RE: order of covariate inclusion -> avoiding stepwise approaches -> abandoning exploratory analysis?

From: marc.gastonguay@snet.net Subject: RE: [NMusers] order of covariate inclusion -> avoiding stepwise approaches -> abandoning exploratory analysis? Date: 9/27/2003 9:07 AM Would it be fair to summarize this lengthy discussion as follows?: 1. Stepwise regression has its problems, but we still use this for covariate model development on a routine basis (in addition to other less commonly employed solutions, such as Wald Approximation Method and Genetic Algorithm). 2. There are different opinions on whether to build a covariate model using stepwise forward addition (or forward/backward) or to start at a full model and proceed with a stepwise backward elimination method. 3. We also have different opinions on how to select the covariate-parameter relationships to be investigated. This ranges from one extreme, where every measured variable should be investigated as a potential effect on every model parameter to the other, where the "best" model is known a priori. The optimal solution is undoubtedly somewhere in the middle. 4. There are different views on how to select the best model, where one approach relies on p-values obtained using the Likelihood Ratio Test and another possibility is based on point and interval estimates (or even full posterior distributions) of model parameters. 5. We need more comparisons and examples to help us work the balance between theoretically appealing approaches and practical implementation. I think the take-home point is that there is NOT just one way to build a model and that whatever you end up with is still the wrong model. Maybe we should spend more time on model evaluation and assessing sensitivity to assumptions once we do arrive at the "best" model. Marc