This study examines the application of Bayesian approach in the context of clinical trials, emphasizing their increasing importance in contemporary biomedical research. While conventional frequentist approach provides a foundational basis for analysis, it often lacks the flexibility to integrate prior knowledge, which can constrain its effectiveness in adaptive settings. In contrast, Bayesian methods enable continual refinement of statistical inferences through the assimilation of accumulating evidence, thereby supporting more informed decision-making and improving the reliability of trial findings. This paper also considers persistent challenges in clinical investigations, including replication difficulties and the misinterpretation of statistical results, suggesting that Bayesian strategies may offer a path toward enhanced analytical robustness. Moreover, discrete probability models, specifically the Binomial, Poisson, and Negative Binomial distributions are explored for their suitability in modeling clinical endpoints, particularly in trials involving binary responses or data with overdispersion. The discussion further incorporates Bayesian networks and Bayesian estimation techniques, with a comparative evaluation against maximum likelihood estimation to elucidate differences in inferential behavior and practical implementation.

翻译：本研究探讨了贝叶斯方法在临床试验中的应用，强调其在当代生物医学研究中日益增长的重要性。尽管传统的频率主义方法为分析提供了基础框架，但其往往缺乏整合先验知识的灵活性，这可能限制其在适应性试验环境中的有效性。相比之下，贝叶斯方法能够通过不断吸收累积证据来持续优化统计推断，从而支持更明智的决策制定并提高试验结果的可靠性。本文还探讨了临床研究中持续存在的挑战，包括结果复现困难和统计结果的误读问题，指出贝叶斯策略可能为提升分析稳健性提供一条路径。此外，研究探讨了离散概率模型——特别是二项分布、泊松分布和负二项分布在临床终点建模中的适用性，尤其适用于涉及二元响应或过度离散数据的试验。讨论进一步纳入了贝叶斯网络和贝叶斯估计技术，并通过与最大似然估计的比较评估，阐明了二者在推断行为和实践应用方面的差异。