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当地仪器变量方法在不同强度的仪器设置中如何执行? 一个模拟研究和紧急手术的评估
Silvia Moler-Zapata1, Richard Grieve1, Anirban Basu2,3
1Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
Health economics
|June 12, 2023
概括
与两阶段最小方程 (2SLS) 相比,局部仪器变量 (LIV) 方法为平均治疗效应 (ATE) 和条件平均治疗效应 (CATE) 提供了较少偏差的估计. 即使在较小的样本大小中,LIV也能很好地使用强大的仪器.
科学领域:
- 计量经济学 计量经济学
- 生物统计学 生物统计学
- 因果推理因果推理
背景情况:
- 局部仪表变量 (LIV) 方法用于一致估计平均治疗效应 (ATE) 和条件平均治疗效应 (CATE).
- 关于LIV表现的证据有限,涉及仪器变量 (IV) 的强度和样本大小变化.
- 研究了四种异质性场景:同质性,公开性,基本性和联合公开性/基本性.
研究的目的:
- 评估本地仪表变量 (LIV) 方法的性能与两阶段最小平方 (2SLS) 方法相比.
- 评估不同样本大小和仪表变量 (IV) 强度对估计准确性的影响.
- 为了在不同的异质场景中比较LIV和2SLS之间的偏差和根平均平方误差 (RMSE).
主要方法:
- 进行了一项模拟研究,比较LIV和2SLS.
- 该研究对样本大小和仪器变量 (IV) 强度进行了变化.
- 在四个异质性场景中评估了性能,包括未测量的混.
主要成果:
- 当地仪器变量 (LIV) 始终产生低偏差估计,特别是使用强大的仪器,无论样本大小如何.
- 与2SLS相比,LIV对ATE和CATE的偏差和根平均平方误差 (RMSE) 都较低.
- 这两种方法都需要更强的IVs,以在较小的样本大小中实现低偏差.
- 在一次紧急手术 (ES) 病例研究中,LIV确定了较脆弱的患者有更糟糕的结果,与2SLS不同.
结论:
- 局部仪器变量 (LIV) 方法对偏差具有坚固性,特别是使用强大的仪器,即使在小样本大小中也是如此.
- 在估计ATE和CATE时,LIV优于2SLS,特别是在存在未测量的混时.
- 对于使用中等强度的连续静脉注射来评估政策相关的治疗效果,LIV方法比2SLS.
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