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

From: lgibiansky@emmes.com Subject: RE: [NMusers] order of covariate inclusion -> avoiding stepwise approaches -> abandoning exploratory analysis? Date: 9/26/2003 12:59 PM Dear All, With all the respect to the other methods that may be more attractive, we cannot rule our forward-addition approach. If you have a model with 5-6 random effects and 20-30 covariates (say, demographics, lab data and concomitant medications, this can easily give you 30), it is unrealistic to fit the full model (30 parameters for each of the random effects). We need to screen the covariates, via diagnostic plots of random effects versus covariates, GAM, significance relative to the base model, etc. If the list of covariates shortens so that you can fit the full model, that is great. But if not, what would you do ? I would add the most significant covariate and continue from that point (forward selection procedure), may be in chunks, adding this covariate to all the parameters at once, and then removing not-significant ones. Mark, is there any alternative to this process if the full model is not converging ? Thanks, Leonid