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
> >
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> >
>
>
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