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 ______________________________________________ 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
