rcorrp.cens

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rcorrp.cens

Stefano Mazzuco
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

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Re: rcorrp.cens

Frank Harrell
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
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Re: rcorrp.cens

Stefano Mazzuco
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 <[hidden email]> 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
>

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