phcpe2 {CPE} | R Documentation |
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)
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 |
Author(s)
Qianxing Mo, Mithat Gonen and Glenn Heller; qianxing.mo@moffitt.org
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
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)