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Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates
Open accessAbstract
Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assumptions. Under monotonicity, the causal estimate represents the average causal effect in 'compliers', individuals for whom the binary exposure would be present if they have the genetic variant and absent otherwise. Unlike in randomized trials, genetic compliers are unlikely to be a large or representative subgroup of the population. Under homogeneity, the causal effect of the exposure on the outcome is assumed constant in all individuals; rarely a plausible assumption. We here provide methods for causal estimation with a binary exposure (although subject to all the above caveats). Mendelian randomization investigations with a dichotomized binary exposure should be conceptualized in terms of an underlying continuous variable.
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“Also, if the exposure is a dichotomisation of a continuous risk factor, this poses a further threat to the violation of the exclusion restriction assumption.114”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [114]
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“Caution is especially warranted when interpreting effect estimates with a binary exposure.114”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [114]
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Metadata
- Authors
- Burgess, S., & Labrecque, J. A.
- Affiliations
- University of Cambridge (GB)
- Erasmus MC (NL)
- Corresponding author
- Burgess, Stephen
- Venue
- European Journal of Epidemiology
- Published
- 2018-07-23
- Volume
- 33
- Issue
- 10
- Pages
- 947-952
- ISSN
- 0393-2990 (ISSN-L)
- 1573-7284
- PMC
- PMC6153517
- OpenAlex
- W2797906093
- Cited by
- 569
- References
- 28
- Citations by year
- 2026: 30 · 2025: 66 · 2024: 173 · 2023: 107 · 2022: 67 · 2021: 68 · 2020: 33 · 2019: 24 · 2018: 1
- FWCI
- 19.28
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Epidemiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, other-oa)
- arXiv (Cornell University) (submittedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Apollo (cc-by)
- Funders
- Wellcome Trust (204623/Z/16/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Mendelian randomization, binary exposure, dichotomization, exclusion restriction, monotonicity, homogeneity, average causal effect, hypertension, blood pressure, causal effect estimation
- MeSH terms
- Blood Pressure, Humans, Hypertension, Models, Genetic, Random Allocation, Risk Factors, Genetic Variation, Models, Statistical, Likelihood Functions, Mendelian Randomization Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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