  Bayesian adaptive designs have gained popularity in all phases of clinical
trials with numerous new developments in the past few decades. During the
COVID-19 pandemic, the need to establish evidence for the effectiveness of
vaccines, therapeutic treatments and policies that could resolve or control the
crisis emphasized the advantages offered by efficient and flexible clinical
trial designs. In many COVID-19 clinical trials, due to the high level of
uncertainty, Bayesian adaptive designs were considered advantageous. Designing
Bayesian adaptive trials, however, requires extensive simulation studies that
are generally considered challenging, particularly in time-sensitive settings
such as a pandemic. In this article, we propose a set of methods for efficient
estimation and uncertainty quantification for design operating characteristics
of Bayesian adaptive trials. Specifically, we model the sampling distribution
of Bayesian probability statements that are commonly used as the basis of
decision making. To showcase the implementation and performance of the proposed
approach, we use a clinical trial design with an ordinal disease-progression
scale endpoint that was popular among COVID-19 trial. However, the proposed
methodology may be applied generally in clinical trial context where design
operating characteristics cannot be obtained analytically.
