Advancing rare disease research: Exploring opportunities for Bayesian methods with FDA’s upcoming guidance

Published June 17, 2025

Regulators, sponsors, and patients can benefit from more efficient trial designs and greater precision in treatment effect estimates with the application of Bayesian methods. These statistical approaches provide a rigorous framework for augmenting clinical trials with real-world or historical trial data.

With the US Food and Drug Administration (FDA) set to publish new draft guidance by end of September 2025, detailed regulatory methodological advice will likely further expand the role of real-world data (RWD) in driving nuanced insights for specialized patient populations, including in rare diseases and subgroup analyses.

Background: Bayesian clinical trial designs

For more than a decade, the FDA has been a proponent of innovative Bayesian clinical trial designs, in which statistically rigorous “borrowing” from external data supplements trial observations to inform effect estimates.^1-3 Bayesian trials utilize existing evidence to retain adequate statistical power with smaller sample sizes, enabling robust studies when traditional strategies would be unfeasible or unethical.⁴ Many efficient and reliable Bayesian trial designs have been proposed, including for single-arm phase II^5-7 and randomized phase III^8-10 studies, but supporters maintain that Bayesian strategies remain underutilized in practice.¹¹

Since the FDA first issued a guidance document on Bayesian statistical approaches in 2010,¹² the scientific community has made substantial progress to better position Bayesian strategies for clinical decision-making. Key advances include:

• Formulation of borrowing approaches that are more robust to misspecification of prior knowledge^13-15
• Greater understanding of how to optimize the trade-off between statistical power and false positive error when borrowing^16,17
• Development of improved models to extrapolate outcomes^18,19 and to indirectly estimate relative treatment effects via a common comparator^20,21

FDA guidance has evolved to explicitly acknowledge target scenarios where there is a strong motivation to employ a Bayesian framework. Such contexts include:

• Studies in specialized patient populations (subgroups and rare diseases)^22,23
• Paediatric extrapolation studies referencing an adult population²⁴
• Non-inferiority trials²⁵
• Optimal dose-finding in phase I/II studies²⁶

Bayesian methods in the spotlight

During a recent panel at the inaugural National Organization for Rare Disorders (NORD) Rare Disease Scientific Symposium in Washington, D.C., FDA representatives reaffirmed acceptability of Bayesian trial designs to leverage real-world or historical trial data for the standard of care to augment control arm populations, or treatment-specific data from earlier-phase studies to inform predictions in an experimental arm.²⁷

The FDA emphasizes early regulatory engagement for sponsors considering a Bayesian trial design.²⁸ They advocate a collaborative approach to align all stakeholders on the optimal sources of external data and the perceived levels of prior confidence in the relevance of this historic information. Following these principles capitalizes on the transparency offered by the Bayesian framework and ensures that the clinical assumptions implicit in using selected prior data sources are thoroughly justified, so that prespecified statistical models are rigorous and fit for confirmatory decision-making.

The FDA recently delivered a demonstration project on Bayesian exploratory analyses in phase III trials as part of the CDER Center for Clinical Trial Innovation (C3TI) program,²⁹ and communicated that draft guidance on the use of Bayesian methodology in clinical trials is due by end of September 2025.³⁰ These developments address the current lack of specific regulatory guidance³¹ and should therefore facilitate the wider adoption of Bayesian methods in clinical research, as well as help to reduce issues with poor reporting of Bayesian analyses that likely stem from a present lack of expertise.¹¹

Reducing uncertainty in evidence generation

The increased availability of detailed methodological guidance from the FDA on Bayesian analyses of clinical trial data will likely have broad implications for the pharmaceutical industry, including beyond regulatory requirements. Many opportunities exist for Bayesian methods to reduce uncertainty in post-hoc exploratory subgroup analyses,³² extrapolated outcomes from immature study data,³³ and indirectly estimated relative treatment effects in diseases with very few studies.²¹ Improving interpretability of these analyses provides actionable information for clinicians to manage individual patient’s disease.

These common problems in healthcare research are also crucially important to payers, as there is increased pressure to optimize allocation of healthcare resources³⁴ and to make decisions based on interim study data.³⁶ While some bodies, such as England’s National Institute for Health and Care Excellence (NICE)³⁶ and Germany’s Institute for Quality and Efficiency in Health Care (IQWiG)³⁷ have stated their willingness to consider Bayesian models, specific technical advice remains scarce. The resources provided by the FDA may set precedents that can then be adopted by other organizations.

Prepare for Bayesian methods implementation

The upcoming draft guidance from the FDA will help to clarify regulatory expectations and highlight the merits of Bayesian methods to non-experts, as well as more broadly pave the way for adopting Bayesian methods to drive clinical development and value evidence generation.

In limiting situations where traditional trial designs are impractical or exploratory subgroup analyses are of central importance, sponsors should proactively survey existing sources of patient-level data that could supplement trial observations. Sponsors should then seek the opinion of regulators, clinicians, and statistical experts to critically evaluate the limitations of these external data and gauge a suitable extent of borrowing from the prior information to achieve the desired study operating characteristics. With careful design, Bayesian strategies can facilitate analyses that would otherwise be unfeasible while meeting regulatory expectations.

Capitalize on the opportunities to drive more informed healthcare decision making and empower evidence generation for specialized patient populations. Parexel’s Advanced Analytics team has a proven track record of successfully implementing advanced statistical models across various healthcare settings, ensuring reliable and accurate insights.

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