In the stand-off between Bayesians and frequentists, the latter own the show for pivotal trials. But with non-profits leading the drive, Bayesians are poised to enter late-stage development via platform trials in cancer. The next areas to benefit might be pediatric and rare diseases, where the approach can maximize learnings from limited data.
Despite widespread panning of the limitations of p-values, and broad agreement that Bayesian analyses can reduce costs, shorten timelines and reduce patient exposure to treatments that don’t work, Bayesian designs have yet to take hold beyond dose-finding studies.
Some pharmas are using Bayesian trials to quickly prioritize which Phase II compounds to take to Phase III, or to mine clinical data post-hoc for new targets and biomarkers (see Sidebar: “Learn Beyond Confirm”).
But few are willing to take the leap and use the approach in registrational trials.
Part of this is because FDA leadership has given full-throttled support for Bayesian methods in designing adaptive trials, but no official endorsement for Bayesian use of prior data or predictive analyses in approval decisions (see Sidebar: “Different Likelihoods”).
Janet Woodcock, director of FDA’s Center for Drug Evaluation and Research (CDER), has repeatedly touted Bayesian adaptive designs for their potential to reduce trial duration and size.
Though FDA’s Center for Devices and Radiological Health (CDRH) has published a guidance document on using Bayesian statistics in medical device trials, including pivotal trials, the agency has not produced similar guidance for therapeutics. A CBER and CDER September draft guidance on adaptive clinical trial designs included considerations for Bayesian simulation methods, but did not directly address use of the approach for registrational trials.
“How we declare success is typically a frequentist approach, and a lot of that is driven by perceived regulatory requirements.”