In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.

中文翻译：

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具有事件数据时间和生物标志物驱动的亚群选择的两阶段自适应设计中的点和区间估计。

在个性化医疗中，通常需要确定是所有患者还是其中一部分患者受益于治疗。我们考虑在两阶段自适应设计中进行估计，即在第一阶段从全部人群中招募患者。在第 2 阶段，患者招募仅限于根据第 1 阶段数据从实验治疗中受益的部分人群。现有的估计量使用第一阶段数据进行调整，以选择招募第二阶段患者的部分人群，以及第二阶段之后的验证性分析，不考虑事件患者结果的时间。在这项工作中，对于事件数据的时间，我们推导出了一个新的对数风险比的渐近无偏估计量和一个具有良好覆盖概率和上限低于真实值的概率的新区间估计量。估计量适用于基于单个或多个生物标志物的几种选择规则，这些生物标志物可以是分类的或连续的。