利用联合建模纵向数据与生存时间数据的适应性富集试验设计

Adaptive enrichment trial designs using joint modelling of longitudinal and time-to-event data

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STATISTICAL METHODS IN MEDICAL RESEARCH

影响因子:1.9

分区:数学3区 / 数学与计算生物学2区统计学与概率论2区卫生保健与服务3区医学：信息4区

发表日期:2024 Nov

作者: Abigail J Burdon, Richard D Baird, Thomas Jaki

DOI: 10.1177/09622802241287711

摘要

适应性富集设计允许在临床试验过程中预定义感兴趣的患者亚组进行研究。近年来，这些设计因其有望缩短试验时间并针对特定患者群体发现有效疗法而受到关注。我们描述一种考虑长期生存结果但也结合常规收集的短期生物标志物信息的富集试验方法。这些方法适用于生物标志物轨迹在亚组之间可能不同，且认为长期终点受治疗、亚组和生物标志物共同影响的情形。此方法在大多数患者至少有两个时间点的生物标志物测量时最具潜力。我们采用联合建模纵向与生存数据的方法，定义亚组筛选与停止标准，并证明家庭误差率在强烈意义下得到保护。通过模拟研究发现，与忽略纵向生物标志物观察值的研究相比，加入生物标志物信息能增强统计功效，并在中期分析时以更高概率富集真正受益的（亚）群体。该研究以转移性乳腺癌的治疗试验为背景，模拟参数值基于实际数据，其中包含每位患者的循环肿瘤DNA反复测量和HER2状态，作为纵向数据和亚组标识。

Abstract

Adaptive enrichment allows for pre-defined patient subgroups of interest to be investigated throughout the course of a clinical trial. These designs have gained attention in recent years because of their potential to shorten the trial's duration and identify effective therapies tailored to specific patient groups. We describe enrichment trials which consider long-term time-to-event outcomes but also incorporate additional short-term information from routinely collected longitudinal biomarkers. These methods are suitable for use in the setting where the trajectory of the biomarker may differ between subgroups and it is believed that the long-term endpoint is influenced by treatment, subgroup and biomarker. Methods are most promising when the majority of patients have biomarker measurements for at least two time points. We implement joint modelling of longitudinal and time-to-event data to define subgroup selection and stopping criteria and we show that the familywise error rate is protected in the strong sense. To assess the results, we perform a simulation study and find that, compared to the study where longitudinal biomarker observations are ignored, incorporating biomarker information leads to increases in power and the (sub)population which truly benefits from the experimental treatment being enriched with higher probability at the interim analysis. The investigations are motivated by a trial for the treatment of metastatic breast cancer and the parameter values for the simulation study are informed using real-world data where repeated circulating tumour DNA measurements and HER2 statuses are available for each patient and are used as our longitudinal data and subgroup identifiers, respectively.