RE: Missing covariates

From: "KOWALSKI, KENNETH G. [PHR/1825]" <kenneth.g.kowalski@pharmacia.com> Subject: RE: Missing covariates Date: Mon, 2 Jul 2001 14:54:24 -0500 Dear Joga, The downside to model building with MI is that for each imputed data set (I guess) we would need to apply the model building procedure. In so doing, we know that we will get optimistic standard errors (whole point of doing MI) and thus probably also more false positives since standard errors and hypothesis tests are related. So, how do we apply the model building procedure to assess which covariates are included in the final model for each imputed data set? Afterall, it is during the averaging process of the multiple imputations that we adjust the standard errors. I don't think the theory has been worked out on how to combine estimates across different final models obtained from the model building procedure applied to each imputed data set when the final models can have different covariate parameters excluded or restricted to zero. If on the otherhand we fit the full model to each imputed data set then MI should work nicely. However, how do we go from this full model with MI adjusted estimates and standard errors to a more parsimonious final model that we might want to use for predictions and simulation? It is something for Rubin et. al. to think about. Ken