CPE (version 1.6.3)

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

Description

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

Usage

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

Value

CPE

Concordance Probability Estimate

CPE.SE

the Standard Error of the Concordance Probability Estimate

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.

Author

Qianxing Mo, Mithat Gonen and Glenn Heller;

References

Mithat Gonen and Glenn Heller. (2005). Concordance probability and discriminatory power in proportional hazards regression. Biometrika, 92, 4, pp.965-970 Glenn Heller and Qianxing Mo. (2016). Estimating the concordance probability in a survival analysis with a discrete number of risk groups. Lifetime Data Analysis, 22(2):263-79.

See Also

phcpe

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)