title: An objective Bayesian analysis of a crossover design via model selection and model averaging.
abstract: Inference about the treatment effect in a crossover design has received much attention over time owing to the uncertainty in the existence of the carryover effect and its impact on the estimation of the treatment effect. Adding to this uncertainty is that the existence of the carryover effect and its size may depend on the presence of the treatment effect and its size. We consider estimation and testing hypothesis about the treatment effect in a two-period crossover design, assuming normally distributed response variable, and use an objective Bayesian approach to test the hypothesis about the treatment effect and to estimate its size when it exists while accounting for the uncertainty about the presence of the carryover effect as well as the treatment and period effects. We evaluate and compare the performance of the proposed approach with a standard frequentist approach using simulated data, and real data. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Mathematical Sciences, University of Cincinnati, Cincinnati, 45221, OH, U.S.A.
authors.forename: Dandan
authors.initials: D
authors.lastname: Li
authors.affiliation: Department of Mathematical Sciences, University of Cincinnati, Cincinnati, 45221, OH, U.S.A.. sivagas@ucmail.uc.edu.
authors.forename: Siva
authors.initials: S
authors.lastname: Sivaganesan
mesh_terms: D001499:Bayes Theorem; D018592:Cross-Over Studies; D012107:Research Design; D035501:Uncertainty
keywords: crossover design; model selection and averaging; objective Bayesian