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
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 ) ?
2) If so, I am getting 0.5*(-0.3634+1) = 0.318 for the C-index. Does
this make sense ?
3) Should I be using some other measurement instead of C-index.
Thank you very much in advance.
Regards, Adai
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