Adaptive enrichment allows for pre-defined patient subgroups of interest to be investigated throughout the course of a clinical trial. Many trials which measure a long-term time-to-event endpoint often also routinely collect repeated measures on biomarkers which may be predictive of the primary endpoint. Although these data may not be leveraged directly to support subgroup selection decisions and early stopping decisions, we aim to make greater use of these data to increase efficiency and improve interim decision making. In this work, we present a joint model for longitudinal and time-to-event data and two methods for creating standardised statistics based on this joint model. We can use the estimates to define enrichment rules and efficacy and futility early stopping rules for a flexible efficient clinical trial with possible enrichment. Under this framework, we show asymptotically that the familywise error rate is protected in the strong sense. To assess the results, we consider a trial for the treatment of metastatic breast cancer where repeated ctDNA measurements are available and the subgroup criteria is defined by patients' ER and HER2 status. Using simulation, we show that incorporating biomarker information leads to accurate subgroup identification and increases in power.

翻译：自适应富集允许在临床试验过程中对预定义的患者亚组进行研究。许多测量长期事件发生时间终点指标的试验，通常会常规收集可能对主要终点具有预测作用的生物标志物重复测量数据。尽管这些数据可能未直接用于支持亚组选择决策和早期停止决策，但我们旨在更充分地利用这些数据以提高试验效率并改进中期决策。本研究提出了一种纵向数据与事件发生时间数据的联合模型，以及基于该模型构建标准化统计量的两种方法。利用这些估计值，我们可定义富集规则以及有效性和无效性早期停止规则，从而设计灵活高效的、可能包含富集策略的临床试验。在此框架下，我们从渐近角度证明家族错误率在强意义下得到控制。为评估结果，我们考虑一项转移性乳腺癌治疗试验，该试验可获取重复ctDNA测量数据，且亚组标准由患者的ER和HER2状态定义。模拟研究表明，纳入生物标志物信息可提高亚组识别的准确性并增加统计功效。