title: Bayesian sample size determination under hypothesis tests.
abstract: We develop a Bayesian approach for calculating sample sizes for clinical trials under the framework of hypothesis tests. We extend the work of Weiss (The Statistician 1997; 46: 185-191) to include composite distributions for the treatment effect and the variance of the data within the null and alternative hypotheses. We select sample sizes using the Bayes factor and the averaged type I error and type II error defined by Weiss (The Statistician 1997; 46: 185-191). Our approach allows the uncertainty inherent in eliciting prior information for both the treatment effect and the variance and permits informative prior information for unknown quantities through the hypothesis specification. We illustrate our method through a real data example from a clinical trial for treatment of multiple sclerosis and from the cerclage trial for preterm birth prevention in high-risk women.
authors.affiliation: Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA. xzhang@ms.soph.uab.edu
authors.forename: Xiao
authors.initials: X
authors.lastname: Zhang
authors.forename: Gary
authors.initials: G
authors.lastname: Cutter
authors.forename: Thomas
authors.initials: T
authors.lastname: Belin
mesh_terms: D001499:Bayes Theorem; D023802:Cerclage, Cervical; D005260:Female; D006801:Humans; D007372:Interferons / Q000627:therapeutic use; D009103:Multiple Sclerosis / Q000188:drug therapy; D011247:Pregnancy; D047928:Premature Birth / Q000517:prevention & control; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018401:Sample Size; D035501:Uncertainty
chemical_list: D007372:Interferons