FDA's lead mathematical statistician from CDER, Dr. Jennifer Clark. has published a podcast (here) providing an overview of Bayesian statistics, advantages of this approach over traditional statistics, and the areas where FDA finds Bayesian approaches particularly useful in risk-benefit analysis of a new drug.

Bayesian Statistics is a particular approach of applying probability to statistical problems. This approach starts with a summary of our prior beliefs based on the relevant, available information. When we collect new data, so for example in the course of a clinical trial, the information from this data is combined with our prior beliefs to provide our current beliefs in terms of probabilities.

Traditional or classical statistical approaches to decision-making are based on only the new data and they don’t incorporate any prior beliefs

By the end of the Fiscal Year in 2025, FDA also anticipates publishing a draft guidance on the use of Bayesian methodology in clinical trials of drugs and biologics

• The areas where Bayesian statistics is particularly useful are pediatric drug development, ultra-rare diseases, and dose-finding trials.
• The podcast mentions a recent example where Bayesian statistics was used in the FDA's decision making: New drug application (NDA) for a fixed dose combination of budesonide and albuterol sulfate metered dose inhaler, submitted by AstraZeneca and Bond Avillion (FDA Pulmonary-Allergy Drugs Advisory Committee, November 2022).

SOURCE

• Using Bayesian statistical approaches to advance our ability to evaluate drug products. 18 August 2023 [Podcast, Transcript]. 18 August 2023 [archive: a, b]

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