Journal of biopharmaceutical statistics
-
Noninferiority trials are unique because they are dependent upon historical information in order to make meaningful interpretation of their results. Hence, a direct application of the Bayesian paradigm in sequential learning becomes apparently useful in the analysis. This paper describes a Bayesian procedure for testing noninferiority in two-arm studies with a binary primary endpoint that allows the incorporation of historical data on an active control via the use of informative priors. ⋯ This criterion is evaluated and compared with the frequentist method using simulation studies in keeping with regulatory framework that new methods must protect type I error and arrive at a similar conclusion with existing standard strategies. Results show that both methods arrive at comparable conclusions of noninferiority when applied to a modified real data set. The advantage of the proposed Bayesian approach lies in its ability to provide posterior probabilities for effect sizes of the experimental treatment over the active control.
-
Bayesian statistical methodology has been used for more than 10 years in medical device premarket submissions to the U. S. Food and Drug Administration (FDA). ⋯ In addition to the increasing number of Bayesian methodological papers in the statistical journals, a number of successful Bayesian clinical trials in the biomedical journals have been recently reported. Some challenges that require more methodological development are discussed. The promise of using Bayesian methods for incorporation of prior information as well as for conducting adaptive trials is great.
-
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. ⋯ 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.
-
In pharmaceutical drug development, for regulatory purposes, there are increasing discussions on the establishment of statistically significant results demonstrating the efficacy of a new treatment on multiple co-primary endpoints. At the design stage with multiple co-primary endpoints, it is critical to determine the appropriate sample size for indicating statistical significance for all co-primary endpoints with preserving the intended power set, since the type II error increases as the number of co-primary endpoints increases. We provide fundamental formulae for power and sample size calculation with multiple co-primary endpoints and illustrate the aspect of the provided methods through numerical tables and examples.