Bayesian methods for designing clinical trials have become more common, and yet these Bayesian designs are almost always evaluated by frequentist criteria. For example, a trial may be designed to stop early 95% of the time under some bad scenario and stop no more than 20% of the time under some good scenario.
These criteria are arbitrary, since the “good” and “bad” scenarios are arbitrary, and because the stopping probability requirements of 95% and 20% are arbitrary. Still, there’s an idea in lurking in the background that in every design there must be something that is shown to happen no more than 5% of the time.
It takes a great deal of effort to design Bayesian methods with desired frequentist properties. It’s an inverse problem, searching for the parameters in a high-dimensional design space, usually via lengthy simulation, that cause the method to satisfy some criteria. Of course frequentist methods satisfy frequentist criteria by design and so meet these criteria with far less effort. It’s rare to see the tables turned, evaluating frequentist methods by Bayesian criteria.
Sometimes the effort to beat frequentist designs at their own game is futile because the frequentist designs are optimal by their own criteria. More often, however, the Bayesian and frequentist methods being compared are not direct competitors but only analogs. The aim in this case is to match the frequentist method’s operating characteristics by one criterion while doing better by a new criterion.
Sometimes a Bayesian method can be shown to have better frequentist operating characteristics than its frequentist counterpart. This puts dogmatic frequentists in the awkward position of admitting that what they see as an unjustified approach to statistics has nevertheless produced a superior product. Some anti-Bayesians are fine with this, happy to have a procedure with better frequentist properties, even though it happened to be discovered via a process they view as illegitimate.
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