Bayesian Meta Experimental Design: Evaluating Cardiovascular Risk in New Antidiabetic Therapies to Treat Type 2 Diabetes

• Activity Number: 493
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): H. Xia*+ and Joseph Ibrahim and Ming-Hui Chen and Thomas Liu
• Companies: Amgen, Inc. and The University of North Carolina at Chapel Hill and University of Connecticut and Amgen, Inc.
• Keywords: Fitting prior ; Partial borrowing power prior ; Sampling prior ; Simulation ; Survival data ; Meta-Design

Abstract

The recent guidance from the FDA for the evaluation of new therapies in the treatment of Type 2 diabetes, calls for a program-wide meta-analysis of cardiovascular outcomes. In this context, we develop a new Bayesian meta-analytic approach using survival models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We extend the fitting and sampling priors of Wang and Gelfand (2002) to Bayesian meta-analysis clinical trial design with a focus on controlling the type I error and power. The historical survival data are incorporated via the power priors of Ibrahim and Chen (2000). Various properties of the proposed methodology are examined and an efficient Markov chain Monte Carlo sampling algorithm is developed to sample from the posterior distributions. In addition, we develop a novel simulation-based algorithm for computing various quantities, such as the power and the type I error, involved in the Bayesian meta-analysis trial design. The proposed methodology is applied to the design of a phase 2/3 development program including a non-inferiority clinical trial for CV risk assessment in Type 2 diabetes studies.