Mendelian randomisation for mediation analysis: current methods and challenges for implementation.
Mendelian randomisation for mediation analysis: current methods and challenges for implementation.
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DOI：
10.1007/s10654-021-00757-1
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发表时间：
2021-05
影响因子：
13.6
通讯作者：
Howe LD
中科院分区：
文献类型：
--
作者：
Carter AR;Sanderson E;Hammerton G;Richmond RC;Davey Smith G;Heron J;Taylor AE;Davies NM;Howe LD
中介分析试图解释暴露影响结果的途径。传统的，非工具性的变量方法进行调解分析的经验，一些方法上的困难，包括偏见，由于暴露，调解人和结果之间的混淆和测量误差。孟德尔随机化（MR）可用于改善中介分析的因果推断。我们描述了两种方法，可用于估计调解分析与MR：多变量MR（MVMR）和两步MR。我们概述了方法，并提供代码来演示它们如何可以用于调解分析。我们回顾了可能影响分析的问题，包括混杂，测量误差，弱仪器偏倚，暴露和介质之间的相互作用以及多个介质的分析。通过模拟和真实的数据实例补充了方法的描述。虽然MR依赖于大样本量和强有力的假设，如具有强大的工具，没有水平多效性的途径，我们的模拟表明，这些方法不受混杂因素的曝光或介质和结果和非差分测量误差的曝光或介质。MVMR和两步MR都可以在个人层面的MR和汇总数据MR中实现。与非工具变量调解方法相比，MR调解方法需要做出不同的假设。在这些假设更合理的地方，MR可以用来改善中介分析中的因果推理。在线版本包含补充材料，可通过10.1007/s10654-021-00757-1获得。
Mediation analysis seeks to explain the pathway(s) through which an exposure affects an outcome. Traditional, non-instrumental variable methods for mediation analysis experience a number of methodological difficulties, including bias due to confounding between an exposure, mediator and outcome and measurement error. Mendelian randomisation (MR) can be used to improve causal inference for mediation analysis. We describe two approaches that can be used for estimating mediation analysis with MR: multivariable MR (MVMR) and two-step MR. We outline the approaches and provide code to demonstrate how they can be used in mediation analysis. We review issues that can affect analyses, including confounding, measurement error, weak instrument bias, interactions between exposures and mediators and analysis of multiple mediators. Description of the methods is supplemented by simulated and real data examples. Although MR relies on large sample sizes and strong assumptions, such as having strong instruments and no horizontally pleiotropic pathways, our simulations demonstrate that these methods are unaffected by confounders of the exposure or mediator and the outcome and non-differential measurement error of the exposure or mediator. Both MVMR and two-step MR can be implemented in both individual-level MR and summary data MR. MR mediation methods require different assumptions to be made, compared with non-instrumental variable mediation methods. Where these assumptions are more plausible, MR can be used to improve causal inference in mediation analysis. The online version contains supplementary material available at 10.1007/s10654-021-00757-1.
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影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
KENNY, DA
影响因子：
2.1
作者：
Bowden J;Davey Smith G;Haycock PC;Burgess S
通讯作者：
Burgess S
DOI：
10.1097/ede.0000000000000161
发表时间：
2014-11
期刊：
Epidemiology (Cambridge, Mass.)
影响因子：
--
作者：
Burgess S;Davies NM;Thompson SG;EPIC-InterAct Consortium
通讯作者：
EPIC-InterAct Consortium
影响因子：
7
作者：
Goldsmith, Kimberley A.;MacKinnon, David P.;Pickles, Andrew
通讯作者：
Pickles, Andrew
影响因子：
8.8
作者：
Jose, Paul E.
通讯作者：
Jose, Paul E.