期刊
EUROPEAN JOURNAL OF EPIDEMIOLOGY
卷 36, 期 5, 页码 465-478出版社
SPRINGER
DOI: 10.1007/s10654-021-00757-1
关键词
Mendelian randomisation; Mediation analysis; Multivariable Mendelian randomisation; Two-step Mendelian randomisation
资金
- UK Medical Research Council Integrative Epidemiology Unit, University of Bristol [MC_UU_00011/1]
- UK Medical Research Council
- University of Bristol [MC_UU_00011/1, MC_UU_ 00011/2, MC_UU_00011/3, MC_UU_00011/7]
- Economics and Social Research Council support NMD via a Future Research Leaders grant [ES/N000757/1]
- Norwegian Research Council [295989]
- National Institute for Health Research (NIHR) Biomedical Research Centre based at University Hospitals Bristol NHS Foundation
- Sir Henry Wellcome Postdoctoral Fellowship [209138/Z/17/Z]
- Career Development Award from the UK Medical Research Council [MR/M020894/1]
- ESRC [ES/N000757/1] Funding Source: UKRI
- MRC [MC_UU_00011/1] Funding Source: UKRI
- Wellcome Trust [209138/Z/17/Z] Funding Source: Wellcome Trust
Mediation analysis aims to explain how an exposure affects an outcome, with Mendelian randomisation (MR) offering improved causal inference. This paper outlines two approaches for mediation analysis with MR: multivariable MR (MVMR) and two-step MR, which are not affected by confounding and measurement errors according to simulations.
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.
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