Labrecque Jeremy, Swanson Sonja A
1Department of Epidemiology, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
2Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA USA.
Curr Epidemiol Rep. 2018;5(3):214-220. doi: 10.1007/s40471-018-0152-1. Epub 2018 Jun 22.
Instrumental variable (IV) methods continue to be applied to questions ranging from genetic to social epidemiology. In the epidemiologic literature, discussion of whether the assumptions underlying IV analyses hold is often limited to only certain assumptions and even then, arguments are mostly made using subject matter knowledge. To complement subject matter knowledge, there exist a variety of falsification strategies and other tools for weighing the plausibility of the assumptions underlying IV analyses.
There are many tools that can refute the IV assumptions or help estimate the magnitude or direction of possible bias if the conditions do not hold perfectly. Many of these tools, including both recently developed strategies and strategies described decades ago, are underused or only used in specific applications of IV methods in epidemiology.
Although estimating causal effects with IV analyses relies on unverifiable assumptions, the assumptions can sometimes be refuted. We suggest that the epidemiologists using IV analyses employ all the falsification strategies that apply to their research question in order to avoid settings that demonstrably violate a core condition for valid inference.
工具变量（IV）方法继续被应用于从基因流行病学到社会流行病学等一系列问题。在流行病学文献中，关于IV分析所基于的假设是否成立的讨论往往仅限于某些假设，即便如此，相关论证大多是基于专业知识进行的。为了补充专业知识，存在多种证伪策略和其他工具来权衡IV分析所基于假设的合理性。
有许多工具可以反驳IV假设，或者在条件不完全成立时帮助估计可能偏差的大小或方向。其中许多工具，包括最近开发的策略和几十年前描述的策略，未得到充分利用，或者仅在IV方法在流行病学中的特定应用中使用。
尽管使用IV分析估计因果效应依赖于无法验证的假设，但这些假设有时可以被反驳。我们建议使用IV分析的流行病学家采用适用于其研究问题的所有证伪策略，以避免明显违反有效推断核心条件的情况。
