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作为双重差分法的推广,论未观察到的混杂因素的负面结果控制

On negative outcome control of unobserved confounding as a generalization of difference-in-differences.

作者信息

Sofer Tamar, Richardson David B, Colicino Elena, Schwartz Joel, Tchetgen Tchetgen Eric J

机构信息

University of Washington, Harvard T.H. Chan School of Public Health, and Gillings School of Global Public Health, University of North Carolina.

出版信息

Stat Sci. 2016;31(3):348-361. doi: 10.1214/16-STS558. Epub 2016 Sep 27.

Abstract

The (DID) approach is a well known strategy for estimating the effect of an exposure in the presence of unobserved confounding. The approach is most commonly used when pre-and post-exposure outcome measurements are available, and one can assume that the association of the unobserved confounder with the outcome is equal in the two exposure groups, and constant over time. Then, one recovers the treatment effect by regressing the change in outcome over time on the exposure. In this paper, we interpret the difference-in-differences as a negative outcome control (NOC) approach. We show that the pre-exposure outcome is a negative control outcome, as it cannot be influenced by the subsequent exposure, and it is affected by both observed and unobserved confounders of the exposure-outcome association of interest. The relation between DID and NOC provides simple conditions under which negative control outcomes can be used to detect and correct for confounding bias. However, for general negative control outcomes, the DID-like assumption may be overly restrictive and rarely credible, because it requires that both the outcome of interest and the control outcome are measured on the same scale. Thus, we present a scale-invariant generalization of the DID that may be used in broader NOC contexts. The proposed approach is demonstrated in simulations and on a Normative Aging Study data set, in which Body Mass Index is used for NOC of the relationship between air pollution and inflammatory outcomes.

摘要

双重差分(DID)方法是一种在存在未观察到的混杂因素时估计暴露效应的著名策略。当有暴露前和暴露后的结局测量值,并且可以假设未观察到的混杂因素与结局的关联在两个暴露组中相等且随时间恒定不变时,该方法最为常用。然后,通过将结局随时间的变化对暴露进行回归来恢复治疗效果。在本文中,我们将双重差分解释为一种负面结局对照(NOC)方法。我们表明,暴露前的结局是一个负面对照结局,因为它不会受到后续暴露的影响,并且它受到感兴趣的暴露 - 结局关联的观察到的和未观察到的混杂因素的影响。双重差分与负面结局对照之间的关系提供了简单的条件,在这些条件下,负面对照结局可用于检测和校正混杂偏差。然而,对于一般的负面对照结局,类似双重差分的假设可能过于严格且很少可信,因为它要求感兴趣的结局和对照结局都在相同的尺度上进行测量。因此,我们提出了一种双重差分的尺度不变推广方法,该方法可用于更广泛的负面结局对照背景。所提出的方法在模拟和一项规范性衰老研究数据集上得到了验证,在该数据集中,体重指数被用作空气污染与炎症结局之间关系的负面结局对照。

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