层的最佳精制以平衡共变量
Katherine Brumberg1, Dylan S Small2, Paul R Rosenbaum2
1Department of Statistics, University of Michigan, Ann Arbor, MI 48109, United States.
Biometrics
|July 12, 2024
概括
本研究介绍了一种方法,以优化分层以减少共变异不平衡. 该方法使用线性程序的随机圆,为观测研究提供了实际解决方案,并改善了共变量平衡.
科学领域:
- 统计 统计 统计 统计
- 观察性研究 观察性研究
- 生物统计学 生物统计学
背景情况:
- 在观察性研究中,分层对于减少共变异不平衡至关重要.
- 现有的方法可能无法优化分层,以最大限度地减少跨多个共变量的层内不平衡.
- 整数编程提供了一个理论解决方案,但对于大型数据集来说,它在计算上是难以处理的.
研究的目的:
- 开发一种最优的方法来将单一层分成两个层,以最大限度地减少层内部的共变异不平衡.
- 通过计算可行的线性编程放松和随机圆形来近似整数编程解决方案.
- 在观察性研究中证明该方法在改善共变量平衡方面的有效性.
主要方法:
- 制定了分层问题作为一个整数程序.
- 通过线性编程放松和随机圆的方法近似整数程序解决方案.
- 分析了随机圆的理论特性,显示了它在某些条件下与线性编程放松非常相似.
主要成果:
- 随机圆方法为最佳整数编程解决方案提供了可用的近似.
- 当层面大小相对于共变量数量较大时,随机圆非常接近线性编程放松.
- 在一项观察性研究中,将5个倾向性得分层精制为10个,实现了优异的共变量平衡,同时保留了所有患者.
结论:
- 拟议的方法有效地提炼了层次,以在观察性研究中实现卓越的共变量平衡.
- 线性程序的随机圆形化为解决复杂的分层问题提供了一种实用和理论上合理的方法.
- 有一个R包,optrefine,可用于实现这种方法.
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