summary.cor: Summary for dependence models for competing risks

In mets: Analysis of Multivariate Event Times

Description

Computes concordance and probandwise 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 probandwise concordance are computed.
marg.cif2	the cumulative incidence for cause 2 for concordance and probandwise 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.

probandwise	gives probandwise 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.

Author(s)

Thomas Scheike

References

Cross odds ratio Modelling of dependence for Multivariate Competing Risks Data, Scheike and Sun (2012), Biostatistics to appear.

A Semiparametric Random Effects Model for Multivariate Competing Risks Data, Scheike, Zhang, Sun, Jensen (2010), Biometrika.

Examples

data(multcif) # simulated data
multcif$cause[multcif$cause==0] <- 2

times=seq(0.05,3,by=0.1) # to speed up computations use only these time-points
add<-comp.risk(Event(time,cause)~const(X)+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,Z=0,se=0,uniform=0)$P1
summary(out1,marg.cif=pad)

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