Computes concordance and casewise concordance for dependence models for competing risks models of the type cor.cif, rr.cif or or.cif for the given cumulative incidences and the different dependence measures in the object.
Usage
# S3 method for class 'cor'
summary(object, marg.cif = NULL, marg.cif2 = NULL, digits = 3, ...)Arguments
- object
object from cor.cif rr.cif or or.cif for dependence between competing risks data for two causes.
- marg.cif
a number that gives the cumulative incidence in one time point for which concordance and casewise concordance are computed.
- marg.cif2
the cumulative incidence for cause 2 for concordance and casewise concordance are computed. Default is that it is the same as marg.cif.
- digits
digits in output.
- ...
Additional arguments.
Value
prints summary for dependence model.
- casewise
gives casewise concordance that is, probability of cause 2 (related to cif2) given that cause 1 (related to cif1) has occured.
- concordance
gives concordance that is, probability of cause 2 (related to cif2) and cause 1 (related to cif1).
- cif1
cumulative incidence for cause1.
- cif2
cumulative incidence for cause1.
References
Cross odds ratio Modelling of dependence for Multivariate Competing Risks Data, Scheike and Sun (2012), Biostatistics.
A Semiparametric Random Effects Model for Multivariate Competing Risks Data, Scheike, Zhang, Sun, Jensen (2010), Biometrika.
Examples
## library("timereg")
## data("multcif",package="mets") # simulated data
## multcif$cause[multcif$cause==0] <- 2
##
## times=seq(0.1,3,by=0.1) # to speed up computations use only these time-points
## add <- timereg::comp.risk(Event(time,cause)~+1+cluster(id),
## data=multcif,n.sim=0,times=times,cause=1)
###
## out1<-cor.cif(add,data=multcif,cause1=1,cause2=1,theta=log(2+1))
## summary(out1)
##
## pad <- predict(add,X=1,se=0,uniform=0)
## summary(out1,marg.cif=pad)