[R] How to calculate the Deviance for test data based on Cox model

[Thomas Lumley]

On Thu, Mar 29, 2012 at 11:03 PM, ChiangKevin
<kevinchiang865 at hotmail.com> wrote:
>
>
> Dear List,
>
> If I got a Cox model based on training set, then how should I calculate the Cox log partial likelihood for the test data?
> Actually I am trying to calculate the deviance on test dataset to evaluate the performance of prediction model, the equation is as follows: D = -2{L(test)[beta_train] - L(test)[0]}. It means using the beta coefficients got from training set to calculate the likelihood of test data. I know I can got log likelihood for training model, but how to get it for test data?

One way to do it is to get the linear predictors for the test set  (eg
with predict.coxph) and use them as an offset
   coxph(Surv(time,status)~offset(lp), data=testdata)

The loglikelihood reported will be the log partial likelihood
evaluated at the test data and the fitted parameter values

-- 
Thomas Lumley
Professor of Biostatistics
University of Auckland