集群随机试验旨在支持可概括的推理
Sarah E Robertson1,2, Jon A Steingrimsson3, Issa J Dahabreh1,2,4
1CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Evaluation review
|January 18, 2024
概括
本研究介绍了集群随机试验的嵌套试验设计,即使使用非随机集群采样,也可以实现可概括的因果推断. 高效的估计方法精确量化治疗对目标人群的影响.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 临床试验 临床试验
背景情况:
- 集群随机试验 (CRT) 由于实际采样约束,在概括发现方面经常面临挑战.
- 基于特征的过量采样集群可以提高试验经济性,但可能会损害概括性.
研究的目的:
- 描述和评估用于CRT的嵌套试验设计,允许已知,特征依赖的集群采样概率.
- 开发分析该设计数据的方法,以确保对目标人群进行可概括的因果推断.
主要方法:
- 一个嵌套试验设计嵌入随机集群在一个更大的符合条件的集群队列内.
- 开发和评估用于识别和估计平均潜在结果和平均治疗效果的统计方法.
- 模拟研究以评估拟议估计器的偏差和精度.
主要成果:
- 提出的方法允许精确量化治疗对目标人群的影响.
- 估计器在模拟研究中显示出低偏差.
- 不同的估计器表现出不同程度的精度.
结论:
- 嵌套试验设计,结合高效估计,可以解决实践试验的需要,同时保持普遍性.
- 这种方法可以在目标人群中准确地推断因果关系,即使是基于特征的集群选择.
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