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dc.contributor.authorWhite, Ian Ren
dc.contributor.authorKaptoge, Stephenen
dc.contributor.authorRoyston, Patricken
dc.contributor.authorSauerbrei, Willien
dc.contributor.authorEmerging Risk Factors Collaboration,en
dc.date.accessioned2018-12-01T00:30:41Z
dc.date.available2018-12-01T00:30:41Z
dc.date.issued2019-02en
dc.identifier.issn0277-6715
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/286197
dc.description.abstractNon-linear exposure-outcome relationships, such as between body mass index (BMI) and mortality, are common. They are best explored as continuous functions using individual participant data from multiple studies. We explore two two-stage methods for meta-analysis of such relationships, where the confounder-adjusted relationship is first estimated in a non-linear regression model in each study, then combined across studies. The "metacurve" approach combines the estimated curves using multiple meta-analyses of the relative effect between a given exposure level and a reference level. The "mvmeta" approach combines the estimated model parameters in a single multivariate meta-analysis. Both methods allow the exposure-outcome relationship to differ across studies. Using theoretical arguments, we show that the methods differ most when covariate distributions differ across studies; using simulated data, we show that mvmeta gains precision but metacurve is more robust to model mis-specification. We then compare the two methods using data from the Emerging Risk Factors Collaboration on BMI, coronary heart disease events and all-cause mortality (>80 cohorts, >18000 events). For each outcome, we model BMI using fractional polynomials of degree 2 in each study, with adjustment for confounders. For metacurve, the powers defining the fractional polynomials may be study-specific or common across studies. For coronary heart disease, metacurve with common powers and mvmeta correctly identify a small increase in risk in the lowest levels of BMI, but metacurve with study-specific powers does not. For all-cause mortality, all methods identify a steep U-shape. The metacurve and mvmeta methods perform well in combining complex exposure-disease relationships across studies.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.publisherWiley-Blackwell
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEmerging Risk Factors Collaborationen
dc.titleMeta-analysis of non-linear exposure-outcome relationships using individual participant data: A comparison of two methods.en
dc.typeArticle
prism.endingPage338
prism.issueIdentifier3en
prism.publicationDate2019en
prism.publicationNameStatistics in medicineen
prism.startingPage326
prism.volume38en
dc.identifier.doi10.17863/CAM.33509
dcterms.dateAccepted2018-08-07en
rioxxterms.versionofrecord10.1002/sim.7974en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-02en
dc.contributor.orcidWhite, Ian R [0000-0002-6718-7661]
dc.contributor.orcidKaptoge, Stephen [0000-0002-1155-4872]
dc.identifier.eissn1097-0258
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idBritish Heart Foundation (None)
pubs.funder-project-idCambridge University Hospitals NHS Foundation Trust (CUH) (unknown)
pubs.funder-project-idBritish Heart Foundation (CH/12/2/29428)
pubs.funder-project-idMedical Research Council (MR/L003120/1)
pubs.funder-project-idMedical Research Council (G0800270)


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