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.cens gives

> 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

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Frank Harrell

Department of Biostatistics, Vanderbilt University