Re: [R] C-index : typical values

From: Frank E Harrell Jr <f.harrell_at_vanderbilt.edu>
Date: Sun 04 Sep 2005 - 04:10:12 EST

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

correct

> 
> 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 ).

You're right about the optimism but that's not the cause in this case.

> 
> 
> 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.

Will do -Frank

> 
> 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
>>>
>>
>>
>
>
-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University

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Received on Sun Sep 04 04:20:36 2005

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