recurrent.marginal.coxmean | R Documentation |

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.

recurrent.marginal.coxmean(recurrent, death)

`recurrent` |
aalen model for recurrent events |

`death` |
cox.aalen (cox) model for death events |

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.

Thomas Scheike

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

### 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))

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