FDA Issues Guidance On Modernizing Statistical Methods For Clinical Trials

In a new draft guidance issued on January 14, 2026, the FDA discussed the use of a modern statistical methodology in clinical trials designed to show the effectiveness of a new drug. The guidance, "Use of Bayesian Methodology in Clinical Trials of Drugs and Biologics Guidance for Industry," outlines how Bayesian methods can support primary inference in studies evaluating drug safety and effectiveness.

As the FDA defines, "Bayesian statistics is an approach to estimation or hypothesis testing to draw inference based on the use of Bayes' theorem. In a Bayesian analysis, data collected in a study is combined with a prior distribution that captures the pre-study information about a parameter of interest to form a posterior distribution that expresses the updated, post-study information about the parameter of interest."

In this guidance, the FDA describes several examples of prior submissions to the Agency that relied on Bayesian statistical methods. These examples include the use of Bayesian approaches to incorporate data from earlier clinical studies to demonstrate drug effectiveness, augment concurrent control arms with data from external or nonconcurrent controls, extrapolate adult clinical trial data to support effectiveness in pediatric populations, integrate data from studies involving similar diseases, disease subtypes, or distinct patient subgroups, and optimize dose selection in dose‑finding trials for oncology drugs. While these examples primarily focused on borrowing or leveraging of previously available trials or information across different patient populations within a trial, the FDA noted that "Bayesian methods can also be considered in other settings."

The guidance emphasizes that the defining feature of Bayesian methods is the use of a prior distribution, which allows analyses to incorporate existing information—whether supportive, contradictory, or neutral. It further underscores that prior construction must be systematic, transparent, and fully justified in the protocol, including an evaluation of the prior's influence and the design's operating characteristics.

The guidance further provides an explanation on the documenting and reporting requirements for FDA submissions containing results from clinical studies that relied upon Bayesian statistics. Specifically, the FDA advises that protocols relying upon Bayesian analyses should include "detailed information to support the proposed prior distribution and any external information borrowing, likelihood function, success criteria, and trial operating characteristics."

The draft guidance is open for public comment until March 13, 2026.

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