Hi everybody, recently I had to teach a course on Cox model, of which I am not a specialist, to an audience of medical epidemiologists. Not a good idea you might say.. anyway, someone in the audience was very hostile. At some point, he sayed that Cox model was useless, since all you have to do is count who dies and who survives, divide by the sample sizes and compute a relative risk, and if there was significant censoring, use cumulated follow-up instead of sample sizes and that's it! I began arguing that in Cox model you could introduce several variables, interactions, etc, then I remembered of logistic models ;-) The only (and poor) argument I could think of was that if mr Cox took pains to devise his model, there should be some reason...
but the story doesn't end here. When I came back to my office, I tried these two methods on a couple of data sets, and true, crude RRs are very close to those coming from Cox model. hence this question: could someone provide me with a dataset (preferably real) where there is a striking difference between estimated RRs and/or between P-values? and of course I am interested in theoretical arguments and references. sorry that this question has nothing to do with R and thank you in advance for your leniency. Eric Elguero GEMI-UMR 2724 IRD-CNRS, Équipe "Évolution des Systèmes Symbiotiques" 911 avenue Agropolis, BP 64501, 34394 Montpellier cedex 5 FRANCE ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
