Department of Statistics, Stanford University, Stanford, CA, U.S.A.
Stat Med. 2014 Jun 15;33(13):2297-340. doi: 10.1002/sim.6128. Epub 2014 Mar 6.
A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.
许多健康研究的目标是确定治疗或干预对健康结果的因果效应。通常，进行完全随机实验在伦理上或实际上是不可能的，因此必须使用观察性研究。观察性研究有效性的一个主要挑战是存在未测量的混杂（即治疗组和对照组在治疗前存在未测量的差异，这些差异也会影响结果）的可能性。工具变量分析是一种控制未测量混杂的方法。这种分析需要测量有效的工具变量，工具变量是指（i）与未测量的混杂无关；（ii）影响治疗；（iii）仅通过其对治疗的影响间接影响结果的变量。本教程讨论了可以通过工具变量分析估计的因果效应类型；工具变量分析提供因果效应有效估计所需的假设以及对这些假设的敏感性分析；使用工具变量估计因果效应的方法；以及健康研究中工具变量的来源。
