Bayesian inference and the use of posterior or posterior predictive probabilities for decision making have become increasingly popular in clinical trials. The current approach toward Bayesian clinical trials is, however, a hybrid Bayesian-frequentist approach where the design and decision criteria are assessed with respect to frequentist operating characteristics such as power and type I error rate. These operating characteristics are commonly obtained via simulation studies. In this article we propose methodology to utilize large sample theory of the posterior distribution to define simple parametric models for the sampling distribution of the Bayesian test statistics, i.e., posterior tail probabilities. The parameters of these models are then estimated using a small number of simulation scenarios, thereby refining these models to capture the sampling distribution for small to moderate sample size. The proposed approach toward assessment of operating characteristics and sample size determination can be considered as simulation-assisted rather than simulation-based and significantly reduces the computational burden for design of Bayesian trials.

翻译：贝叶斯推断及其基于后验或后验预测概率的决策方法在临床试验中日益普及。然而，当前贝叶斯临床试验采用了一种混合贝叶斯-频率学派方法，其设计与决策标准需通过频率学派操作特性（如检验功效和第一类错误率）进行评估。这些操作特性通常通过模拟研究获得。本文提出利用后验分布的大样本理论来定义贝叶斯检验统计量（即后验尾部概率）抽样分布的简单参数模型。随后，通过少量模拟场景估计这些模型的参数，从而改进模型以捕捉小至中等样本量下的抽样分布。所提出的操作特性评估与样本量确定方法可视为"模拟辅助型"而非"模拟主导型"，显著降低了贝叶斯试验设计的计算负担。