The problem with this approach is that we can only observe ONE level of treatment effectiveness, i.e., the level of treatment effectiveness that the treatment actually possesses. All other possible levels of effectiveness are entirely hypothetical. There's no data about all these other possible levels of effectiveness because they don't occur in reality. So the data cannot possibly tell you anything about how likely is the observed outcome, because the observed outcome is the only outcome that you observe. I

This criticism was made over 100 years ago, and Bayesians still don't have an answer. They just keep going as if nothing happened, but the reality is their methodology is utterly and fatally flawed.

the reason it has been limited to those cases is

drug development, today, is constrained by commercialization.

all four categories listed - ebola, sars-cov-2, pediatric and rare disease drugs - each for their own reason, have low commercialization risk, so if they are scientifically robust solutions, there is ROI.

most drugs in development today are new indications for existing molecules, generally in oncology, also because these are the most favorable conditions for commercialization.

commercialization, commercialization, commercialization. people don't want innovations in drug approval, nobody is clamoring for that. they want innovations in commercialization.

i don't know if bayesian methods will make trials a lot cheaper. and that's their problem! there are already a lot of very smart people working on this issue, and they have litigated to death all the objective facts.