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dc.contributor.authorKüchenhoff, Helmuten_US
dc.contributor.authorLederer, Wolfgangen_US
dc.contributor.authorLesaffre, Emmanuelen_US
dc.description.abstractMost epidemiological studies suffer from misclassification in the response and/or the covariates. Since ignoring misclassification induces bias on the parameter estimates, correction for such errors is important. For measurement error, the continuous analog to misclassification, a general approach for bias correction is the SIMEX (simulation extrapolation) originally suggested by Cook and Stefanski (1994). This approach has been recently extended to regression models with a possibly misclassified categorical response and/or the covariates by K¨uchenhoff et al. (2005), and is called the MC-SIMEX approach. To assess the importance of a regressor not only its (corrected) estimate is needed, but also its standard error. For the original SIMEX approach. Carroll et al. (1996) developed a method for estimating the asymptotic variance. Here we derive the asymptotic variance estimators for the MC-SIMEX approach, extending the methodology of Carroll et al. (1996). We also include the case where the misclassification probabilities are estimated by a validation study. An extensive simulation study shows the good performance of our approach. The approach is illustrated using an example in caries research including a logistic regression model, where the response and a binary covariate are possibly misclassified.en_US
dc.publisher|aTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen|cMünchenen_US
dc.relation.ispartofseries|aDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München|x473en_US
dc.subject.keywordSIMEX approachen_US
dc.subject.keywordvariance estimationen_US
dc.titleAsymptotic Variance Estimation for the Misclassification SIMEXen_US
dc.type|aWorking Paperen_US

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