The FDA just gave us 25 pages on Bayesian inference in pivotal trials. The philosophical message is clear: Bayesian methods are welcome. Informative priors, borrowing from external data, direct interpretation of posterior probabilities—all explicitly endorsed. The operational message is different: pre-specify everything. Bayesian methods promise learning. Regulators demand commitment.
I’ve been in meetings where “adaptive” was treated as a synonym for “smaller trial.” The assumption goes like this: if we can learn as we go, we can stop early when something works, drop arms that fail, and route patients to better treatments. Surely that means fewer patients overall.
The pharmaceutical industry is facing a cost disease. Bringing a new drug to market now routinely exceeds $2 billion, with patient recruitment standing as the single largest bottleneck. In response, we usually reach for operational fixes: faster sites, simpler protocols, digital recruitment. But there is a mathematical lever that is
