From: Adaikalavan Ramasamy <ramasamy_at_stats.ox.ac.uk>

Date: Sat 03 Sep 2005 - 22:41:28 EST

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 Received on Sat Sep 03 22:04:52 2005

Date: Sat 03 Sep 2005 - 22:41:28 EST

Thank you ! So to be absolutely sure, the C-index in my case is 0.5 * ( 0.3634 + 1 ) = 0.6817 right ?

If the above calculation is correct then why do I get the following :

rcorr.cens( predict(fit), Surv( GBSG$rfst, GBSG$cens ) )[ "C Index" ]
C Index

0.3115156

( I am aware that is a re-substitution error rate and optimistic, but this is what led me to believe that my C-index was < 0.5 ).

Can I suggest that it is probably worth adding a sentence about the relationship between C-index and Dxy in validate.cph or elsewhere if this is not a widely known issue.

Thank you again.

Regards, Adai

On Fri, 2005-09-02 at 19:55 -0400, Frank E Harrell Jr wrote:

> Adaikalavan Ramasamy wrote:

*> > I am doing some coxPH model fitting and would like to have some idea
**> > about how good the fits are. Someone suggested to use Frank Harrell's
**> > C-index measure.
**> >
**> > As I understand it, a C-index > 0.5 indicates a useful model. I am
**>
**> No, that just means predictions are better than random.
**>
**> > probably making an error here because I am getting values less than 0.5
**> > on real datasets. Can someone tell me where I am going wrong please ?
**> >
**> > Here is an example using the German Breast Study Group data available in
**> > the mfp package. The predictors in the model were selected by stepAIC().
**> >
**> >
**> > library(Design); library(Hmisc); library(mfp); data(GBSG)
**> > fit <- cph( Surv( rfst, cens ) ~ htreat + tumsize + tumgrad +
**> > posnodal + prm, data=GBSG, x=T, y=T )
**> >
**> > val <- validate.cph( fit, dxy=T, B=200 )
**> > round(val, 3)
**> > index.orig training test optimism index.corrected n
**> > Dxy -0.377 -0.383 -0.370 -0.013 -0.364 200
**> > R2 0.140 0.148 0.132 0.016 0.124 200
**> > Slope 1.000 1.000 0.925 0.075 0.925 200
**> > D 0.028 0.030 0.027 0.004 0.025 200
**> > U -0.001 -0.001 0.002 -0.002 0.002 200
**> > Q 0.029 0.031 0.025 0.006 0.023 200
**> >
**> > 1) Am I correct in assuming C-index = 0.5 * ( Dxy + 1 ) ?
**>
**> Yes
**>
**> >
**> > 2) If so, I am getting 0.5*(-0.3634+1) = 0.318 for the C-index. Does
**> > this make sense ?
**>
**> For the Cox model, the default calculation correlates the linear
**> predictor with survival time. A large linear predictor (large log
**> hazard) means shorter survival time. To phrase it in the more usually
**> way, negate Dxy before computing C.
**>
**> Frank
**>
**> >
**> > 3) Should I be using some other measurement instead of C-index.
**> >
**> > Thank you very much in advance.
**> >
**> > Regards, Adai
**> >
**> > ______________________________________________
**> > 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
**> >
**>
*

>

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 Received on Sat Sep 03 22:04:52 2005

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