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I ran a Cox-Regression on Prepayment analysis. Right now, I'm using a stratified Cox-Regression with time dependent variables. The focus of my work lays NOT on prediction but rather on differentiating among risk groups.

The problem is that my p-values are extreme low, while my (pseudo) R-square is extremely low, around 0.001 (max 0.225) . Concordance is around 0.50

What do these results tell me? I heard different comments on the R-Square measure. Some say that it is complete meaningless since it does not handle censored data correctly (same for concordance)

Can I simply ignore this measures? Or is there one way to push R-Square?

IWS
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Kosta S.
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  • For an extendend discussion, one should read the post from Ben Kuhn at in: https://stats.stackexchange.com/questions/116540/how-to-evaluate-the-goodness-of-fit-for-survial-functions He proposes a "Harrrels C" index, tough I'm not quite sure if this is actually practically appliable. – Kosta S. Oct 17 '17 at 14:01

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