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dc.contributor.authorBurgess, Stephenen
dc.contributor.authorBowden, Jen
dc.contributor.authorFall, Ten
dc.contributor.authorIngelsson, Een
dc.contributor.authorThompson, Simonen
dc.date.accessioned2016-03-01T11:41:13Z
dc.date.available2016-03-01T11:41:13Z
dc.date.issued2017-01en
dc.identifier.citationEpidemiology 2016en
dc.identifier.issn1044-3983
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/254079
dc.description.abstractMendelian randomization investigations are becoming more powerful and simpler to perform, due to the increasing size and coverage of genome-wide association studies and the increasing availability of summarized data on genetic associations with risk factors and disease outcomes. However, when using multiple genetic variants from different gene regions in a Mendelian randomization analysis, it is highly implausible that all the genetic variants satisfy the instrumental variable assumptions. This means that a simple instrumental variable analysis alone should not be relied on to give a causal conclusion. In this article, we discuss a range of sensitivity analyses that will either support or question the validity of causal inference from a Mendelian randomization analysis with multiple genetic variants. We focus on sensitivity analyses of greatest practical relevance for ensuring robust causal inferences, and those that can be undertaken using summarized data. Aside from cases in which the justification of the instrumental variable assumptions is supported by strong biological understanding, a Mendelian randomization analysis in which no assessment of the robustness of the findings to violations of the instrumental variable assumptions has been made should be viewed as speculative and incomplete. In particular, Mendelian randomization investigations with large numbers of genetic variants without such sensitivity analyses should be treated with skepticism.
dc.languageengen
dc.language.isoenen
dc.publisherWolters Kluwer
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants.en
dc.typeArticle
dc.provenanceOA-7289
prism.endingPage42
prism.issueIdentifier1en
prism.publicationDate2017en
prism.publicationNameEpidemiologyen
prism.startingPage30
prism.volume28en
dc.rioxxterms.funderWellcome Trust
dc.rioxxterms.funderMRC
dc.rioxxterms.funderBHF
dc.rioxxterms.projectid100114
dc.rioxxterms.projectidMR/N501906/1
dc.rioxxterms.projectidCH/12/2/29428
dcterms.dateAccepted2016-09-30en
rioxxterms.versionofrecord10.1097/EDE.0000000000000559en
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2017-01en
dc.contributor.orcidBurgess, Stephen [0000-0001-5365-8760]
dc.contributor.orcidThompson, Simon [0000-0002-5274-7814]
dc.identifier.eissn1531-5487
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idMRC (MR/L003120/1)
pubs.funder-project-idMRC (G0800270)
pubs.funder-project-idBritish Heart Foundation (CH/12/2/29428)
pubs.funder-project-idWellcome Trust (100114/Z/12/Z)
pubs.funder-project-idBritish Heart Foundation (RG/08/014/24067)
pubs.funder-project-idBritish Heart Foundation (RG/13/13/30194)
dc.identifier.urlhttps://journals.lww.com/epidem/Fulltext/2017/01000/Sensitivity_Analyses_for_Robust_Causal_Inference.6.aspxen


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International