Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation
Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation
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DOI：
10.1002/sim.6209
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发表时间：
2014-10-15
影响因子：
2
通讯作者：
Sugitani, Yasuo
中科院分区：
文献类型：
--
作者：
Morita, Satoshi;Yamamoto, Hideharu;Sugitani, Yasuo
将生物标志物纳入抗癌药物开发的益处和挑战已被越来越多地讨论。在许多情况下，患者的敏感亚群是根据临床前数据和/或回顾性分析临床试验数据确定的。对敏感亚群患者的前瞻性探索可能使我们能够有效地开发出明确有效的治疗方法，从而加速药物开发并降低开发成本。我们考虑开发一种新的分子靶向治疗癌症患者。鉴于在I期研究中观察到的初步但有希望的疗效数据，可能值得设计一个旨在确定敏感亚群的II期临床试验。为了实现这一目标，我们提出了一个贝叶斯随机II期临床试验设计，其中包括一个生物标志物，该生物标志物是在分级量表上测量的。我们比较了两种贝叶斯方法，一种基于亚组分析，另一种基于回归模型，以分析时间到事件的终点，如无进展生存期（PFS）时间。这两种方法基本上估计了生物标志物亚组中PFS风险比的贝叶斯后验概率。广泛的模拟研究评估了这些方法的操作特征，包括在广泛的临床场景下正确识别所需亚群的概率。我们还研究了亚组人口比例对方法操作特征的影响。虽然这两种方法的性能取决于治疗效果的分布和患者亚组的人口比例，但基于回归的方法显示出更有利的操作特征。版权所有：John Wiley & Sons， Ltd
The benefits and challenges of incorporating biomarkers into the development of anticancer agents have been increasingly discussed. In many cases, a sensitive subpopulation of patients is determined based on preclinical data and/or by retrospectively analyzing clinical trial data. Prospective exploration of sensitive subpopulations of patients may enable us to efficiently develop definitively effective treatments, resulting in accelerated drug development and a reduction in development costs. We consider the development of a new molecular-targeted treatment in cancer patients. Given preliminary but promising efficacy data observed in a phase I study, it may be worth designing a phase II clinical trial that aims to identify a sensitive subpopulation. In order to achieve this goal, we propose a Bayesian randomized phase II clinical trial design incorporating a biomarker that is measured on a graded scale. We compare two Bayesian methods, one based on subgroup analysis and the other on a regression model, to analyze a time-to-event endpoint such as progression-free survival (PFS) time. The two methods basically estimate Bayesian posterior probabilities of PFS hazard ratios in biomarker subgroups. Extensive simulation studies evaluate these methods' operating characteristics, including the correct identification probabilities of the desired subpopulation under a wide range of clinical scenarios. We also examine the impact of subgroup population proportions on the methods' operating characteristics. Although both methods' performance depends on the distribution of treatment effect and the population proportions across patient subgroups, the regression-based method shows more favorable operating characteristics. Copyright (C) 2014 John Wiley & Sons, Ltd.