recurrent.marginal.coxmean: Estimates marginal mean of recurrent events based on two cox...

In timereg: Flexible Regression Models for Survival Data

Estimates marginal mean of recurrent events based on two cox models

Description

Fitting two Cox models for death and recurent events these are combined to prducte the estimator

the mean number of recurrent events, here

is the probability of survival, and

is the probability of an event among survivors. For now the estimator is based on the two-baselines so

, but covariates can be rescaled to look at different x's and extensions possible.

Usage

recurrent.marginal.coxmean(recurrent, death)

Arguments

recurrent	aalen model for recurrent events
death	cox.aalen (cox) model for death events

Details

IID versions along the lines of Ghosh & Lin (2000) variance. See also mets package for quick version of this for large data. IID versions used for Ghosh & Lin (2000) variance. See also mets package for quick version of this for large data mets:::recurrent.marginal, these two version should give the same when there are now ties.

Author(s)

Thomas Scheike

References

Ghosh and Lin (2002) Nonparametric Analysis of Recurrent events and death, Biometrics, 554–562.

Examples

### do not test because iid slow  and uses data from mets
library(mets)
data(base1cumhaz)
data(base4cumhaz)
data(drcumhaz)
dr <- drcumhaz
base1 <- base1cumhaz
base4 <- base4cumhaz
rr <- simRecurrent(100,base1,death.cumhaz=dr)
rr$x <- rnorm(nrow(rr)) 
rr$strata <- floor((rr$id-0.01)/50)
drename(rr) <- start+stop~entry+time

ar <- cox.aalen(Surv(start,stop,status)~+1+prop(x)+cluster(id),data=rr,
                   resample.iid=1,,max.clust=NULL,max.timepoint.sim=NULL)
ad <- cox.aalen(Surv(start,stop,death)~+1+prop(x)+cluster(id),data=rr,
                   resample.iid=1,,max.clust=NULL,max.timepoint.sim=NULL)
mm <- recurrent.marginal.coxmean(ar,ad)
with(mm,plot(times,mu,type="s"))
with(mm,lines(times,mu+1.96*se.mu,type="s",lty=2))
with(mm,lines(times,mu-1.96*se.mu,type="s",lty=2))

timereg documentation built on Jan. 17, 2023, 5:11 p.m.