% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/casewise.R
\name{casewise.test}
\alias{casewise.test}
\alias{slope.process}
\alias{casewise.bin}
\title{Estimates the casewise concordance based on Concordance and marginal estimate using timereg and performs test for independence}
\usage{
casewise.test(conc, marg, test = "no-test", p = 0.01)
}
\arguments{
\item{conc}{Concordance}

\item{marg}{Marginal estimate}

\item{test}{Type of test for independence assumption. "conc" makes test on concordance scale and "case" means a test on the casewise concordance}

\item{p}{check that marginal probability is greater at some point than p}
}
\description{
Estimates the casewise concordance based on Concordance and marginal estimate using timereg and performs test for independence
}
\details{
Uses cluster based conservative standard errors for marginal and sometimes only the uncertainty of the concordance estimates. This works prettey well, alternatively one can use also the 
         funcions Casewise for a specific time point
}
\examples{
\donttest{ ## Reduce Ex.Timings
library("timereg")
data("prt",package="mets");
prt <- force.same.cens(prt,cause="status")

prt <- prt[which(prt$id \%in\% sample(unique(prt$id),7500)),]
### marginal cumulative incidence of prostate cancer
times <- seq(60,100,by=2)
outm <- comp.risk(Event(time,status)~+1,data=prt,cause=2,times=times)

cifmz <- predict(outm,X=1,uniform=0,resample.iid=1)
cifdz <- predict(outm,X=1,uniform=0,resample.iid=1)

### concordance for MZ and DZ twins
cc <- bicomprisk(Event(time,status)~strata(zyg)+id(id),
                 data=prt,cause=c(2,2))
cdz <- cc$model$"DZ"
cmz <- cc$model$"MZ"

### To compute casewise cluster argument must be passed on,
###  here with a max of 100 to limit comp-time
outm <-comp.risk(Event(time,status)~+1,data=prt,
                 cause=2,times=times,max.clust=100)
cifmz <-predict(outm,X=1,uniform=0,resample.iid=1)
cc <-bicomprisk(Event(time,status)~strata(zyg)+id(id),data=prt,
                cause=c(2,2),se.clusters=outm$clusters)
cdz <- cc$model$"DZ"
cmz <- cc$model$"MZ"

cdz <- casewise.test(cdz,cifmz,test="case") ## test based on casewise
cmz <- casewise.test(cmz,cifmz,test="conc") ## based on concordance

plot(cmz,ylim=c(0,0.7),xlim=c(60,100))
par(new=TRUE)
plot(cdz,ylim=c(0,0.7),xlim=c(60,100))

slope.process(cdz$casewise[,1],cdz$casewise[,2],iid=cdz$casewise.iid)

slope.process(cmz$casewise[,1],cmz$casewise[,2],iid=cmz$casewise.iid)

}
}
\author{
Thomas Scheike
}
