A Bayesian Adaptive Design for Device Surveillance

• Activity Number: 70
• Type: Topic Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Theodore Lystig*+ and Thomas A Murray and Bradley P Carlin
• Companies: Medtronic and University of Minnesota and University of Minnesota
• Keywords: Adaptive trial ; Bayesian statistics ; Surveillance ; Interim analysis ; Markov Chain Monte Carlo (MCMC) ; Medical device

Abstract

A post-market device surveillance study may have an important primary objective tied to estimating a survival function at some future time T with a certain amount of precision. This presentation will describe the details and various operating characteristics of a Bayesian adaptive design for device surveillance, as well as a method for estimating a sample size vector (determined by the maximum sample size and a pre-determined number of interim looks) that will achieve the desired power. We adopt a Bayesian adaptive framework which recognizes the fact that persons enrolled in a study report their results over time, not all at once. At each interim look we assess whether we expect to achieve our goals with only the current group, or whether the achievement of such goals is extremely unlikely even for the maximum sample size. We show that our Bayesian adaptive design can outperform two non-adaptive frequentist methods currently recommended by FDA guidance documents in many settings. We also investigate the robustness of our procedures to model misspecification and changes in the trial's enrollment rate.