Maleyeff Lara, Golchi Shirin, Moodie Erica E M, Hudson Marie
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, QC H3A 1G1, Canada.
Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae141.
Precision medicine is transforming healthcare by offering tailored treatments that enhance patient outcomes and reduce costs. As our understanding of complex diseases improves, clinical trials increasingly aim to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which initially enroll a broad population and later restrict to treatment-sensitive patients, are gaining popularity. However, current practice often assumes either pre-trial knowledge of biomarkers or a simple, linear relationship between continuous markers and treatment effectiveness. Motivated by a trial studying rheumatoid arthritis treatment, we propose a Bayesian adaptive enrichment design to identify predictive variables from a larger set of candidate biomarkers. Our approach uses a flexible modeling framework where the effects of continuous biomarkers are represented using free knot B-splines. We then estimate key parameters by marginalizing over all possible variable combinations using Bayesian model averaging. At interim analyses, we assess whether a biomarker-defined subgroup has enhanced or reduced treatment effects, allowing for early termination for efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider both pre-categorized and continuous biomarkers, the latter potentially having complex, nonlinear relationships to the outcome and treatment effect. Through simulations, we derive the operating characteristics of our design and compare its performance to existing methods.
精准医学正在通过提供量身定制的治疗方法来改变医疗保健，这些方法可改善患者治疗效果并降低成本。随着我们对复杂疾病的理解不断提高，临床试验越来越旨在检测出治疗效果增强的患者亚组。生物标志物驱动的适应性富集设计最初纳入广泛人群，随后限制为对治疗敏感的患者，这种设计越来越受欢迎。然而，当前的做法通常要么假设在试验前就了解生物标志物，要么假设连续标志物与治疗效果之间存在简单的线性关系。受一项研究类风湿性关节炎治疗的试验启发，我们提出了一种贝叶斯适应性富集设计，以从更大的一组候选生物标志物中识别预测变量。我们的方法使用了一个灵活的建模框架，其中连续生物标志物的效应使用自由节点B样条来表示。然后，我们通过使用贝叶斯模型平均法对所有可能的变量组合进行边缘化来估计关键参数。在中期分析中，我们评估由生物标志物定义的亚组的治疗效果是增强还是降低，从而允许因疗效或无效而提前终止试验，并将未来的入组限制为对治疗敏感的患者。我们考虑了预先分类的生物标志物和连续生物标志物，后者与结局和治疗效果可能存在复杂的非线性关系。通过模拟，我们得出了我们设计的操作特征，并将其性能与现有方法进行了比较。
