一个部分随机的患者偏好,序列式,多重分配,随机试验设计,通过加权和复制的频率主义和贝叶斯方法进行分析.
Marianthie Wank1, Sarah Medley1, Roy N Tamura2
1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
将患者偏好纳入临床试验设计可以提高代表性. 新的部分随机,患者偏好,序列,多重分配,随机试验 (PRPP-SMART) 设计及其分析方法增强了动态治疗方案的估计.
科学领域:
- 临床试验的设计
- 生物统计学 生物统计学
- 医疗服务研究 医疗服务研究
背景情况:
- 随机对照试验 (RCT) 在忽视参与者的偏好时可能缺乏外部有效性.
- 排除或不适应患者偏好可能会对试验积累,坚持,保留和概括性产生负面影响.
- 越来越需要临床试验设计,有效地整合参与者的治疗偏好.
研究的目的:
- 引入和评估一种新的临床试验设计,即部分随机,患者偏好,序列,多重分配,随机试验 (PRPP-SMART).
- 开发和评估贝叶斯和频率权重和复制回归模型 (WRRMs) 来分析PRPP-SMART试验中的数据.
- 通过使用随机和非随机参与者的数据,高效地估计动态治疗方案 (DTR).
主要方法:
- 拟议的PRPP-SMART设计结合了部分随机,患者偏好 (PRPP) 和顺序,多重分配,随机试验 (SMART) 设计的元素.
- 为了估计嵌入式DTRs,一个具有二进制结果的两阶段PRPP-SMART被概念化.
- 贝叶斯和频率主义WRRMs被开发用于分析数据,包括随机和非随机参与者.
主要成果:
- 开发的WRRMs通过包括非随机参与者,提供了对DTR效应的有效估计.
- 提出的方法在DTR效应估计中显示了可以忽略不计的偏差.
- 与传统的PRPP分析相比,该分析排除了非随机参与者,显示效率有所提高.
结论:
- PRPP-SMART设计为适应患者偏好的临床试验提供了一个有希望的框架.
- 相关的贝叶斯式和频率式WRRMs为此类试验提供了强大的和高效的分析方法.
- 整合参与者的偏好可以提高临床试验结果的有效性和适用性.
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