评估治疗效果异质性的最大最佳集群随机设计
Mary M Ryan1,2, Denise Esserman1,2, Fan Li1,2,3
1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Statistics in medicine
|June 20, 2023
概括
集群随机试验 (CRT) 现在可以为异质治疗效应 (HTE) 分析进行最佳设计. 新的配方确保HTE的最大功率和预算限制内的平均治疗效果.
科学领域:
- 生物统计学 生物统计学
- 临床试验设计 临床试验设计
- 公共卫生研究 公共卫生研究
背景情况:
- 集群随机试验 (CRT) 对实用性研究至关重要,但在分析异质治疗效应 (HTEs) 方面面临挑战.
- 在CRT中预先规定的HTE分析对于了解干预对子群的影响至关重要.
- 现有的CRT样本大小公式通常假定已知的集群内相关系数 (ICC),限制它们对HTE的应用.
研究的目的:
- 开发最佳的集群随机试验设计,以最大限度地提高预先规定的异质治疗效应 (HTE) 分析的功率.
- 为了获得新的设计公式来确定集群大小和集群的数量在预算限制下HTE估计.
- 建立设计CRT的方法,以适应平均和异质治疗效应分析.
主要方法:
- 为局部最佳设计 (LOD) 推导出新的设计公式,以最大限度地减少HTE参数估计的差异.
- 开发了一个最大限度的设计,以最大限度地提高HTE分析效率,在最坏的情况下,在未知的ICC的情况下.
- 建立了多目标的最佳设计,平衡了对平均和异质治疗效果的考虑.
主要成果:
- 新的设计公式为在预算限制下进行HTE分析提供了最佳的集群大小和集群数量.
- 马克西明的设计提供了强大的HTE分析效率,即使在未知的共变量和结果ICC的情况下.
- 多目标设计有效地平衡了平均处理效果和高高电压的功率.
结论:
- 开发的方法使得优化的集群随机试验设计能够进行强大的异质治疗效果分析.
- 这些设计增强了对实用设置中不同亚群体的干预影响的理解.
- 附带的R Shiny应用程序有助于这些最佳设计计算的实际应用.
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