在观察性研究中根据两个标准对一个样本进行匹配
B Zhang1, D S Small1, K B Lasater1
1Wharton School, Schools of Nursing and Medicine, University of Pennsylvania.
Journal of the American Statistical Association
|June 22, 2023
概括
这项研究引入了一种新的多变量匹配方法,以创建可比较的研究组. 该技术同时平衡协变量并形成密切对,改进了观察性研究设计.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 多变量匹配旨在在观察性研究中创建可比的治疗和控制组.
- 传统方法在处理许多共变量时,难以实现共变量分布平衡和个体对同质性.
- 现有的技术往往需要单独的方法来平衡共变量分布和形成同质匹配对.
研究的目的:
- 引入一种新的多项式时间多变量匹配方法,实现组共变量分布相似性和个体对均性.
- 概括和改进现有的多变量匹配技术.
- 将该方法应用于对医疗保险人口中护理护理和败血症死亡率的现实研究.
主要方法:
- 提出了一种利用在三方图上最小成本流量优化的新方法.
- 三方图形结构允许同时优化平衡共变量分布 (右侧) 和匹配密切对 (左侧).
- 该方法概括了现有的方法,可以将边际分布之间的地球移动器距离最小化.
主要成果:
- 提出的方法成功地解决了多变量匹配的双重目标,这些目标通常与传统方法相冲突.
- 这种方法在计算上是高效的 (多项式时间).
- 该方法应用于一项研究,该研究调查了医疗保险受益人护理质量和败血症死亡率之间的联系.
结论:
- 新的基于三方图的最低成本流程方法为多变量匹配提供了统一和有效的方法.
- 这种方法增强了构建可靠的观察性研究治疗和控制组的能力.
- 在R中,可以使用match2C包来实现这种先进的匹配技术.
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