有治疗偏好的SMART:由STAR*D激发的实用SMART设计和方法
Sarah Medley1, Marianthie Wank1, Roy N Tamura2
1Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America.
Contemporary clinical trials
|November 26, 2025
概括
一个新的部分随机化,患者偏好SMART (PRPP-SMART) 设计和权重和复制回归模型 (WRRMs) 有效估计慢性疾病的动态治疗方案 (DTRs). 这种方法改进了传统的试验,因为它结合了患者的偏好,以便更好地招募和保留患者.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 个性化医疗是个性化的医疗.
背景情况:
- 慢性疾病需要适应性治疗指南,称为动态治疗方案 (DTR).
- 顺序,多重分配,随机试验 (SMART) 对于估计DTR是理想的,但由于患者偏好而面临招聘挑战.
- 现有的方法难以将随机和非随机参与者的数据整合到考虑偏好的试验中.
研究的目的:
- 评估权重和复制回归模型 (WRRMs) 在部分随机化,患者偏好SMART (PRPP-SMART) 设计中估计DTR,具有连续结果.
- 在不同患者偏好率和结果分布中评估WRRM的稳定性.
- 为了证明PRPP-SMART和WRRMs的应用,使用来自STAR*D试验的大型抑郁障碍的数据进行调整.
主要方法:
- 使用了部分随机的患者偏好SMART (PRPP-SMART) 设计,该设计仅随机选择无关紧要的参与者.
- 采用加权和复制回归模型 (WRRMs) 来结合随机和非随机参与者的数据.
- 评估了连续结果的WRRM,测试不同偏好率和结果分布的性能.
主要成果:
- 在PRPP-SMART框架内,WRRMs在估计持续结果的DTR效应方面表现强.
- 该方法的准确性在不同患者偏好水平和结果分布中是一致的.
- 使用STAR*D适应数据的说明性分析展示了PRPP-SMART在实用临床试验中的潜力.
结论:
- 与WRRMs相结合的PRPP-SMART设计,与传统随机临床试验相比,为估计DTR提供了一个优越的框架.
- 这种方法通过适应患者的偏好,有效地解决招聘和保留问题.
- 提出的方法为未来实用临床研究中开发更个性化和更有效的DTR提供了基础.
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