title: Bayesian modeling of multivariate average bioequivalence.
abstract: Bioequivalence trials are usually conducted to compare two or more formulations of a drug. Simultaneous assessment of bioequivalence on multiple endpoints is called multivariate bioequivalence. Despite the fact that some tests for multivariate bioequivalence are suggested, current practice usually involves univariate bioequivalence assessments ignoring the correlations between the endpoints such as AUC and C(max). In this paper we develop a semiparametric Bayesian test for bioequivalence under multiple endpoints. Specifically, we show how the correlation between the endpoints can be incorporated in the analysis and how this correlation affects the inference. Resulting estimates and posterior probabilities 'borrow strength' from one another where the amount and the direction of the strength borrowed are determined by the prior correlations. The method developed is illustrated using a real data set.
authors: Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303-3083, USA. pghosh@mathstat.gsu.edu
authors: Pulak
authors: P
authors: Ghosh
authors: Mithat
authors: M
authors: Gönen
mesh_terms: D001499:Bayes Theorem; D001682:Biological Availability; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D004917:Erythromycin / Q000097:blood / Q000493:pharmacokinetics; D006801:Humans; D015233:Models, Statistical; D015999:Multivariate Analysis; D013810:Therapeutic Equivalency
chemical_list: D004917:Erythromycin