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st: R: how evaluate the accuracy of parametric survival models in a resampling process?


From   "Carlo Lazzaro" <carlo.lazzaro@tin.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: R: how evaluate the accuracy of parametric survival models in a resampling process?
Date   Tue, 19 Apr 2011 09:20:41 +0200

Albert wrote: 

"Because we are working with 1000 resamples, graphical methods aren?t very
practical. We prefer not using a pseudo R2 (?A perfectly adequate model may
have what, at face value, seems like a terrible low R2 due to a high percent
of censored data? [Hosmer-Lemeshow])".

As far as censoring is concerned (and assuming you can handle it in your
simulations), you may want to divide the resamples in different categories
according to the probabilities of censoring (eg 25%; 40%), as reported by
Bang H, Tsiatis AA. Median Regression with Censored Cost Data. Biometrics
2002;58:643-649.

Kindeste Regards,
Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Albert Navarro
Inviato: lunedì 18 aprile 2011 22.06
A: statalist@hsphsun2.harvard.edu
Oggetto: st: how evaluate the accuracy of parametric survival models in a
resampling process?

I re-write this message. Last subject message was unclear... I'm sorry!

----------------

Dear all,

we are conducting a study to identify the associated distribution with the
generating process of a particular phenomenon (survival data). Briefly:

1. We fitted Weibull, log-normal and log-logistic models to 1000 resamples
(null models, without covariates)
2. We compared the AIC of the models in each resample. We selected the model
with lowest AIC in the higher number of resamples.
3. Estimated parameters: we selected the median of the estimations in the
1000 resamples, for the selected model.

The next step would be to get evidence on the accuracy of the model
selected. The best model is not necessarily a good model ...

Because we are working with 1000 resamples, graphical methods aren?t very
practical. We prefer not using a pseudo R2 (?A perfectly adequate model may
have what, at face value, seems like a terrible low R2 due to a high percent
of censored data? [Hosmer-Lemeshow]).

Can anyone help us, please? For weeks we are thinking on this issue and we
fail to find a good solution.

Thank you very much,

Best regards,

Albert Navarro


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