Summary for dependence models for competing risks

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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

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## 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

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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)

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