Guo Zijian, Cheng Jing, Lorch Scott A, Small Dylan S
Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, U.S.A.
Stat Med. 2014 Sep 10;33(20):3528-46. doi: 10.1002/sim.6227. Epub 2014 Jun 15.
An important concern in an observational study is whether or not there is unmeasured confounding, that is, unmeasured ways in which the treatment and control groups differ before treatment, which affect the outcome. We develop a test of whether there is unmeasured confounding when an instrumental variable (IV) is available. An IV is a variable that is independent of the unmeasured confounding and encourages a subject to take one treatment level versus another, while having no effect on the outcome beyond its encouragement of a certain treatment level. We show what types of unmeasured confounding can be tested for with an IV and develop a test for this type of unmeasured confounding that has correct type I error rate. We show that the widely used Durbin-Wu-Hausman test can have inflated type I error rates when there is treatment effect heterogeneity. Additionally, we show that our test provides more insight into the nature of the unmeasured confounding than the Durbin-Wu-Hausman test. We apply our test to an observational study of the effect of a premature infant being delivered in a high-level neonatal intensive care unit (one with mechanical assisted ventilation and high volume) versus a lower level unit, using the excess travel time a mother lives from the nearest high-level unit to the nearest lower-level unit as an IV.
在观察性研究中,一个重要的问题是是否存在未测量的混杂因素,即治疗组和对照组在治疗前存在的未测量的差异方式,这些差异会影响结果。当有工具变量(IV)可用时,我们开发了一种检验是否存在未测量混杂因素的方法。工具变量是一个与未测量的混杂因素无关的变量,它促使受试者采用一种治疗水平而非另一种治疗水平,同时除了对特定治疗水平的促进作用外,对结果没有影响。我们展示了可以用工具变量检验哪些类型的未测量混杂因素,并开发了一种针对此类未测量混杂因素的检验方法,该方法具有正确的I型错误率。我们表明,当存在治疗效果异质性时,广泛使用的杜宾-吴-豪斯曼检验可能会有膨胀的I型错误率。此外,我们表明,与杜宾-吴-豪斯曼检验相比,我们的检验能更深入地洞察未测量混杂因素的性质。我们将我们的检验应用于一项观察性研究,该研究比较了在高水平新生儿重症监护病房(配备机械辅助通气且容量大)与低水平病房分娩的早产儿的情况,使用母亲居住的地方到最近的高水平病房与最近的低水平病房的额外出行时间作为工具变量。