标题： 通过贝叶斯自助法评估贝叶斯预期功率 显示英文标题

标题： Assessment of Bayesian Expected Power via Bayesian Bootstrap

摘要： 贝叶斯预期功效（BEP）在样本量确定和评估未来试验成功率（POS）方面越来越受欢迎。 与假设单一参数集的传统功效相比，BEP 考虑了功效分析中所假设参数周围的不确定性，因此更具鲁棒性。 目前评估BEP的方法通常基于参数框架，在试点数据上施加模型以从参数的后验分布中推导和采样。 基于模型的方法实现起来在解析上具有挑战性，并且计算成本高昂，特别是对于多变量数据集；此外，如果模型设定错误，还可能导致误导性的BEP。 我们提出了一种基于贝叶斯自助法（BBS）的方法，可以在个体水平试点数据存在的情况下模拟未来的试验，从而计算经验BEP。 BBS方法无需关于先验数据分布的假设，避免了获取参数后验分布相关的解析和计算复杂性。 来自多个试点研究的信息也易于组合。 我们还提出了双自助法（BS2），作为BBS的频率学派对应方法，它具有类似的特性，并实现了与BBS相同的目标。 进行了仿真研究和案例研究，以展示BBS和BS2技术的实施，并将BEP结果与基于模型的方法进行比较。 显示英文摘要

摘要： The Bayesian expected power (BEP) has become increasingly popular in sample size determination and assessment of the probability of success (POS) for a future trial. The BEP takes into consideration the uncertainty around the parameters assumed by a power analysis and is thus more robust compared to the traditional power that assumes a single set of parameters. Current methods for assessing BEP are often based in a parametric framework by imposing a model on the pilot data to derive and sample from the posterior distributions of the parameters. Implementation of the model-based approaches can be analytically challenging and computationally costly especially for multivariate data sets; it also runs the risk of generating misleading BEP if the model is mis-specified. We propose an approach based on the Bayesian bootstrap technique (BBS) to simulate future trials in the presence of individual-level pilot data, based on which the empirical BEP can be calculated. The BBS approach is model-free with no assumptions about the distribution of the prior data and circumvents the analytical and computational complexity associated with obtaining the posterior distribution of the parameters. Information from multiple pilot studies is also straightforward to combine. We also propose the double bootstrap (BS2), a frequentist counterpart to the BBS, that shares similar properties and achieves the same goal as the BBS for BEP assessment. Simulation studies and case studies are presented to demonstrate the implementation of the BBS and BS2 techniques and to compare the BEP results with model-based approaches.