Applications of the Bayesian Methods in Clinical Trials with Large Sample Size

Bayesian methods have gained prominence as robust alternatives to traditional frequentist approaches in the design and analysis of clinical trials, particularly those involving large sample sizes. While frequentist methods rely on fixed hypotheses and long-run probability interpretations, Bayesian frameworks incorporate prior knowledge and allow for iterative updating of evidence as data accrue. This adaptability facilitates the implementation of innovative trial structures such as adaptive designs and platform trials, while also supporting real-time decision-making. The integration of historical or external data within Bayesian analyses further enhances trial efficiency, especially in interim monitoring and interpretation of treatment effects. Despite these advantages, the broader adoption of Bayesian methods in confirmatory Phase III trials remains constrained by computational demands, challenges in the elicitation and justification of prior distributions, and varying degrees of regulatory acceptance. Nevertheless, advancements in high-performance computing, the emergence of hybrid Bayesian–frequentist methodologies, and growing regulatory engagement underscore a progressive shift toward broader implementation. This paper critically examines the evolution, methodological underpinnings, and practical applications of Bayesian approaches in large-sample clinical trials, offering a comparative assessment with frequentist methods. It also outlines key benefits, prevailing limitations, and potential trajectories for future research and regulatory alignment. These insights contribute to ongoing discourse on optimizing trial design for enhanced scientific rigor, ethical standards, and decision-making in evidence-based medicine.