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 <[EMAIL PROTECTED]> 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]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html