Challenging statistical issues often arise in the design and analysis of clinical trials to assess safety and effectiveness of medical devices in the regulatory setting. The use of Bayesian methods in the design and analysis of medical device clinical trials has been increasing significantly in the past decade, not only due to the availability of prior information, but mainly due to the appealing nature of Bayesian clinical trial designs. The Center for Devices and Radiological Health at the Food and Drug Administration (FDA) has gained extensive experience with the use of Bayesian statistical methods and has identified some important issues that need further exploration. In this article, we discuss several topics relating to the use of Bayesian statistical methods in medical device trials, based on our experience and real applications. We illustrate the benefits and challenges of Bayesian approaches when incorporating prior information to evaluate the effectiveness and safety of a medical device. We further present an example of a Bayesian adaptive clinical trial and compare it to a traditional frequentist design. Finally, we discuss the use of Bayesian hierarchical models for multiregional trials and highlight the advantages of the Bayesian approach when specifying clinically relevant study hypotheses.

Adaptive designs have generated great interest in the clinical trial community as a result of their versatility and efficiency. Recently, the Center for Devices and Radiological Health (CDRH) at the US Food and Drug Administration (FDA) surveyed all adaptive design applications submitted between 2007 and May 2013 for regulatory review. In this paper, we discuss the overall results and findings that emerged from an in-depth examination of the submissions. We summarize the current status of adaptive designs used in medical device studies. We also identify some of the lessons learned and common pitfalls that we encountered in our review of the designs.

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