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; 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)
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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)
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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/phcpe2.Rd_%03d_medium.png", width=480, height=480)
> ### Name: phcpe2
> ### Title: Gonen & Heller Concordance Probability Estimate for the Cox
> ### Proportional Hazards model
> ### Aliases: phcpe2
> ### 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)
>
> phcpe(coxph.fit,CPE.SE=TRUE)
$CPE
[1] 0.5113818
$CPE.SE
[1] 0.0124193
> phcpe2(coef=coxph.fit$coefficients,coef.var=coxph.fit$var,design=model.matrix(coxph.fit))
$CPE
[1] 0.5113818
>
> #*** For unknown reason, 'coxph.fit' may need to be removed before running cph()***
> rm(coxph.fit)
>
> cph.fit <- cph(Surv(time, status)~x1+x2+x3, data=survd,method="breslow")
>
> ### Calculate CPE only (needs much less time).
> phcpe2(cph.fit$coefficients,coef.var=cph.fit$var,design=model.matrix(cph.fit),CPE.SE=TRUE)
$CPE
[1] 0.5113818
$CPE.SE
[1] 0.0124193
>
>
>
>
>
>
> dev.off()
null device
1
>