title: A semi-parametric Bayesian approach to average bioequivalence.
abstract: Bioequivalence assessment is an issue of great interest. Development of statistical methods for assessing bioequivalence is an important area of research for statisticians. Bioequivalence is usually determined based on the normal distribution. We relax this assumption and develop a semi-parametric mixed model for bioequivalence data. The proposed method is quite flexible and practically meaningful. Our proposed method is based on a mixture normal distribution and a non-parametric Bayesian approach using a Dirichlet process mixture prior. A numerical example illustrates the use of our procedure.
authors.affiliation: Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303-3083, U.S.A. pghosh@mathstat.gsu.edu
authors.forename: Pulak
authors.initials: P
authors.lastname: Ghosh
authors.forename: Gary L
authors.initials: GL
authors.lastname: Rosner
mesh_terms: D001499:Bayes Theorem; D035843:Biomedical Research / Q000706:statistics & numerical data; D018592:Cross-Over Studies; D015233:Models, Statistical; D018709:Statistics, Nonparametric; D013810:Therapeutic Equivalency; D014481:United States