1d:T4c5,# Estimates the casewise concordance based on Concordance and marginal estimate using prodlim but no testing .. content for description (no empty lines) .. ```r 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 ## Examples ```r ## 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) ``` ## Author(s) Thomas Scheike1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.92132 10.9874 2.44599 10.7644 2.44599 10.7644C1.78773 10.3149 2.49584 10.3238 2.49584 10.3238C3.22353 10.375 3.60629 11.0711 3.60629 11.0711C4.25298 12.1788 5.30335 11.8588 5.71638 11.6732C5.78225 11.205 5.96962 10.8854 6.17658 10.7043C4.56675 10.5209 2.87415 9.89918 2.87415 7.12104C2.87415 6.32925 3.15677 5.68257 3.62053 5.17563C3.54576 4.99226 3.29697 4.25521 3.69174 3.25691C3.69174 3.25691 4.30015 3.06196 5.68522 3.99973C6.26337 3.83906 6.8838 3.75895 7.50022 3.75583C8.1162 3.75895 8.73619 3.83906 9.31523 3.99973C10.6994 3.06196 11.3069 3.25691 11.3069 3.25691C11.7

casewise function