Defining the target population based on predictive biomarkers plays an important role during clinical development. After establishing a relationship between a biomarker candidate and response to treatment in exploratory phases, a subsequent confirmatory trial ideally involves only subjects with high potential of benefiting from the new compound. In order to identify those subjects in case of a continuous biomarker, a cut-off is needed. Usually, a cut-off is chosen that resulted in a subgroup with a large observed treatment effect in an exploratory trial. However, such a data-driven selection may lead to overoptimistic expectations for the subsequent confirmatory trial. Treatment effect estimates, probability of success, and posterior probabilities are useful measures for deciding whether or not to conduct a confirmatory trial enrolling the biomarker-defined population. These measures need to be adjusted for selection bias. We extend previously introduced Approximate Bayesian Computation techniques for adjustment of subgroup selection bias to a time-to-event setting with cut-off selection. Challenges in this setting are that treatment effects become time-dependent and that subsets are defined by the biomarker distribution. Simulation studies show that the proposed method provides adjusted statistical measures which are superior to naïve Maximum Likelihood estimators as well as simple shrinkage estimators.

中文翻译：

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对具有生存终点的随机临床试验中探索性截止选择的调整

基于预测性生物标志物定义目标人群在临床开发过程中发挥着重要作用。在探索阶段建立候选生物标志物与治疗反应之间的关系后，理想情况下，随后的验证性试验仅涉及具有从新化合物中受益的高潜力的受试者。为了在连续生物标志物的情况下识别那些受试者，需要截止。通常，选择的临界值导致在探索性试验中观察到的治疗效果较大的亚组。然而，这种数据驱动的选择可能会导致对后续验证性试验的过度乐观预期。治疗效果估计、成功概率、后验概率是决定是否进行纳入生物标志物定义人群的验证性试验的有用指标。这些措施需要针对选择偏差进行调整。我们将之前介绍的近似贝叶斯计算技术扩展到使用截止选择的时间到事件设置来调整子组选择偏差。这种情况下的挑战是治疗效果变得依赖于时间，并且子集由生物标志物分布定义。模拟研究表明，所提出的方法提供了优于朴素最大似然估计量和简单收缩估计量的调整后的统计量度。我们将之前介绍的近似贝叶斯计算技术扩展到使用截止选择的时间到事件设置来调整子组选择偏差。这种情况下的挑战是治疗效果变得依赖于时间，并且子集由生物标志物分布定义。模拟研究表明，所提出的方法提供了优于朴素最大似然估计量和简单收缩估计量的调整后的统计量度。我们将之前介绍的近似贝叶斯计算技术扩展到使用截止选择的时间到事件设置来调整子组选择偏差。这种情况下的挑战是治疗效果变得依赖于时间，并且子集由生物标志物分布定义。模拟研究表明，所提出的方法提供了优于朴素最大似然估计量和简单收缩估计量的调整后的统计量度。