We consider a Bayesian estimator of sample size (BESS) and an application to oncology dose optimization clinical trials. BESS is built upon balancing a trio of Sample size, Evidence from observed data, and Confidence in posterior inference. It uses a simple logic of "given the evidence from data, a specific sample size can achieve a degree of confidence in the posterior inference." 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 while BESS assumes a possible outcome from the observed data. As a result, the calibration of the sample size is not based on Type I or Type II error rates, but on posterior probabilities. We argue 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 SSE and demonstrate its usage through a case study of oncology optimization trial. BESS can be applied to general hypothesis tests. R functions are available at https://ccte.uchicago.edu/bess.

翻译：我们提出了一种样本量的贝叶斯估计量（BESS），并将其应用于肿瘤学剂量优化临床试验。BESS 建立在平衡样本量、观测数据证据及后验推断置信度三要素的基础上，其核心理念是“给定数据中的证据，特定样本量可在后验推断中达到一定置信度”。BESS 与标准样本量估计（SSE）的关键区别在于：SSE 通常基于频率学派推断，需在计算中指定真实参数值；而 BESS 则假设观测数据的可能结果。因此，样本量的校准并非依据第一类或第二类错误率，而是基于后验概率。我们认为，BESS 能为研究者提供更具可解释性的结论，并可灵活整合先验信息及样本量重估计。我们通过与 SSE 的性能对比分析，并结合肿瘤学优化试验的案例研究，展示了其实用性。BESS 可适用于一般性假设检验。相关 R 函数已发布于 https://ccte.uchicago.edu/bess。