Variable Selection for Marginal Longitudinal Generalized Linear Models

Variable selection is an essential part of any statistical analysis and yet has been somewhat neglected in the context of longitudinal data analysis. In this article, we propose a generalized version of Mallowsʹs Cp (GCp) suitable for use with both parametric and nonparametric models. GCp provides an estimate of a measure of modelʹs adequacy for prediction. We examine its performance with popular marginal longitudinal models (fitted using GEE) and contrast results with what is typically done in practice: variable selection based on Wald-type or score-type tests. An application to real data further demonstrates the merits of our approach while at the same time emphasizing some important robust features inherent to GCp.