Basket trials in oncology enroll multiple patients with cancer harboring identical gene alterations and evaluate their response to targeted therapies across cancer types. Several existing methods have extended a Bayesian hierarchical model borrowing information on the response rates in different cancer types to account for the heterogeneity of drug effects. However, these methods rely on several pre-specified parameters to account for the heterogeneity of response rates among different cancer types. Here, we propose a novel Bayesian under-parameterized basket design with a unit information prior (BUPD) that uses only one (or two) pre-specified parameters to control the amount of information borrowed among cancer types, considering the heterogeneity of response rates. BUPD adapts the unit information prior approach, originally developed for borrowing information from historical clinical trial data, to enable mutual information borrowing between two cancer types. BUPD enables flexible controls of the type 1 error rate and power by explicitly specifying the strength of borrowing while providing interpretable estimations of response rates. Simulation studies revealed that BUPD reduced the type 1 error rate in scenarios with few ineffective cancer types and improved the power in scenarios with few effective cancer types better than five existing methods. This study also illustrated the efficiency of BUPD using response rates from a real basket trial.

翻译：肿瘤学中的篮式试验招募具有相同基因改变的多种癌症患者，并评估靶向疗法在不同癌症类型中的疗效反应。现有多种方法通过扩展贝叶斯层次模型，借用在不同癌症类型中的反应率信息，以考虑药物效应的异质性。然而，这些方法依赖于多个预先指定的参数来解释不同癌症类型间反应率的异质性。本文提出了一种新颖的采用单位信息先验的贝叶斯欠参数化篮式设计（BUPD），该设计仅使用一个（或两个）预先指定的参数来控制癌症类型间的信息借用程度，同时考虑反应率的异质性。BUPD采用了最初为从历史临床试验数据中借用信息而开发的单位信息先验方法，使其能够在两种癌症类型之间实现相互信息借用。BUPD通过明确指定借用强度，灵活控制第一类错误率和功效，同时提供可解释的反应率估计。模拟研究表明，在少数癌症类型无效的场景中，BUPD降低了第一类错误率；在少数癌症类型有效的场景中，其功效优于五种现有方法。本研究还通过一个真实篮式试验的反应率数据，展示了BUPD的效率。