-Lin structure

Usage

sim.recurrent(cox1,coxd=NULL,coxc=NULL,n=1,data=NULL,
type=c("default","cox-cox","gl-cox"),id="id",
varz=1,share=1,cens=0.001,scale1=1,scaled=1,dependence=NULL,...)

Arguments

cox1: cox/ghosh-lin for recurrent events
coxd: cox for terminal event (phreg)
coxc: possible cox for censoring (phreg)
n: number of id's
data: on which the models are fitted (to draw covariates)
type: to specify type of simulation, if not default
id: name of id variable
varz: dependence frailty
share: to fit patly shared random effects model
cens: censoring rate for exponential censoring
scale1: to scale baseline of recurrent events model
scaled: to scale baseline of terminal event
dependence: if dependence different from NULL, then uses simRecurrentList based on models given
...: Additional arguments to simGLcox, nmin, nmax regulates linear approximation grid

Details

Must specify two phreg objects, or a phreg and a recreg object, then simulates data from two-stage model

References

Scheike (2024), Twostage recurrent events models, under review.

Author

Thomas Scheike

Examples

data(hfactioncpx12)
hf <- hfactioncpx12
hf$x <- as.numeric(hf$treatment)
n <- 100
xr <- phreg(Surv(entry,time,status==1)~x+cluster(id),data=hf)
dr <- phreg(Surv(entry,time,status==2)~x+cluster(id),data=hf)
simcoxcox <- sim.recurrent(xr,dr,n=n,data=hf)
recGL <- recreg(Event(entry,time,status)~x+cluster(id),hf,death.code=2)
simglcox <- sim.recurrent(recGL,dr,n=n,data=hf)