FDA Issues Draft Guidance to Advance Bayesian Methods in Clinical Trials

The FDA has released new draft guidance aimed at modernizing the use of statistical methodologies in clinical trials.¹

The new guidance, Use of Bayesian Methodology in Clinical Trials of Drugs and Biologics, is intended to provide clarity on how sponsors can apply Bayesian statistical methods to improve trial efficiency, reduce development timelines, and make better use of existing data sources while maintaining rigorous standards for safety and efficacy evaluation.

Guidance targets efficiency, cost, and trial design flexibility

In announcing the draft guidance, the FDA noted that Bayesian methodologies can help address persistent challenges in drug development such as high costs, long timelines, and limited patient availability.

In an agency statement, FDA Commissioner Marty Makary, MD, MPH, said: "Bayesian methodologies help address two of the biggest problems of drug development: high costs and long timelines. Providing clarity around modern statistical methods will help sponsors bring more cures and meaningful treatments to patients faster and more affordably.”

Unlike traditional approaches, Bayesian analyses combine trial data with relevant prior information to generate updated probability distributions that can be used to support inference around safety and efficacy. The FDA noted that these methods can be applied across multiple stages of clinical development when used appropriately.

Applications across adaptive designs and rare disease trials

The guidance outlines a range of use cases where Bayesian methods may be incorporated into clinical trials, including determining futility or success earlier in adaptive trials, informing dose selection for subsequent studies, and supporting subgroup analyses.

Bayesian approaches may also incorporate external data sources such as prior clinical studies, real-world evidence (RWE), or external or nonconcurrent controls when scientifically justified. While the guidance places emphasis on the use of Bayesian methods to support primary inference, it acknowledges their broader role in trial design optimization.

The agency highlighted that Bayesian methodologies may be especially valuable in rare disease and pediatric trials, where smaller patient populations can limit the feasibility of traditional trial designs.

PDUFA VII commitments and public comment period

The release of the draft guidance fulfills a commitment outlined under the Prescription Drug User Fee Act (PDUFA) VII, which included performance goals related to enhancing the FDA’s capacity to review complex and innovative clinical trial designs.

As part of that agreement, the FDA committed to issuing draft guidance on Bayesian methodology for drugs and biologics to provide sponsors with greater regulatory clarity. The agency is now seeking public comment on the draft guidance.

Regulatory momentum builds around data modernization

The Bayesian methodology guidance follows another recent FDA action aimed at expanding the use of advanced data sources in regulatory decision-making. In December 2025, the agency removed a longstanding barrier to the use of RWE in drug and device application reviews by eliminating the requirement that identifiable individual patient data always be submitted.²

Under the updated policy, FDA reviewers will assess the strength and relevance of submitted RWE on a case-by-case basis, opening the door to broader use of de-identified datasets such as national disease registries, electronic health record networks, and insurance claims databases.

"We're removing unnecessary barriers that have prevented us from using powerful real-world evidence to get life-changing treatments to patients faster,” Makary said in a press release from the time. “This common-sense reform will unlock access to vast databases like cancer and cystic fibrosis registries that contain critical insights about how treatments work in the real world."

Together, the FDA’s recent actions on Bayesian methodology and RWE reflect a broader regulatory effort to modernize clinical trial evaluation by enabling more efficient designs, greater use of existing data, and earlier, more informed decision-making across the drug development lifecycle.

Expert insight on the use of RWE

Earlier in September 2025, Applied Clinical Trials caught up with Ananth Kadambi, VP of real-world evidence and modeling solutions at Certara to discuss the FDA’s recent emphasis on requiring overall survival as the primary endpoint in oncology studies.

Kadambi noted that RWE and predictive modeling can be integral parts of an integrated evidence framework.

“I […] mentioned this evidence gap between regulators and payers, and I think one approach that can help bridge this gap earlier in the process is to bring these tools, like real-world evidence and model informed drug development to bear earlier on in development,” he said in a video interview.

References

1. FDA Issues Guidance on Modernizing Statistical Methods for Clinical Trials. News release. FDA. January 12, 2026. Accessed January 14, 2026. https://www.fda.gov/news-events/press-announcements/fda-issues-guidance-modernizing-statistical-methods-clinical-trials

2. FDA Eliminates Major Barrier to Using Real-World Evidence in Drug and Device Application Reviews. News release. FDA. December 15, 2025. Accessed January 14, 2026. https://www.fda.gov/news-events/press-announcements/fda-eliminates-major-barrier-using-real-world-evidence-drug-and-device-application-reviews