title: Predictive model selection for repeated measures random effects models using Bayes factors.
abstract: The random effects model fit to repeated measures data is an extremely common model and data structure in current biostatistical practice. Modern data analysis often involves the selection of models within broad classes of prespecified models, but for models beyond the generalized linear model, few model-selection tools have been actively studied. In a Bayesian analysis, Bayes factors are the natural tool to use to explore these classes of models. In this paper, we develop a predictive approach for specifying the priors of a repeated measures random effects model with emphasis on selecting the fixed effects. The advantage of the predictive approach is that a single predictive specification is used to specify priors for all models considered. The methodology is applied to a pediatric pain data analysis.
authors.affiliation: Department of Biostatistics, UCLA School of Public Health 90095-1772, USA.
authors.forename: R E
authors.initials: RE
authors.lastname: Weiss
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: J G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D002648:Child; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D010146:Pain / Q000523:psychology