CPE (version 1.4.2)

phcpe2: Gonen & Heller Concordance Probability Estimate for the Cox Proportional Hazards model

Description

A function to calculate Gonen & Heller concordance probability estimate (CPE) for the Cox proportional hazards model.

Usage

phcpe2(coef,coef.var,design, CPE.SE=FALSE,out.ties=FALSE)

Arguments

coef

The coefficients of the Cox model.

coef.var

The covariance matrix of the coefficients of the Cox model.

design

A design matrix for covariates. The rows correspond to subjects, and the columns correspond to covariates.

CPE.SE

A logical value indicating whether the standard error of the CPE should be calculated

out.ties

If out.ties is set to FALSE,pairs of observations tied on covariates will be used to calculate the CPE. Otherwise, they will not be used.

Value

• CPE
  Concordance Probability Estimate
• CPE.SE
  the Standard Error of the Concordance Probability Estimate

References

Mithat Gonen and Glenn Heller. (2005). Concordance probability and discriminatory power in proportional hazards regression. Biometrika, 92, 4, pp.965-970

Examples

### create a simple data set for testing
set.seed(199)
nn <- 1000
time <- rexp(nn)
status <- sample(0:1, nn, replace=TRUE)
covar <- matrix(rnorm(3*nn), ncol=3)
survd <- data.frame(time, status, covar)
names(survd) <- c("time","status","x1","x2","x3")

coxph.fit <- coxph(Surv(time,status)~x1+x2+x3,data=survd)

phcpe(coxph.fit,CPE.SE=TRUE)
phcpe2(coef=coxph.fit$coefficients,coef.var=coxph.fit$var,design=model.matrix(coxph.fit))

#*** For unknown reason, 'coxph.fit' may need to be removed before running cph()***
rm(coxph.fit)

cph.fit <- cph(Surv(time, status)~x1+x2+x3, data=survd,method="breslow")

### Calculate CPE only (needs much less time).
phcpe2(cph.fit$coefficients,coef.var=cph.fit$var,design=model.matrix(cph.fit),CPE.SE=TRUE)