In recent years, basket trials, which enable the evaluation of an experimental therapy across multiple tumor types within a single protocol, have gained prominence in early-phase oncology development. Unlike traditional trials, where each tumor type is evaluated separately with limited sample size, basket trials offer the advantage of borrowing information across various tumor types. However, a key challenge in designing basket trials lies in dynamically determining the extent of information borrowing across tumor types to enhance statistical power while maintaining an acceptable type I error rate. In this paper, we propose a local power prior framework that includes a 3-component borrowing mechanism with explicit model interpretation. Unlike many existing Bayesian methods that require Markov Chain Monte Carlo (MCMC) sampling, the proposed framework offers a closed-form solution, eliminating the time-consuming nature of MCMC in large-scale simulations for evaluating operating characteristics. Extensive simulations have been conducted and demonstrated a good performance of the proposal method comparable to the other complex methods. The significantly shortened computation time further underscores the practical utility in the context of basket trials.

翻译：近年来，篮式试验因能够在单一方案中评估实验性疗法在多种肿瘤类型中的疗效，在肿瘤早期开发中备受关注。与传统试验中每种肿瘤类型需以有限样本量分别评估不同，篮式试验的优势在于可跨肿瘤类型借用信息。然而，篮式试验设计的核心挑战在于：如何动态确定跨肿瘤类型的信息借用程度，以在维持可接受的I类错误率的同时提升统计效能。本文提出了一种包含三组分借用机制且具有明确模型解释的局部幂先验框架。与许多需要马尔可夫链蒙特卡洛（MCMC）采样的现有贝叶斯方法不同，该框架提供闭式解，消除了在大规模模拟中评估操作特征时MCMC的耗时问题。大量模拟表明，所提方法的优良性能与其它复杂方法相当。显著缩短的计算时间进一步凸显了其在篮式试验中的实用价值。