An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of tailoring variables
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用贝叶斯模型平均选择个体化变量及识别阈值的自适应富集设计
- 研究问题
- 临床试验如何从众多生物标志物中识别治疗敏感人群，并在标志物效应可能非线性时动态调整后续入组范围？
- 方法与思路
- 作者结合自由结点 B 样条与贝叶斯模型平均，在中期分析评估标志物定义的子群效应，支持提前终止或限制后续入组。
- 材料陈述的贡献
- 摘要称设计兼容分类和连续标志物，可选择个体化变量并处理复杂非线性关系，通过模拟评估运行特性并比较两种既有方法。
- 阅读关注点
- 应核对先验与样条结点设置、子群及阈值的确定规则、中期决策标准，以及适应性选择下错误率与功效的评价。
- 阅读边界
- 仅据 arXiv 预印本摘要，不能确认同行评审或期刊接收；随机化细节、阈值识别规则和模拟结果未说明，不能认定优于比较方法。
原文摘要
Precision medicine stands as a transformative approach in healthcare, offering tailored treatments that can enhance patient outcomes and reduce healthcare costs. As understanding of complex disease improves, clinical trials are being designed to detect subgroups of patients with enhanced treatment effects. Biomarker-driven adaptive enrichment designs, which enroll a general population initially and later restrict accrual to treatment-sensitive patients, are gaining popularity. Current practice often assumes either pre-trial knowledge of biomarkers defining treatment-sensitive subpopulations 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 which identifies important tailoring variables out of a larger set of candidate biomarkers. Our proposed design is equipped with a flexible modelling framework where the effects of continuous biomarkers are introduced using free knot B-splines. The parameters of interest are then estimated by marginalizing over the space of 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 due to efficacy or futility and restricting future enrollment to treatment-sensitive patients. We consider pre-categorized and continuous biomarkers, the latter of which may have complex, nonlinear relationships to the outcome and treatment effect. Using simulations, we derive the operating characteristics of our design and compare its performance to two existing approaches.