I've created a few Cox regression models and I would like to see how well these models perform and I thought that perhaps a ROC-curve or a c-statistic might be useful similar to this articles use:

http://onlinelibrary.wiley.com/doi/10.1002/bjs.6930/abstract

Armitage used logistic regression but I wonder if it's possible to use a model from the survival package, the http://cran.r-project.org/web/packages/survivalROC/index.html gives a hint of this being possible but I can't figure out how to get that to work with a regular Cox regression.

I would be grateful if someone would show me how to do a ROC-analysis on this example:

```
library(survival)
data(veteran)
attach(veteran)
surv <- Surv(time, status)
fit <- coxph(surv ~ trt + age + prior, data=veteran)
summary(fit)
```

If possible I would appreciate both the raw c-statics output and a nice graph

Thanks!

Update
Thank you very much for answers. @Dwin: I would just like to be sure that I've understood it right before selecting your answer.

The calculation as I understand it according to DWin's suggestion:

```
library(survival)
library(rms)
data(veteran)
fit.cph <- cph(surv ~ trt + age + prior, data=veteran, x=TRUE, y=TRUE, surv=TRUE)
# Summary fails!?
#summary(fit.cph)
# Get the Dxy
v <- validate(fit.cph, dxy=TRUE, B=100)
# Is this the correct value?
Dxy = v[rownames(v)=="Dxy", colnames(v)=="index.corrected"]
# The c-statistic according to the Dxy=2(c-0.5)
Dxy/2+0.5
```

I'm unfamiliar with the validate function and bootstrapping but after looking at prof. Frank Harrel's answer http://r.789695.n4.nabble.com/Interpreting-the-example-given-by-Prof-Frank-Harrell-in-Design-validate-cph-tt3316820.html#a3324516 I figured that it's probably the way to get the Dxy. The help for validate states:

... Somers' Dxy rank correlation to be computed at each resample (this
takes a bit longer than the likelihood based statistics). The values
corresponting to the row Dxy are equal to 2 * (C - 0.5) where C is the
C-index or concordance probability.

I guess I'm mostly confused by the columns. I figured that the corrected value is the one I should use but I haven't really understood the validate output:

```
index.orig training test optimism index.corrected n
Dxy -0.0137 -0.0715 -0.0071 -0.0644 0.0507 100
R2 0.0079 0.0278 0.0037 0.0242 -0.0162 100
Slope 1.0000 1.0000 0.2939 0.7061 0.2939 100
...
```

In the http://r.789695.n4.nabble.com/Interpreting-the-example-given-by-Prof-Frank-Harrell-in-Design-validate-cph-tt3316820.html#a3324516 I've understood that I should have "surv=TRUE" in the cph if I have strata but I'm uncertain on what the purpose of the "u=60" parameter in the validate function is. I would be grateful if you could help me understand these and check that I haven't made any mistakes.