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

phcpe(coxfit, CPE.SE=FALSE,out.ties=FALSE)

Arguments

Value

Author(s)

Qianxing Mo, Mithat Gonen and Glenn Heller; qmo@bcm.edu

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)

### Calculate CPE only (needs much less time).
phcpe(coxph.fit)
phcpe(coxph.fit,out.ties=TRUE)
#result is identical because the covariates are not tied #

### Calculate CPE and CPE.SE
phcpe(coxph.fit, CPE.SE=TRUE)
phcpe(coxph.fit, CPE.SE=TRUE,out.ties=TRUE)

#*** 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).
phcpe(cph.fit)
phcpe(cph.fit,out.ties=TRUE)

### Calculate CPE and CPE.SE
phcpe(cph.fit, CPE.SE=TRUE)
phcpe(cph.fit, CPE.SE=TRUE,out.ties=TRUE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(CPE)
Loading required package: survival
Loading required package: rms
Loading required package: Hmisc
Loading required package: lattice
Loading required package: Formula
Loading required package: ggplot2

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/CPE/phcpe.Rd_%03d_medium.png", width=480, height=480)
> ### Name: phcpe
> ### Title: Gonen & Heller Concordance Probability Estimate for the Cox
> ###   Proportional Hazards model
> ### Aliases: phcpe
> ### Keywords: survival
> 
> ### ** 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)
> 
> ### Calculate CPE only (needs much less time).
> phcpe(coxph.fit)
$CPE
[1] 0.5113818

> phcpe(coxph.fit,out.ties=TRUE)
$CPE
[1] 0.5113818

> #result is identical because the covariates are not tied #
> 
> ### Calculate CPE and CPE.SE
> phcpe(coxph.fit, CPE.SE=TRUE)
$CPE
[1] 0.5113818

$CPE.SE
[1] 0.0124193

> phcpe(coxph.fit, CPE.SE=TRUE,out.ties=TRUE)
$CPE
[1] 0.5113818

$CPE.SE
[1] 0.0124193

> 
> #*** 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).
> phcpe(cph.fit)
$CPE
[1] 0.5113818

> phcpe(cph.fit,out.ties=TRUE)
$CPE
[1] 0.5113818

> 
> ### Calculate CPE and CPE.SE
> phcpe(cph.fit, CPE.SE=TRUE)
$CPE
[1] 0.5113818

$CPE.SE
[1] 0.0124193

> phcpe(cph.fit, CPE.SE=TRUE,out.ties=TRUE)
$CPE
[1] 0.5113818

$CPE.SE
[1] 0.0124193

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>