<quote author="Göran Broström">
[...]
I do not understand why you expect to get comparable results with SAS
"discrete" and coxph "exact". They are two different approaches to
handling ties (as Terry explained; of course, some comparability
should be expected in "normal" cases).
[...]
</quote>

I didn't know precisely the specifities of each approximation method.
I thus came back to section 3.3 of Therneau and Grambsch, Extending the Cox
Model. I think I now see things more clearly. If I have understood
correctly, both "discrete" option and "exact" functions assume "true"
discrete event times in a model approximating the Cox model. Cox partial
likelihood cannot be exactly maximized, or even written, when there are some
ties, am I right ?

In my sample, many of the ties (those whithin a single observation of the
process) are due to the fact that continuous event times are grouped into
intervals.

So I think the logistic approximation may not be the best for my problem
despite the estimate on my real data set (shown on my previous post) do give
interessant results regarding to the context of my data set !
I was thinking about distributing the events uniformly in each interval.
What do you think about this option ? Can I expect a better approximation
than directly applying Breslow or Efron method directly with the grouped
event data ? Finally, it becomes a model problem more than a computationnal
or algorithmic one I guess.

<quote author="Terry Therneau-2">
[...]
I never use the discrete option, having found the Efron
approximation to be sufficient in every practical situation.
[...]
</quote>

Which criteria would you use to determinate if Efron approximation is good
or not with grouped event data ? Is it possible to compare appproximation
models by the results and not by the model assumption ?

Thank you very much.

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