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

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

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

coxc

possible cox for censrong

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)