A Bayesian Discrete Framework for Enhancing Decision-Making Processes in Clinical Trial Designs and Evaluations
This study addresses the limitations of conventional frequentist approaches in effectively incorporating prior knowledge, which constrains adaptive decision-making and reliability in clinical trials. The authors propose a Bayesian framework tailored for discrete probability distributions—such as binomial, Poisson, and negative binomial—to model binary responses and overdispersed clinical endpoints using Bayesian networks. By continuously integrating accumulating evidence, the framework dynamically optimizes trial design and evaluation. Compared to maximum likelihood estimation, this approach demonstrates greater flexibility and robustness in both inferential behavior and practical performance, substantially enhancing decision quality while mitigating misinterpretation of results and reproducibility challenges.