[R] Hosmer-Lemeshow test for Cox model

[Frank Harrell]

Any method that requires binning is problematic.  Instead, take a look at the
calibrate function in the rms package.  There is a new option for continuous
calibration curves for survival models. 
Frank

jane.wong wrote
> 
> Dear list,
> 
> Usually we use Hosmer-Lemeshow test to test the goodness of fit for
> logistic model, but if I use it to test for Cox model, how can I get the
> observed probability for each group?
> Suppose I calculated the 5-year predicted probability using Cox model,
> then I split the dataset into 10 group according to this predicted
> probability. We should compare the observed probability with predicted
> probability within each group,but how to calculate this observed
> probability, should I use Kaplan-Meier to estimate it?  how should I
> modify the following program,thanks.
> 
> hosmerlem = function(y, yhat, g=10) {
>   cutyhat = cut(yhat,
>      breaks = quantile(yhat, probs=seq(0,
>        1, 1/g)), include.lowest=TRUE)
>   obs = xtabs(cbind(1 - y, y) ~ cutyhat)
>   expect = xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
>   chisq = sum((obs - expect)^2/expect)
>   P = 1 - pchisq(chisq, g - 2)
>   return(list(chisq=chisq,p.value=P))
> }
> 


-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
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