FDA Bayesian trials guidance is good, but will we make a good use of it?
Back in January FDA published draft guidance on use of Bayesian clinical trials. Two of us already covered it, Adam Kroetsch on this blog and yours truly on Andrew Gelman’s blog. Now Andrew, Erik van Zwet and I also put together a short commentary for JAMA:
It’s only around 1,000 words, so you should read it. The overall assessment is similar to the piece I wrote in January: we think it’s a very positive step. In one sentence, the contribution of this guidance is to clearly define how relevant information can be used to create Bayesian priors for trials, which can make many of them more efficient. Now the question is whether trial sponsors are willing to do it more often.
But there is also the perspective of the regulator: are there any broader benefits to the FDA from being Bayesian? We think the answer is yes. Following these principles can lead to more consistent and transparent decisions. This may strike some people as counterintuitive. The most common critique of Bayesian methods in clinical trials is that they “sneak in subjectivity” under the guise of prior. So how could they lead to more consistency? As Adam nicely outlined in his piece, the status quo is that the FDA already relies very heavily on “clinical judgement” in its approvals, which does not sound dissimilar from having a Bayesian prior to me! We write:
Consistency and transparency in these decisions are important—uncertainty about the procedures would prevent drugmakers from conducting research and lack of transparency can expose decisions to political pressure and lobbying. However, a survey of 912 FDA applications [5] found that FDA decisions did not consistently cite prior reasoning and that some approvals, after additional review, reflected new interpretations of existing evidence rather than new evidence, raising concerns about consistency across review cycles.
This survey of 912 applications, Janiaud et al, is also a short and interesting read. It is co-authored by FDA staff and it broadly acknowledges that there is no consistency, “resulting in standalone, bespoke decisions”. And that is not automatically a bad thing! As we also say in the piece, flexibility is necessary in regulatory decisions. But there are notable examples where this becomes a problem, where the basis of these judgements becomes… questionable:
A recent high-profile example may be illustrative. In February 2026, the head of the FDA’s Center for Biologics Evaluation and Research issued a refusal-to-file letter for Moderna’s mRNA influenza vaccine, citing the failure to compare the vaccine against high-dose competitors in older adults [6]; however, this decision was reversed after a week.[7] The problem of comparisons among relevant subpopulations can be directly and transparently addressed using hierarchical bayesian methods. Influenza vaccines are among the best studied therapeutics in existence. Relevant information from other trials could be incorporated into the prior and data reanalyzed to conduct a risk-benefit analysis in older adults without conducting a new trial.
In other words, this new guidance specifically focuses on the topic of how to transparently incorporate information across other trials and incorporate benefit-risk into the definition of success. If the FDA reserves the right to move the goalposts somewhat, that is a valid concern! But why not use the principles from its own Bayesian guidance to reach this decision: based on all available data, how likely is it that the vaccine is not as effective in the elderly as its high-dose competitors? What sort of prior assumptions are required to reach this conclusion? The Bayesian principles in this guidance make this project feasible.
In sum, we should be scrutinising both the work of the drug maker and the decision maker. Yes, it is entirely the sponsor’s job to generate data that proves their therapeutic is safe and effective. But then it is the job of the regulator to prove to us that their approval decisions are rational—especially in the cases which lean heavily on judgement. Or, as we like to say in my line of work: show us the model!
ANNOUNCEMENT KLAXON
The Irish government is seeking volunteers for their Clinical Trials Advisory Council. If you have relevant expertise and are interested in shaping European regulatory frameworks, consider applying.