标题： 一种高效的设计贝叶斯平台试验的方法 显示英文标题

标题： An Efficient Approach to Design Bayesian Platform Trials

摘要： 平台试验将多种实验治疗与共同对照组（和/或彼此之间）进行比较，这通常可以缩短试验时间和样本量。 贝叶斯平台设计提供了多种实际优势，包括使用后验概率灵活地添加或移除实验臂，以及纳入先验/外部信息。 监管机构要求通过蒙特卡洛模拟估计后验概率的抽样分布来评估贝叶斯设计的操作特性。 对于所有考虑的设计配置重复此模拟过程在计算上是密集的，尤其是在具有复杂中期决策程序的平台试验中。 在本文中，我们提出了一种高效的方法来评估操作特性并确定贝叶斯平台试验的样本量以及其他设计参数。 我们证明了理论结果，使我们能够仅使用在两个样本量下进行的模拟来建模多个终点和试验阶段的后验概率的联合抽样分布。 这项工作受到SSTARLET试验设计复杂性的启发，SSTARLET是一项正在进行的用于结核病预防疗法的贝叶斯自适应平台试验（ClinicalTrials.gov ID: NCT06498414）。 我们提出的设计方法不仅计算效率高，而且能够容纳如SSTARLET中遇到的复杂现实试验约束。 显示英文摘要

摘要： Platform trials evaluate multiple experimental treatments against a common control group (and/or against each other), which often reduces the trial duration and sample size. Bayesian platform designs offer several practical advantages, including the flexible addition or removal of experimental arms using posterior probabilities and the incorporation of prior/external information. Regulatory agencies require that the operating characteristics of Bayesian designs are assessed by estimating the sampling distribution of posterior probabilities via Monte Carlo simulation. It is computationally intensive to repeat this simulation process for all design configurations considered, particularly for platform trials with complex interim decision procedures. In this paper, we propose an efficient method to assess operating characteristics and determine sample sizes as well as other design parameters for Bayesian platform trials. We prove theoretical results that allow us to model the joint sampling distribution of posterior probabilities across multiple endpoints and trial stages using simulations conducted at only two sample sizes. This work is motivated by design complexities in the SSTARLET trial, an ongoing Bayesian adaptive platform trial for tuberculosis preventive therapies (ClinicalTrials.gov ID: NCT06498414). Our proposed design method is not only computationally efficient but also capable of accommodating intricate, real-world trial constraints like those encountered in SSTARLET.