Thank you Frank for your prompt reply

You're definitely right, it seems that comparing rank concordance is a quite
inefficient way to test the predictive power of a covariable. Thus LR test
works better.

Stefano


On 4/21/06, Frank E Harrell Jr <f.harrell@vanderbilt.edu> wrote:
>
> Stefano Mazzuco wrote:
> > Hi R-users,
> >
> > I'm having some problems in using the Hmisc package.
> >
> > I'm estimating a cox ph model and want to test whether the drop in
> > concordance index due to omitting one covariate is significant. I think
> (but
> > I'm not sure) here are two ways to do that:
> >
> > 1) predict two cox model (the full model and model without the covariate
> of
> > interest) and estimate the concordance index (i.e. area under the ROC
> curve)
> > with rcorr.cens for both models, then compute the difference
> >
> > 2) predict the two cox models and estimate directly the difference
> between
> > the two c-indices using rcorrp.cens. But it seems that the rcorrp.censgives
> > me the drop of Dxy index.
> >
> > Do you have any hint?
> >
> > Thanks
> > Stefano
>
> First of all, any method based on comparing rank concordances loses
> powers and is discouraged.  Likelihood ratio tests (e.g., by embedding a
> smaller model in a bigger one) are much more powerful.  If you must base
> comparisons on rank concordance (e.g., ROC area=C, Dxy) then rcorrp.cens
> can work if the sample size is large enough so that uncertainty about
> regression coefficient estimates may be ignored.  rcorrp.cens doesn't
> give the drop in C; it gives the probability that one model is "more
> concordant" with the outcome than another, among pairs of paired
> predictions.
>
> The bootcov function in the Design package has a new version that will
> output bootstrap replicates of C for a model, and its help file tells
> you how to use that to compare C for two models.  This should only be
> done to show how low a power such a procedure has.  rcporrp is likely to
> be more powerful than that, but likelihood ratio is what you want.  You
> will find many cases where one model increases C by only 0.02 but it has
> many more useful (more extreme) predictions.
>
> --
> Frank E Harrell Jr   Professor and Chair           School of Medicine
>                       Department of Biostatistics   Vanderbilt University
>

	[[alternative HTML version deleted]]

