casewise | R Documentation |
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casewise(conc, marg, cause.marg)
conc |
Concordance |
marg |
Marginal estimate |
cause.marg |
specififes which cause that should be used for marginal cif based on prodlim |
Thomas Scheike
## Reduce Ex.Timings library(prodlim) data(prt); prt <- force.same.cens(prt,cause="status") ### 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)
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