Regulators are generally really conservative. Spiegelhalter et al. already wrote a fantastic textbook on Bayesian methods for trial analysis back in 2004. It is a great synthesis, and used by statisticians from other fields. I have seen it quoted in e.g. DeepMind presentations.

Bayesian methods enable using prior information and fancy adaptive trial designs, which have the potential to make drug development much cheaper. It's also easier to factor in utility functions and look at cost:benefit. But things move slowly.

They are used in some trials, but not the norm, and require rowing against the stream. This is actually a great niche for a startup. Leveraging prior knowledge to make target discovery, pre-clinical, and clinical trials more adaptive and efficient.

Journals are also conservative. But Bayesian methods are not that niche anymore. Even mainstream journals such as Nature or Nature Genetics include Bayesian-specific items in their standard submission checklists [1]. For example, they require you to indicate prior choice and MCMC parameters.

[1] https://www.nature.com/documents/nr-reporting-summary-flat.p...

If choosing a Bayesian approach in a clinical trial can reduce the number of recruited subjects, I would imagine the pharma industry is strongly incentivized to adopt it.

BOIN (Bayesian Optimal Interval) trial design is already very common in Phase I studies across therapeutic areas.

Biggest benefit I see from this guidance is support for rare disease trials, where patients are harder to find. Also regulatory bodies will be taking a closer look at stratification groups when it comes time for approval, so sponsors need to keep a super close eye on ensuring even enrollment and preventing misstrats.

Please watch https://www.youtube.com/shorts/2TWu_4gd-ik

Been used since the 90s.