工具变量:加强还是不加强?

Siyu Heng, Bo Zhang, Xu Han, Scott A. Lorch, Dylan S. Small
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引用次数: 4

摘要

工具变量(IVs)被广泛用于处理不可测量的混杂。然而,弱静脉注射可能会引起问题。许多匹配的研究都考虑过通过丢弃一些样本来加强静脉注射。人们普遍认为,加强IV往往会增加非参数测试和敏感性分析的能力。我们重新评估这种传统智慧,并提供新的见解。首先,我们评估了假设有效的静脉注射强度和样本量之间的权衡,并展示了加强静脉注射增加功率的条件。其次,我们推导了一个用于检查连续剂量敏感性分析模型有效性的标准,并表明广泛使用的Γ敏感性分析模型(用于证明在大样本中加强静脉注射会增加敏感性分析的能力)不适用于连续静脉注射。第三,我们用一个可能无效的IV量化Wald估计器的偏差,并利用它来开发一个有效的敏感性分析框架,并表明加强IV可能会或可能不会增加敏感性分析的能力。我们使用我们的框架来研究在高科技/高容量新生儿重症监护室分娩对早产儿的影响。
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Instrumental variables: to strengthen or not to strengthen?
Abstract Instrumental variables (IVs) are extensively used to handle unmeasured confounding. However, weak IVs may cause problems. Many matched studies have considered strengthening an IV through discarding some of the sample. It is widely accepted that strengthening an IV tends to increase the power of non-parametric tests and sensitivity analyses. We re-evaluate this conventional wisdom and offer new insights. First, we evaluate the trade-off between IV strength and sample size assuming a valid IV and exhibit conditions under which strengthening an IV increases power. Second, we derive a criterion for checking the validity of a sensitivity analysis model with a continuous dose and show that the widely used Γ sensitivity analysis model, which was used to argue that strengthening an IV increases the power of sensitivity analyses in large samples, does not work for continuous IVs. Third, we quantify the bias of the Wald estimator with a possibly invalid IV and leverage it to develop a valid sensitivity analysis framework and show that strengthening an IV may or may not increase the power of sensitivity analyses. We use our framework to study the effect on premature babies of being delivered in a high technology/high volume neonatal intensive care unit.
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