% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/recurrent.marginal.R
\name{recurrentMarginal}
\alias{recurrentMarginal}
\alias{tie.breaker}
\alias{recmarg}
\alias{recurrentMarginalIPCW}
\alias{recurrentMarginalAIPCW}
\alias{recurrentMarginalAIPCWdata}
\title{Fast recurrent marginal mean when death is possible}
\usage{
recurrentMarginal(recurrent, death, fixbeta = NULL, km = TRUE, ...)
}
\arguments{
\item{recurrent}{phreg object with recurrent events}

\item{death}{phreg object with deaths}

\item{fixbeta}{to force the estimation of standard errors to think of regression coefficients as known/fixed}

\item{km}{if true then uses Kaplan-Meier for death, otherwise exp(- Nelson-Aalen )}

\item{...}{Additional arguments to lower level funtions}
}
\description{
Fast Marginal means of recurrent events. Using the Lin and Ghosh (2000) 
standard errors.  
Fitting two models for death and recurent events these are
combined to prducte the estimator 
\deqn{ \int_0^t  S(u|x=0) dR(u|x=0) } the mean number of recurrent events, here
\deqn{ S(u|x=0) }  is the probability of survival for the baseline group, and 
\deqn{ dR(u|x=0) }  is the hazard rate of an event among survivors for the baseline. 
Here \deqn{ S(u|x=0) }  is estimated by \deqn{ exp(-\Lambda_d(u|x=0) }  with 
 \deqn{\Lambda_d(u|x=0) } being the cumulative baseline for death.
}
\details{
Assumes no ties in the sense that jump times needs to be unique, this is particularly so for the stratified version.
}
\examples{

data(base1cumhaz)
data(base4cumhaz)
data(drcumhaz)
dr <- drcumhaz
base1 <- base1cumhaz
base4 <- base4cumhaz
rr <- simRecurrent(1000,base1,death.cumhaz=dr)
rr$x <- rnorm(nrow(rr)) 
rr$strata <- floor((rr$id-0.01)/500)

##  to fit non-parametric models with just a baseline 
xr <- phreg(Surv(entry,time,status)~cluster(id),data=rr)
dr <- phreg(Surv(entry,time,death)~cluster(id),data=rr)
par(mfrow=c(1,3))
bplot(dr,se=TRUE)
title(main="death")
bplot(xr,se=TRUE)
### robust standard errors 
rxr <-   robust.phreg(xr,fixbeta=1)
bplot(rxr,se=TRUE,robust=TRUE,add=TRUE,col=4)

## marginal mean of expected number of recurrent events 
out <- recurrentMarginal(xr,dr)
bplot(out,se=TRUE,ylab="marginal mean",col=2)

########################################################################
###   with strata     ##################################################
########################################################################
xr <- phreg(Surv(entry,time,status)~strata(strata)+cluster(id),data=rr)
dr <- phreg(Surv(entry,time,death)~strata(strata)+cluster(id),data=rr)
par(mfrow=c(1,3))
bplot(dr,se=TRUE)
title(main="death")
bplot(xr,se=TRUE)
rxr <-   robust.phreg(xr,fixbeta=1)
bplot(rxr,se=TRUE,robust=TRUE,add=TRUE,col=1:2)

out <- recurrentMarginal(xr,dr)
bplot(out,se=TRUE,ylab="marginal mean",col=1:2)

########################################################################
###   cox case        ##################################################
########################################################################
xr <- phreg(Surv(entry,time,status)~x+cluster(id),data=rr)
dr <- phreg(Surv(entry,time,death)~x+cluster(id),data=rr)
par(mfrow=c(1,3))
bplot(dr,se=TRUE)
title(main="death")
bplot(xr,se=TRUE)
rxr <-   robust.phreg(xr)
bplot(rxr,se=TRUE,robust=TRUE,add=TRUE,col=1:2)

out <- recurrentMarginal(xr,dr)
bplot(out,se=TRUE,ylab="marginal mean",col=1:2)

########################################################################
###   CIF  #############################################################
########################################################################
### use of function to compute cumulative incidence (cif) with robust standard errors
 data(bmt)
 bmt$id <- 1:nrow(bmt)
 xr  <- phreg(Surv(time,cause==1)~cluster(id),data=bmt)
 dr  <- phreg(Surv(time,cause!=0)~cluster(id),data=bmt)

 out <- recurrentMarginal(xr,dr,km=TRUE)
 bplot(out,se=TRUE,ylab="cumulative incidence")

}
\references{
Ghosh and Lin (2002) Nonparametric Analysis of Recurrent events and death, 
            Biometrics, 554--562.
}
\author{
Thomas Scheike
}
