It is well-known that that information should not be discarded during the analysis phase (such as what happens with dichotomization), and one of the typical examples for this point is that if we want to study 30-day mortality, and we know the survival times, we should not use logistic regression (as it would essentially bin the survival times), rather use a Cox-model. And, if doctors insist to see results for the 30-day mark, that’s fine, but then extract it from the Cox-model – it is still no reason to use logistic regression.
The problem I now face is just 30-day mortality prediction, and I indeed have survival times to day precision. But they go to 15 years! Should I apply this advice here as well?! (I.e. use Cox model instead of logistic regression.) While I can’t produce any statistical argument, I very-very much doubt that using information on when the patient died after - say - 10 years has any use for 30-day mortality prediction… But the Cox-model introduces further requirements, chiefly proportionality, which can be avoided with logistic regression.
So, basically my question is: can I still use - in contrast to the general advice - logistic regression in this particular case?