PREDICTIVE MODEL SELECTION FOR REPEATED-MEASURES RANDOM EFFECTS MODELS USING BAYES FACTORS

The random effects model fit to repeated measures data is an extremely common model and data structure in current biostatistical practice. M odern data analysis often involves the selection of models within broa d classes of prespecified models, but for models beyond the generalize d linear model, few model-selection tools have been actively studied. In a Bayesian analysis, Bayes factors are the natural tool to use to e xplore these classes of models. In this paper, we develop a predictive approach for specifying the priors of a repeated measures random effe cts model with emphasis on selecting the fixed effects. The advantage of the predictive approach is that a single predictive specification i s used to specify priors for all models considered. The methodology is applied to a pediatric pain data analysis.