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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