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. 2021 Oct 26:375:n2233.
doi: 10.1136/bmj.n2233.

Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration

Veronika W Skrivankova  1 , Rebecca C Richmond  2   3 , Benjamin A R Woolf  2   4 , Neil M Davies  2   3   5 , Sonja A Swanson  6 , Tyler J VanderWeele  7 , Nicholas J Timpson  2   3 , Julian P T Higgins  3   8 , Niki Dimou  9 , Claudia Langenberg  10   11 , Elizabeth W Loder  12   13 , Robert M Golub  14   15 , Matthias Egger  1   3   16 , George Davey Smith  2   3   8 , J Brent Richards  17   18
Affiliations

Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration

Veronika W Skrivankova et al. BMJ. .

Abstract

Mendelian randomisation (MR) studies allow a better understanding of the causal effects of modifiable exposures on health outcomes, but the published evidence is often hampered by inadequate reporting. Reporting guidelines help authors effectively communicate all critical information about what was done and what was found. STROBE-MR (strengthening the reporting of observational studies in epidemiology using mendelian randomisation) assists authors in reporting their MR research clearly and transparently. Adopting STROBE-MR should help readers, reviewers, and journal editors evaluate the quality of published MR studies. This article explains the 20 items of the STROBE-MR checklist, along with their meaning and rationale, using terms defined in a glossary. Examples of transparent reporting are used for each item to illustrate best practices.

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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the SNSF, NIHR Biomedical Research Centre at University Hospitals Bristol, Weston NHS Foundation Trust, and University of Bristol for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; EWL (head of research at The BMJ) played no part in the peer review or decision making of this paper at the editorial level, and contributed solely as an author; no other relationships or activities that could appear to have influenced the submitted work. Provenance and peer review: Not commissioned; externally peer reviewed

Figures

Fig 1
Fig 1
Canonical causal diagram illustrating the assumptions of instrumental variable (IV) analyses. Genetic variant G is used as an instrumental variable (proxy) for exposure X to assess its causal effect on outcome Y. IV assumptions include: I. Relevance: genetic variant G is associated with the exposure of interest X; II. Independence: genetic variant G shares no unmeasured cause with outcome Y; III. Exclusion restriction: genetic variant G does not affect outcome Y except through its potential effect on the exposure of interest X. Solid arrows=causal effects; dashed arrows=causal effects that are specifically prohibited by the IV assumptions. Note that other causal diagrams can be drawn that satisfy the IV assumptions (eg, genetic variant G does not have to directly cause exposure X); likewise, other pathways not drawn might violate the IV assumptions (eg, selection biases can also lead to violation of the independence assumption)
Fig 2
Fig 2
Numbers of participants in UK Biobank who passed validation for mendelian randomisation study. Figure reproduced with permission from Mountjoy et al, 2018
Fig 3
Fig 3
“Causal relationships of insomnia symptoms. (A) Associations between SNPs associated with frequent insomnia symptoms and CAD. Per allele associations with risk plotted against per allele associations with frequent insomnia symptom risk (vertical and horizontal black lines around points show 95% confidence intervals (CI) for each polymorphism) are shown for three different MR association tests. (B) Forest plot showing the estimates of the effect of genetically increased insomnia risk on CAD. Nearest genes are displayed to the right of the plots. Also shown for each SNP is the 95% CI (gray line segment) of the estimate and the IVW MR, MR-Egger, and weighted-median MR results in red. Sample sizes of each GWAS used in the MR analyses are as follows: frequent insomnia symptoms (n cases=129,270; n controls=108,352), CAD (n cases=60,801; n controls=123,504).” Figure reproduced with permission from Lane et al, 2019
Fig 4
Fig 4
Leave-one-out analysis. Figure reproduced with permission from Wootton et al, 2018
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