Rethinking Trial Design With Bayesian Approaches

In a recent video interview with Applied Clinical Trials, David Morton, PhD, director of biostatistics, Certara, discussed how the FDA’s increased support for Bayesian methods is expected to drive a shift from traditional frequentist approaches toward more flexible, probability-based trial designs. He emphasized the growing role of external data borrowing, adaptive decision-making, and simulation-driven planning in improving efficiency, particularly in rare disease and small population studies. Morton also highlighted the need for stronger upfront design, including justification of prior data and robust analytical frameworks, as well as greater cross-functional alignment and early regulatory engagement to support transparency, reduce uncertainty, and accelerate more data-driven development models.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

ACT: Where do you see Bayesian methods having the greatest impact—particularly in rare disease, small populations, or adaptive trials?

Morton: Rare diseases and small populations are a big area. That’s what we see a lot, especially in oncology and certain types of cancers. These methods can help power studies more effectively in those settings.

They allow for augmenting concurrent controls with external or non-concurrent data. When you have limited patient availability, you’re borrowing from historical or external controls.

In pediatrics, you can also extrapolate adult data to form informative priors when disease progression is expected to be similar. It also helps with subgroup modeling and hierarchical borrowing across strata.

Bayesian hierarchical models allow you to borrow information across similar disease subtypes, like in basket trials between patient subgroups. That’s where they can really add value.