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 ______________________________________________ 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