We consider a Bayesian framework for estimating the sample size of a clinical trial. The new approach, called BESS, is built upon three pillars: Sample size of the trial, Evidence from the observed data, and Confidence of the final decision in the posterior inference. It uses a simple logic of "given the evidence from data, a specific sample size can achieve a degree of confidence in trial success." The key distinction between BESS and standard sample size estimation (SSE) is that SSE, typically based on Frequentist inference, specifies the true parameters values in its calculation to achieve properties under repeated sampling while BESS assumes possible outcome from the observed data to achieve high posterior probabilities of trial success. As a result, the calibration of the sample size is directly based on the probability of making a correct decision rather than type I or type II error rates. We demonstrate that BESS leads to a more interpretable statement for investigators, and can easily accommodates prior information as well as sample size re-estimation. We explore its performance in comparison to the standard SSE and demonstrate its usage through a case study of oncology optimization trial. An R tool is available at https://ccte.uchicago.edu/BESS.

翻译：我们提出了一种用于临床试验样本量估计的贝叶斯框架。这一名为BESS的新方法建立在三大支柱之上：试验样本量、观测数据的证据以及后验推断中最终决策的置信度。其核心逻辑是“在给定数据证据的前提下，特定样本量能够达到试验成功所需的一定置信度”。BESS与标准样本量估计方法的关键区别在于：标准方法通常基于频率学推断，在计算中需设定真实参数值以实现重复抽样下的统计性质；而BESS则基于观测数据可能产生的结果，以实现试验成功的高后验概率。因此，样本量的校准直接基于做出正确决策的概率，而非Ⅰ类或Ⅱ类错误率。我们证明BESS能为研究者提供更具可解释性的结论，并能灵活纳入先验信息及进行样本量重新估计。通过与标准样本量估计方法的性能对比，我们探讨了该方法的优势，并通过肿瘤优化试验的案例研究展示了其应用。相关R工具可在https://ccte.uchicago.edu/BESS获取。