Halabi Susan, Choi Taehwa, Garrett-Mayer Elizabeth, Schilsky Richard L, Trippa Lorenzo
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Duke Cancer Institute, Durham, NC, USA.
Clin Trials. 2025 Aug 21:17407745251358233. doi: 10.1177/17407745251358233.
BACKGROUND/AIM: Basket designs have been utilized in recent oncology clinical trials due to an increased interest in precision medicine. One current successful basket trial is the American Society for Clinical Oncology Targeted Agent and Profiling Utilization Registry (TAPUR) study, a pragmatic phase II trial where patients are matched based on their tumor genomic profile to treatments that target specific genomic alterations. Despite its success, recruiting patients with rare genomic alterations remains challenging. This study aims to introduce and evaluate a Bayesian approach for integrating data from ongoing independent basket trials that share similar primary aims to improve interim decisions and final analyses and reduce necessary to evaluate treatments.
We introduce a Bayesian two-stage phase II single-arm trial specifically for rare cancers utilizing a hierarchical Bayesian random effects model that incorporate data from ongoing trials. We compare this approach with the standard Simon two-stage design through extensive numerical simulations and apply it to real-world scenarios.
Simulation results demonstrate that in rare populations our Bayesian approach has attractive operating characteristics. The simulations show that our approach performs well across a broad set of scenarios with fixed and variable numbers of trials.
Our proposed Bayesian two-stage approach effectively integrates data from multiple ongoing basket trials, enhancing the ability to recruit and analyze patients with rare genomic alterations. This approach improves the timing of interim decision-making and final analysis, making it a valuable tool for trials with slow accrual rates.
背景/目的：由于对精准医学的兴趣增加，篮子设计已被用于近期的肿瘤学临床试验。当前一项成功的篮子试验是美国临床肿瘤学会靶向药物与分析利用登记处（TAPUR）研究，这是一项务实的II期试验，患者根据其肿瘤基因组特征与针对特定基因组改变的治疗方法进行匹配。尽管取得了成功，但招募具有罕见基因组改变的患者仍然具有挑战性。本研究旨在引入并评估一种贝叶斯方法，用于整合来自正在进行的具有相似主要目标的独立篮子试验的数据，以改善中期决策和最终分析，并减少评估治疗所需的资源。
我们引入一种贝叶斯两阶段II期单臂试验，专门针对罕见癌症，利用分层贝叶斯随机效应模型整合来自正在进行的试验的数据。我们通过广泛的数值模拟将这种方法与标准的西蒙两阶段设计进行比较，并将其应用于实际场景。
模拟结果表明，在罕见人群中，我们的贝叶斯方法具有吸引人的操作特性。模拟显示，我们的方法在固定和可变试验数量的广泛场景中表现良好。
我们提出的贝叶斯两阶段方法有效地整合了来自多个正在进行的篮子试验的数据，增强了招募和分析具有罕见基因组改变患者的能力。这种方法改善了中期决策和最终分析的时机，使其成为招募率较低的试验的有价值工具。
