timereg (version 2.0.6)

recurrent.marginal.mean: Estimates marginal mean of recurrent events

Description

Fitting two aalen models for death and recurent events these are combined to prducte the estimator $${\int }_0^{t}S(u)dR(u)$$ the mean number of recurrent events, here $$S(u)$$ is the probability of survival, and $$dR(u)$$ is the probability of an event among survivors.

Usage

recurrent.marginal.mean(recurrent, death)

Arguments

recurrent

aalen model for recurrent events

death

aalen model for recurrent events

Author

Thomas Scheike

Details

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 no ties.

References

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

Examples

# \donttest{
### get some data using mets simulaitons, and avoid dependency, see 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 <- aalen(Surv(start,stop,status)~+1+cluster(id),data=rr,resample.iid=1
#                                                      ,max.clust=NULL)
# ad <- aalen(Surv(start,stop,death)~+1+cluster(id),data=rr,resample.iid=1,
#                                                      ,max.clust=NULL)
# mm <- recurrent.marginal.mean(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))
# }