casewise: Estimates the casewise concordance based on Concordance and...

In mets: Analysis of Multivariate Event Times

Description

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Usage

casewise(conc, marg, cause.marg)

Arguments

conc	Concordance
marg	Marginal estimate
cause.marg	specififes which cause that should be used for marginal cif based on prodlim

Author(s)

Thomas Scheike

Examples

library(prodlim)
data(prt);

### marginal cumulative incidence of prostate cancer##'
outm <- prodlim(Hist(time,status)~+1,data=prt)

times <- 60:100
cifmz <- predict(outm,cause=2,time=times,newdata=data.frame(zyg="MZ")) ## cause is 2 (second cause)
cifdz <- predict(outm,cause=2,time=times,newdata=data.frame(zyg="DZ"))

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

cdz <- casewise(cdz,outm,cause.marg=2)
cmz <- casewise(cmz,outm,cause.marg=2)

plot(cmz,ci=NULL,ylim=c(0,0.5),xlim=c(60,100),legend=TRUE,col=c(3,2,1))
par(new=TRUE)
plot(cdz,ci=NULL,ylim=c(0,0.5),xlim=c(60,100),legend=TRUE)
summary(cdz)
summary(cmz)

mets documentation built on May 2, 2019, 4:43 p.m.