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Practical employment of Bayesian trial designs has been rare, in part due tothe regulators' requirement to calibrate such designs with an upper bound forType 1 error rate. This has led to an internally inconsistent hybridmethodology, where important advantages from applying the Bayesian principlesare lost. To present an alternative approach, we consider the prototype case ofa 2-arm superiority trial with binary outcomes. The design is adaptive,applying block randomization for treatment assignment and using error tolerancecriteria based on sequentially updated posterior probabilities, to concludeefficacy of the experimental treatment or futility of the trial. The methodalso contains the option of applying a decision rule for terminating the trialearly if the predicted costs from continuing would exceed the correspondinggains. We propose that the control of Type 1 error rate be replaced by acontrol of false discovery rate (FDR), a concept that lends itself to bothfrequentist and Bayesian interpretations. Importantly, if the regulators andthe investigators can agree on a consensus distribution to represent theirshared understanding on the effect sizes, the selected level of risk toleranceagainst false conclusions during the data analysis will also be a valid boundfor the FDR. The approach can lower the ultimately unnecessary barriers fromthe practical application of Bayesian trial designs. This can lead to moreflexible experimental protocols and more efficient use of trial data whilestill effectively guarding against falsely claimed discoveries.

Towards reaching a consensus in Bayesian trial designs: the case of 2-arm trials