在飞行中重新匹配:顺序匹配的随机化和对共变量调整的随机化的一个例子
Jonathan J Chipman1,2, Lindsay Mayberry3, Robert A Greevy4
1Department of Population Health Sciences, Division of Biostatistics, University of Utah Intermountain, Salt Lake City, Utah, USA.
Statistics in medicine
|July 13, 2023
概括
顺序重新匹配的随机化 (SRR) 通过改善共变量平衡和研究效率来增强共变量调整的随机化 (CAR). 与传统方法相比,这些先进的方法提供了优越的试验设计.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 实验设计 实验设计
背景情况:
- 与共变量调整的随机化 (CAR) 方法旨在最大限度地减少共变量失衡,并在临床试验中增强统计能力.
- 分层随机化是常见的,但匹配随机化 (MR) 和顺序匹配随机化 (SMR) 提供了改进的共变量平衡.
- 现有的SMR方法面临的挑战是预先指定值并实现最佳匹配.
研究的目的:
- 引入和评估顺序重新匹配随机化 (SRR),这是SMR的延伸.
- 评估SRR扩展是否可以改善共变量平衡,估计器效率和匹配最佳性.
- 将SRR与现有的CAR方案进行比较,并研究共变量调整策略.
主要方法:
- 开发了具有同时随机化,动态值和重新匹配能力的SRR.
- 在简化设置和现实世界案例研究中评估SRR.
- 与传统的CAR方法和参数共变量调整对比SRR的性能.
主要成果:
- 个人和集体的SRR扩展增强了共变量平衡,估计器效率和研究功率.
- 与标准的SMR相比,SRR产生了更高质量的匹配.
- 基于随机推断的CAR方案表现出与参数调整方法相比或更强的功率.
结论:
- SRR代表了CAR的重大进步,提供了改进的试验设计和分析.
- 这项研究强调了顺序随机化中的动态和自适应匹配的好处.
- 基于随机推断的CAR为参数模型中的传统协变量调整提供了强大的替代方案.
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