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dc.contributor.authorAgogo, George O.
dc.contributor.authorvan der Voet, Hilko
dc.contributor.authorvan't Veer, Pieter
dc.contributor.authorFerrari, Pietro
dc.contributor.authorLeenders, Max
dc.contributor.authorMuller, David C.
dc.contributor.authorSánchez-Cantalejo, Emilio
dc.contributor.authorBamia, Christina
dc.contributor.authorBraaten, Tonje
dc.contributor.authorKnüppel, Sven
dc.contributor.authorJohansson, Ingegerd
dc.contributor.authorVan Eeuwijk, Fred A.
dc.contributor.authorBoshuizen, Hendriek
dc.date.accessioned2022-05-09T07:38:57Z
dc.date.available2022-05-09T07:38:57Z
dc.date.issued2014-11-17
dc.description.abstractIn epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted twopart regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.en_US
dc.identifier.citationAgogo, van der Voet H, van't Veer P, Ferrari P, Leenders M, Muller, Sánchez-Cantalejo E, Bamia C, Braaten T, Knüppel S, Johansson I, Van Eeuwijk, Boshuizen H. Use of two-part regression calibration model to correct for measurement error in episodically consumed foods in a single-replicate study design: EPIC case study. PLOS ONE. 2014;9:e113160(11)en_US
dc.identifier.cristinIDFRIDAID 1226603
dc.identifier.doi10.1371/journal.pone.0113160
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/10037/25030
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.journalPLOS ONE
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2014 The Author(s)en_US
dc.titleUse of two-part regression calibration model to correct for measurement error in episodically consumed foods in a single-replicate study design: EPIC case studyen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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