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dc.contributor.authorDalen, Ingvild
dc.contributor.authorHjartåker, Anette
dc.contributor.authorBuonaccorsi, John P.
dc.contributor.authorLaake, Petter
dc.contributor.authorThoresen, Magne
dc.date.accessioned2007-07-20T12:16:06Z
dc.date.available2007-07-20T12:16:06Z
dc.date.issued2006-07-04
dc.description.abstractBackground: Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile) scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods: We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC). Results: In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion: Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a percentile scale. Relating back to the original scale of the exposure solves the problem. The conclusion regards all regression models.en
dc.format.extent290139 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.citationEmerging Themes in Epidemiology 3(2006) article no 6, pp 11en
dc.identifier.doidoi:10.1186/1742-7622-3-6
dc.identifier.issn1742-7622
dc.identifier.urihttps://hdl.handle.net/10037/1110
dc.identifier.urnURN:NBN:no-uit_munin_932
dc.language.isoengen
dc.publisherBioMed Centralen
dc.rights.accessRightsopenAccess
dc.subjectVDP::Mathematics and natural science: 400::Basic biosciences: 470::Bioinformatics: 475en
dc.subjectVDP::Medical disciplines: 700::Clinical medical disciplines: 750::Other clinical medical disciplines: 799en
dc.subjectBioinformatikken
dc.subjectAnalytic perspectiveen
dc.subjectRegression analysisen
dc.subjectNaive approachen
dc.subjectDermatologien
dc.titleRegression analysis with categorized regression calibrated exposure. Some interesting findingsen
dc.typeJournal articleen
dc.typeTidsskriftartikkelen
dc.typePeer reviewed


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