UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course

The UCSF-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI) is pleased to announce the 2025 Bayesian Thinking in Clinical Research Course.

Why this course? There are a variety of 4-hour or one-day short courses that cover some Bayesian concepts or examples. There are also many in-depth statistical courses that are steeped in mathematics, computation, and inference. This course is designed to be in the sweet spot: A more in-depth course on Bayesian thinking with real-life examples and applications that do not involve mathematics. The UCSF-Stanford CERSI Bayesian Thinking in Clinical Research Course is meant to focus on concepts that will allow students to have engaging conversations with statisticians and review the clinical trial literature with a more educated perspective on inferring what is likely to be true.

Bayesian Statistics has been a major branch of statistical science for centuries but has had limited utility in practical applications for a wide variety of reasons. Bayesian methods are now emerging as a useful and powerful alternative to hypothesis testing and frequentist statistical approaches based on p-values. Bayesian methods offer more information and easier interpretation due to direct estimation of the probability that a conclusion is true given the data observed in a trial. Bayesian Statistical methods are based on incorporating prior knowledge into the analysis of newly generated experimental data to update our knowledge of a scientific hypothesis in a quantitative way. In this sense, the Bayesian approach is more aligned with scientific endeavors that continually build on previous knowledge by performing experiments and analyzing data to come to a better understanding of natural phenomena.

Participants will have the opportunity to learn Bayesian concepts and statistical principles for how to assess the likelihood of a hypothesis being true or false. The initial set of lectures will focus on broad principles of Bayesian thinking with subsequent lectures focused on more detailed implementation in clinical trials. Participants will be exposed to a broad range of case studies covering a variety of therapeutic areas and phases of drug development, including phase 3 trials for regulatory approval. The lectures will cover key Bayesian concepts and terminology to enable the audience to read and understand the publication on Bayesian trials in medical literature. All lectures will focus on principles and concepts without the underlying mathematics. Thus, the material should be accessible to a broad scientific and clinical audience and may also help statisticians who have not been exposed to Bayesian methods.

This is a virtual course comprised of twelve 90-minute sessions delivered live by experts in the field of Bayesian statistics and its applications to clinical trials. Sessions will be held on Thursdays, with some exceptions, from January 23, 2025, through April 10, 2025, from 10 – 11:30 am Pacific Time (1 – 2:30 pm Eastern Time). Each session may include pre-reading assignments, lectures, and discussion of case studies. Participants who successfully complete the course will be issued a Statement of Completion from the UCSF-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI). Sessions will be recorded and available to all participants for the duration of the course.

Note: This course is intended for professional development and is not accredited for CME or PMP credit.

Learning objectives

• Discuss how Bayesian methods are used in the design, analysis, and interpretation of clinical studies.
• Explain factors that are important when considering the use of a Bayesian approach.
• Explain the fundamental differences between Frequentist hypothesis testing and Bayesian inference (particularly the contrast between p-values and Bayesian posterior probabilities).
• Interpret clinical literature that uses Bayesian methods for inference and interpretation.
• Describe the basics of decision-making when using Bayesian inference (e.g., interim analysis, study success criteria, probability of study success, go/no-go decisions in drug development).
• Explain the flexibility available for adaptive study designs including the inclusion of interim analyses.
• Discuss the use of Bayesian methods to extrapolate efficacy or safety findings to another population (e.g., adults to pediatrics) and to borrow information across subgroups to estimate more precise treatment effects in each subgroup.

Target audience

• Early- to mid-career professionals involved in clinical trials (industry, academia, and government) who would like a broad overview of the latest developments in the application of Bayesian methods in clinical research.
• Faculty members who are interested in using clinical trials to advance medical practice.
• Trainees (students/residents/postdocs) who would like to complement their training and research in basic and applied statistics through the review of case studies and examples.

A basic understanding of statistical hypothesis testing and clinical trial design and execution is necessary. Familiarity with regulated clinical drug development would also be helpful but not necessary.