The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Extending the MR‐Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropy
Open accessAbstract
Methods have been developed for Mendelian randomization that can obtain consistent causal estimates while relaxing the instrumental variable assumptions. These include multivariable Mendelian randomization, in which a genetic variant may be associated with multiple risk factors so long as any association with the outcome is via the measured risk factors (measured pleiotropy), and the MR‐Egger (Mendelian randomization‐Egger) method, in which a genetic variant may be directly associated with the outcome not via the risk factor of interest, so long as the direct effects of the variants on the outcome are uncorrelated with their associations with the risk factor (unmeasured pleiotropy). In this paper, we extend the MR‐Egger method to a multivariable setting to correct for both measured and unmeasured pleiotropy. We show, through theoretical arguments and a simulation study, that the multivariable MR‐Egger method has advantages over its univariable counterpart in terms of plausibility of the assumption needed for consistent causal estimation and power to detect a causal effect when this assumption is satisfied. The methods are compared in an applied analysis to investigate the causal effect of high‐density lipoprotein cholesterol on coronary heart disease risk. The multivariable MR‐Egger method will be useful to analyse high‐dimensional data in situations where the risk factors are highly related and it is difficult to find genetic variants specifically associated with the risk factor of interest (multivariable by design), and as a sensitivity analysis when the genetic variants are known to have pleiotropic effects on measured risk factors.
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Cites
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“In addition, the Cochran's Q test in the IVW model and MR Egger model Abbreviations: AgeSmk: age of smoking initiation; BMI: body mass index; CigDay: cigarettes per day; MVMR-Egger: multivariable Mendelian randomization using Egger regression; MVMR-IVW: multivariable Mendelian randomization using inverse variance-weig…”
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Metadata
- Authors
- Rees, J. M. B., Wood, A. M., & Burgess, S.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2017-09-27
- Volume
- 36
- Issue
- 29
- Pages
- 4705-4718
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2742117056
- Cited by
- 404
- References
- 10
- FWCI
- 12.11
- Type
- journal article
- Language
- en
- Open access
- hybrid
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