MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Population Health Sciences, University of Bristol, Bristol, UK.
Int J Epidemiol. 2019 Jun 1;48(3):713-727. doi: 10.1093/ije/dyy262.
Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.
We use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single-sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK Biobank to estimate the effect of education and cognitive ability on body mass index.
MVMR analysis consistently estimates the direct causal effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual- or summary-level data.
孟德尔随机化（MR）是流行病学中一种强大的工具，它可以通过利用作为暴露的工具变量（IV）的遗传变异，在存在未观察到的混杂的情况下，估计暴露对结果的因果效应。这已经扩展到多变量 MR（MVMR），以估计两种或更多暴露对结果的影响。
我们使用模拟和理论来澄清在一系列潜在情况下，MVMR 分析中估计效应的解释，其中次要暴露作为混杂因素、中介物、多效途径和碰撞器的作用各不相同。然后，我们描述了如何在单样本设置中评估 MVMR 分析的工具强度和有效性，并开发了用于评估两种流行的汇总数据设置中这些假设的检验。我们使用来自英国生物库的数据来估计教育和认知能力对体重指数的影响来说明我们的方法。
MVMR 分析一致地估计了感兴趣的暴露或暴露的直接因果效应，并为使用个体或汇总水平数据在广泛的情况下确定因果效应提供了强大的工具。
