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A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable

Corbin, Marine; Haslett, Stephen; Pearce, Neil; Maule, Milena; Greenland, Sander

Description

Purpose: Measurement error is an important source of bias in epidemiological studies. We illustrate three approaches to sensitivity analysis for the effect of measurement error: imputation of the ‘true’ exposure based on specifying the sensitivity and specificity of the measured exposure (SS); direct imputation (DI) using a regression model for the predictive values; and adjustment based on a fully Bayesian analysis. Methods: We deliberately misclassify smoking status in data from a...[Show more]

dc.contributor.authorCorbin, Marine
dc.contributor.authorHaslett, Stephen
dc.contributor.authorPearce, Neil
dc.contributor.authorMaule, Milena
dc.contributor.authorGreenland, Sander
dc.date.accessioned2020-12-20T20:51:28Z
dc.date.available2020-12-20T20:51:28Z
dc.identifier.issn0300-5771
dc.identifier.urihttp://hdl.handle.net/1885/217788
dc.description.abstractPurpose: Measurement error is an important source of bias in epidemiological studies. We illustrate three approaches to sensitivity analysis for the effect of measurement error: imputation of the ‘true’ exposure based on specifying the sensitivity and specificity of the measured exposure (SS); direct imputation (DI) using a regression model for the predictive values; and adjustment based on a fully Bayesian analysis. Methods: We deliberately misclassify smoking status in data from a case-control study of lung cancer. We then implement the SS and DI methods using fixed-parameter (FBA) and probabilistic (PBA) bias analyses, and Bayesian analysis using the Markov-Chain Monte-Carlo program WinBUGS to show how well each recovers the original association. Results: The ‘true’ smoking-lung cancer odds ratio (OR), adjusted for sex in the original dataset, was OR = 8.18 [95% confidence limits (CL): 5.86, 11.43]; after misclassification, it decreased to OR = 3.08 (nominal 95% CL: 2.40, 3.96). The adjusted point estimates from all three approaches were always closer to the ‘true’ OR than the OR estimated from the unadjusted misclassified smoking data, and the adjusted interval estimates were always wider than the unadjusted interval estimate. When imputed misclassification parameters departed much from the actual misclassification, the ‘true’ OR was often omitted in the FBA intervals whereas it was always included in the PBA and Bayesian intervals. Conclusions: These results illustrate how PBA and Bayesian analyses can be used to better account for uncertainty and bias due to measurement error.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherOxford University Press
dc.sourceInternational Journal of Epidemiology
dc.titleA comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume46
dc.date.issued2017
local.identifier.absfor119999 - Medical and Health Sciences not elsewhere classified
local.identifier.ariespublicationa383154xPUB8356
local.type.statusPublished Version
local.contributor.affiliationCorbin, Marine, Massey University
local.contributor.affiliationHaslett, Stephen, Administrative Portfolio, ANU
local.contributor.affiliationPearce, Neil, Massey University
local.contributor.affiliationMaule, Milena, University of Turin
local.contributor.affiliationGreenland, Sander, University of California
local.bibliographicCitation.issue3
local.bibliographicCitation.startpage1063
local.bibliographicCitation.lastpage1072
local.identifier.doi10.1093/ije/dyx027
dc.date.updated2020-11-23T10:04:43Z
local.identifier.scopusID2-s2.0-85027732187
local.identifier.thomsonID000406242600038
CollectionsANU Research Publications

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