Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
Neuroepidemiology. 2010;35(4):307-10. doi: 10.1159/000321179. Epub 2010 Oct 29.
In the first part of this series, it was highlighted how even though randomised controlled trials can provide robust evidence for therapeutic interventions, for many types of exposure it may not be either practical or ethical to randomise patients to such studies (see part 1). Instrumental variables (IV) analyses have been increasingly employed in recent times in epidemiology to investigate the potential causal effects of an exposure. An IV is a variable that can realistically mimic the treatment allocation process in a randomised study and is assumed to be not directly related to outcome, except through the direct effect of treatment and not related to outcome through either measured or unmeasured confounders. As discussed in the first article, IV analyses can be useful in estimating direct treatment effects provided that the chosen instrument is strong. A particular type of IV analysis where a specific genetic variant has been used as the instrument known as 'Mendelian randomisation' has become increasingly common. The aim of the second part of this statistical primer is to outline the approach to Mendelian randomisation and some of the advantages and disadvantages of this approach.
在本系列的第一部分中，强调了即使随机对照试验可以为治疗干预措施提供可靠的证据，但对于许多类型的暴露，将患者随机分配到此类研究中在实践上或伦理上可能都不可行（见第一部分）。工具变量（IV）分析在最近的流行病学中越来越多地被用于研究暴露的潜在因果效应。工具变量是一个可以真实模拟随机研究中治疗分配过程的变量，并且假定它与结果没有直接关系，除非通过治疗的直接效应，并且与通过测量或未测量的混杂因素无关。正如第一篇文章中所讨论的，只要选择的工具足够强大，IV 分析就可以用于估计直接治疗效果。一种特定类型的 IV 分析，其中使用特定的遗传变体作为工具，称为“孟德尔随机化”，已经变得越来越普遍。本统计入门第二部分的目的是概述孟德尔随机化的方法以及该方法的一些优点和缺点。
