当使用医生的处方偏好作为仪器变量时,比较两阶段残留含入方法的性能:未测量的混和非可折叠性
1Department of Population Health Sciences, University of Leicester, Leicester, LE1 7RH, UK.
Journal of comparative effectiveness research
|April 3, 2024
概括
两阶段的剩余包含 (2SRI) 比一般化线性模型 (GLM) 和两阶段最小平方 (2SLS) 更有效地减少未测量的混偏差. 2SRI还在治疗效果估计中证明了对非缩性的稳定性.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 健康 数据科学 数据科学
背景情况:
- 在观察性研究中,未测量的混偏差仍然是一个重大挑战.
- 通用线性模型 (GLM) 和两阶最小方程 (2SLS) 是常见的方法,但它们在未测量的混和非合性下的性能尚未完全理解.
- 作为解决这些问题的方法,提出了两阶段的残留含量 (2SRI).
研究的目的:
- 为了比较2SRI,2SLS和多变量GLM的偏差减小性能.
- 评估2SRI和2SLS在存在非合性时处理未测量的混的能力.
- 评估仪器可变强度对强度的影响.
主要方法:
- 为了比较统计方法,进行了一项模拟研究.
- 一项经验分析使用了英国临床实践研究数据链接.
- 医生的处方偏好作为仪器变量 (IV).
主要成果:
- 与GLM和2SLS相比,2SRI在治疗效果估计中表现出较低的百分比偏差 (通常<15%).
- 2SRI表现出强度至轻度的非缩性 (百分比偏差<50%).
- 偏差降低效率随着未测量的混增加而下降;强烈的IV更强大.
结论:
- 2SRI是比GLM和2SLS估计治疗效果的偏差较小的方法.
- 2SRI提供了对非崩性的稳定性,特别是在轻微的未测量的混效应下.
- 这些发现支持2SRI在流行病学研究中的实用性,具有潜在的未测量混.
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