Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines
Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines
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DOI：
10.1002/sim.7272
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发表时间：
2017-05-30
影响因子：
2
通讯作者：
Beckman, Robert A.
中科院分区：
文献类型：
--
作者：
Li, Wen;Chen, Cong;Beckman, Robert A.
个性化药物可能使具有某些预测性生物标志物特征或某些疾病类型的亚群受益。然而，在实践中设计验证性试验时，亚群中药物活性存在很大的不确定性，除非可获得可信的外部信息用于决策目的，否则采用两阶段方法进行研究是合乎逻辑的。第一阶段在中期分析中取消选择（或修剪）不表现的亚群，第二阶段在最终分析中合并剩余的亚群。两个阶段使用的终点一般可以不同。一个关键的问题是在最终分析的检验统计量和点估计的统计特性。以前的研究主要集中在I类错误控制和功率计算这样的两阶段设计。本文将研究治疗效应的估计偏倚，这隐含在此类两阶段设计中多重性控制的名义I类错误调整中。以前的工作处理的治疗效果的中间终点作为滋扰参数，以提供最保守的I型错误控制。本文采用同样的方法来探讨偏倚。该方法适用于两个先前研究的设计。在第一种设计中，具有不同生物标志物水平的患者被招募到研究中，并且假设治疗效果是有序的。中期分析的目的是确定亚群的生物标志物临界点。在第二种设计中，将具有不同肿瘤类型但具有相同生物标志物特征的患者纳入应用篮式设计的试验中。中期分析的目的是在没有治疗效果排序的情况下确定肿瘤类型的子集。封闭形式的方程提供了估计偏差以及两种设计下的方差。在不同的场景下进行模拟，以验证分析结果，证明在实践中可以正确估计的偏差。工作的例子。扩展一般自适应设计和操作的考虑进行了讨论。版权所有（c）2017约翰威利父子有限公司
A personalized medicine may benefit a subpopulation with certain predictive biomarker signatures or certain disease types. However, there is great uncertainty about drug activity in a subpopulation when designing a confirmatory trial in practice, and it is logical to take a two-stage approach with the study unless credible external information is available for decision-making purpose. The first stage deselects (or prunes) non-performing subpopulations at an interim analysis, and the second stage pools the remaining subpopulations in the final analysis. The endpoints used at the two stages can be different in general. A key issue of interest is the statistical property of the test statistics and point estimate at the final analysis. Previous research has focused on type I error control and power calculation for such two-stage designs. This manuscript will investigate estimation bias of the treatment effect, which is implicit in the adjustment of nominal type I error for multiplicity control in such two-stage designs. Previous work handles the treatment effect of an intermediate endpoint as a nuisance parameter to provide the most conservative type I error control. This manuscript takes the same approach to explore the bias. The methodology is applied to the two previously studied designs. In the first design, patients with different biomarker levels are enrolled in a study, and the treatment effect is assumed to be in an order. The goal of the interim analysis is to identify a biomarker cut-off point for the subpopulations. In the second design, patients with different tumour types but the same biomarker signature are included in a trial applying a basket design. The goal of the interim analysis is to identify a subset of tumour types in the absence of treatment effect ordering. Closed-form equations are provided for the estimation bias as well as the variance under the two designs. Simulations are conducted under various scenarios to validate the analytic results that demonstrated that the bias can be properly estimated in practice. Worked examples are presented. Extensions to general adaptive designs and operational considerations are discussed. Copyright (c) 2017 John Wiley & Sons, Ltd.