title: Bayesian approaches in medical device clinical trials: a discussion with examples in the regulatory setting.
abstract: 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.
authors.affiliation: Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA.
authors.forename: Pablo
authors.initials: P
authors.lastname: Bonangelino
authors.forename: Telba
authors.initials: T
authors.lastname: Irony
authors.forename: Shengde
authors.initials: S
authors.lastname: Liang
authors.forename: Xuefeng
authors.initials: X
authors.lastname: Li
authors.forename: Vandana
authors.initials: V
authors.lastname: Mukhi
authors.forename: Shiling
authors.initials: S
authors.lastname: Ruan
authors.forename: Yunling
authors.initials: Y
authors.lastname: Xu
authors.forename: Xiting
authors.initials: X
authors.lastname: Yang
authors.forename: Chenguang
authors.initials: C
authors.lastname: Wang
mesh_terms: D017130:Angioplasty / Q000379:methods; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000331:legislation & jurisprudence / Q000379:methods* / Q000706:statistics & numerical data; D018795:Device Approval / Q000331:legislation & jurisprudence*; D004869:Equipment Safety / Q000706:statistics & numerical data* / Q000639:trends; D004864:Equipment and Supplies / Q000706:statistics & numerical data*; D033161:Government Regulation; D006801:Humans; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000379:methods / Q000706:statistics & numerical data* / Q000639:trends; D009203:Myocardial Infarction / Q000601:surgery / Q000628:therapy; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data / Q000639:trends; D012107:Research Design; D015607:Stents; D016896:Treatment Outcome; D014481:United States