RE: covariate selection question

From: mark.e.sale@gsk.com Subject: RE: [NMusers] covariate selection question Date: Fri, 20 Jan 2006 11:47:04 -0500 Ken, I agree with (nearly) everything you said. Especially the part about casting too wide a net. Where we disagree is: "Stepwise procedures can routinely find a parsimonious model, however, there is no guarantee that they will find the most parsimonious model nor the most biologically plausible model. There may be several almost equally parsimonious models of which a stepwise procedure may find one. Other parsimonious models not selected by a stepwise procedure may be more biologically plausible." You seem to imply (perhaps you don't mean to), that step wise will typically find the most parsimonious model. Not only is there no guarantee of finding the most parsimonious model, you have no reason to expect that you will. And in fact we have internal data that step wise rarely, if ever finds the best solution (in our, as yet unreported results, stepwise is about 0 for 20 in finding the optimal model - a record that makes the Michigan football team look good - Go Bucks). For stepwise to find the true optimal solution the assumption of independence of effects. The index case in this discussion is strong evidence (and I think we all must believe intuitively) that covariate effects - and probably all effects are highly dependent. Stepwise cannot be expected to given you the most parsimonious model in the presence of dependencies of the effects. Personal opinion: The only reason we use step wise is because we haven't found a better way (with the exception of WAM of course). Textbooks on combinatorial optimization will provide insight into better ways. Mark Sale M.D. Global Director, Research Modeling and Simulation GlaxoSmithKline 919-483-1808 Mobile 919-522-6668