We consider a Bayesian estimator of sample size (BESS) and an application to oncology dose optimization clinical trials. BESS is built upon three pillars, 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 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 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. BESS can be applied to general hypothesis tests. An R tool is 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）及其在肿瘤学剂量优化临床试验中的应用。BESS建立在三个支柱之上：样本量、观测数据的证据以及后验推论的置信度。它采用一个简单的逻辑：“基于数据提供的证据，特定样本量可以在后验推论中达到一定程度的置信度。”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，在其计算中指定真实参数值，而BESS则假设来自观测数据的可能结果。因此，样本量的校准并非基于I型或II型错误率，而是基于后验概率。我们证明，BESS能为研究者提供更具可解释性的结论，并能够轻松纳入先验信息以及样本量重新估计。我们通过与标准SSE的对比探讨其性能，并通过一个肿瘤学优化试验的案例研究展示其应用。BESS可适用于一般假设检验。相关R工具可访问https://ccte.uchicago.edu/BESS获取。