RE: order of covariate inclusion

From: Ken.Kowalski@pfizer.com Subject: RE: [NMusers] order of covariate inclusion Date: 9/25/2003 2:42 PM Pete, As I indicated in my previous message, stepwise procedures have their pitfalls in the presence of collinearity among the covariates. You might try a backward elimination approach starting from a full model (all covariate parameters included simultaneously) and see if you arrive at the same model as the forward selection approach. Of course, if you have a very high degree of collinearity among the covariates you may not be able to fit the full model (rounding errors) but this is useful to know as the modeler may be faced with having to make difficult choices on reducing the set of covariates effects to investigate based on his/her understanding of which covariate effects are the most plausible. One of the problems with stepwise procedures are that they result in selecting a single model and don't give a sense of competing models that might also provide a good fit. Ignoring the effects of collinearity just because a forward selection can find a good fitting model doesn't mean we should feel complacent about the model chosen by forward selection. Because of the order of testing there may be other models that will never be evaluated by the procedure that are as good and maybe better than the one obtained by forward selection. This is one of the reasons that motivated me to develop the WAM algorithm. It provides a ranking of all 2^k possible models (where k is the number of covariate parameters) and the top 10-15 models are then run in NONMEM to give some sense of the competing good-fitting models. Try it...you might like it. Ken