Bayesian Clinical Trials

Advancements in modern computation power have led to an increasing interest in incorporating Bayesian methods into clinical trials. A key benefit of Bayesian methods is that they incorporate prior knowledge into trial design and estimate the probability of treatment effect. Despite the benefits, the use of these methods in clinical trial settings has been limited so far.

The DIA Bayesian Scientific Working Group (DIA BSWG) conducted a survey to gain insight into non-statistician clinical trialists’ level of comfort with Bayesian methods and to identify the barriers for implementation. Based on their findings, the DIA BSWG worked with clinical trail and regulatory experts to develop a Bayesian Clinical Trial special collection. However, the group is also inviting contributions related to this topic from other experts in the field.

The collection discusses the advantages and challenges of applying a Bayesian framework in different clinical trial designs, interpreting the results, and making interim decisions. These concepts are illustrated through examples from different therapeutic applications.

In addition to the already published article(s) below, the special collection plans to cover the following topics: (1) Perceptions and Interpretation of Bayesian and Frequentist Statistics; (2) Bayesian Statistics Education; (3) FDA (CDER and CBER) Perspectives on Bayesian Methods; (4) Bayesian Statistics in Medical Device trials; (5) Bayesian Statistics in Rare Diseases and Pediatrics; and (6) Bayesian Statistics in Contemporary Events, including COVID-19 Trials.

We invite experts within the field of Bayesian clinical trials to submit manuscripts relevant to these topics.


  • Natasha Muehlemann

    Natalia Muehlemann has over 18 years of experience in Life Sciences industry and combines medical, statistics & data science and strategic expertise enhancing value through evidence generation, advanced analytics and stakeholders’ engagement. In her position of VP, Strategic Consulting at Cytel, Natalia leads Clinical Development and Regulatory practice focusing on the integration of adaptive and innovative designs into clinical development strategies that combine clinical, regulatory, and market access considerations. Natalia acts as Expert Jury member for the European Commission’s European Innovation Council and SMEs Executive Agency.

  • Jennifer Clark

    Jennifer Clark is a Mathematical Statistician in the Center for Drug Evaluation and Research at the FDA with extensive experience in developing and evaluating study designs for diabetes, general endocrinology, cardiology, and nephrology drug trials. Her research interests include cardiovascular trials, estimands, biomarker assessment, clinical outcome assessments, digital health technology, and efficient applications of statistical methods in clinical trials.

  • Alexei Ionan

    Alexei C. Ionan is a Mathematical Statistician in the Division of Biometrics IX of the Office of Biostatistics, supporting application review in the Office of Oncologic Diseases at the FDA. He has over 17 years of experience evaluating, developing, and applying statistical methods in oncology. He leads multiple groups at the FDA. His research interests include Bayesian methods, estimands, decision theory, causal inference, predictive biomarkers, early detection of cancer, and optimal design.

Articles (1 in this collection)