title: Bayesian sequential meta-analysis design in evaluating cardiovascular risk in a new antidiabetic drug development program.
abstract: Recently, the Center for Drug Evaluation and Research at the Food and Drug Administration released a guidance that makes recommendations about how to demonstrate that a new antidiabetic therapy to treat type 2 diabetes is not associated with an unacceptable increase in cardiovascular risk. One of the recommendations from the guidance is that phases II and III trials should be appropriately designed and conducted so that a meta-analysis can be performed. In addition, the guidance implies that a sequential meta-analysis strategy could be adopted. That is, the initial meta-analysis could aim at demonstrating the upper bound of a 95% confidence interval (CI) for the estimated hazard ratio to be < 1.8 for the purpose of enabling a new drug application or a biologics license application. Subsequently after the marketing authorization, a final meta-analysis would need to show the upper bound to be < 1.3. In this context, we develop a new Bayesian sequential meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for sequential meta-analysis clinical trial design with a focus on controlling the familywise type I error rate and power. We use the partial borrowing power prior to incorporate the historical survival meta-data into the Bayesian design. We examine various properties of the proposed methodology, and simulation-based computational algorithms are developed to generate predictive data at various interim analyses, sample from the posterior distributions, and compute various quantities such as the power and the type I error in the Bayesian sequential meta-analysis trial design. We apply the proposed methodology to the design of a hypothetical antidiabetic drug development program for evaluating cardiovascular risk.
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, Connecticut 06269, U.S.A.
authors.forename: Ming-Hui
authors.initials: MH
authors.lastname: Chen
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
authors.forename: H
authors.initials: H
authors.lastname: Amy Xia
authors.forename: Thomas
authors.initials: T
authors.lastname: Liu
authors.forename: Violeta
authors.initials: V
authors.lastname: Hennessey
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000209:etiology*; D017322:Clinical Trials, Phase II as Topic / Q000379:methods; D017326:Clinical Trials, Phase III as Topic / Q000379:methods; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000188:drug therapy; D006801:Humans; D007004:Hypoglycemic Agents / Q000009:adverse effects*; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D012107:Research Design / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000706:statistics & numerical data
chemical_list: D007004:Hypoglycemic Agents
keywords: fitting prior; interim analysis margin; meta-survival data; partial borrowing power prior; sampling prior; trial success margin