Zhang Zhongheng, Uddin Md Jamal, Cheng Jing, Huang Tao
Department of Emergency Medicine, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Department of Statistics, Shahjalal University of Science and Technology, Sylhet, Bangladesh.
Ann Transl Med. 2018 May;6(10):182. doi: 10.21037/atm.2018.03.37.
Observational studies are prone to bias due to confounding either measured or unmeasured. While measured confounding can be controlled for with a variety of sophisticated methods such as propensity score-based matching, stratification and multivariable regression model, the unmeasured confounding is usually cumbersome, leading to biased estimates. In econometrics, instrumental variable (IV) is widely used to control for unmeasured confounding. However, its use in clinical researches is generally less employed. In some subspecialties of clinical medicine such as pharmacoepidemiological research, IV analysis is increasingly used in recent years. With the development of electronic healthcare records, more and more healthcare data are available to clinical investigators. Such kind of data are observational in nature, thus estimates based on these data are subject to confounding. This article aims to review several methods for implementing IV analysis for binary and continuous outcomes. R code for these analyses are provided and explained in the main text.
观察性研究容易因测量或未测量的混杂因素而产生偏差。虽然测量的混杂因素可以通过多种复杂方法进行控制，如倾向得分匹配、分层和多变量回归模型，但未测量的混杂因素通常很麻烦，会导致估计有偏差。在计量经济学中，工具变量（IV）被广泛用于控制未测量的混杂因素。然而，它在临床研究中的应用通常较少。在临床医学的一些亚专业中，如药物流行病学研究，近年来IV分析的应用越来越多。随着电子健康记录的发展，临床研究人员可以获得越来越多的医疗数据。这类数据本质上是观察性的，因此基于这些数据的估计容易受到混杂因素的影响。本文旨在综述几种用于二元和连续结局进行IV分析的方法。文中提供并解释了这些分析的R代码。
