Correction of Misclassification Error in Presence of Non-Ignorable Missing Data

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Presentation given at Eastern North American Region International Biometric Society (ENAR).


Missing data and misclassification errors are very common problem in many research studies. It is well known that the misclassification error in covariates can cause bias estimation of parameters for statistical model. It can also reduce the overall statistical power. Misclassification simulation extrapolation (MC-SIMEX) procedure is a well-known method to correct the bias in parameter estimation due to misclassification for given statistical model. Misclassification matrix has to be known or estimated from a validation study to use MC-SIMEX method. However, in many circumstances, the validation study has non-ignorable missing data. Estimation of misclassification matrix can be biased and hence the estimation of parameters of given statistical model in presence of non-ignorable missing data. In this paper, we apply the Baker, Rosenberger and Dersimonian modeling approach to perform the sensitivity analysis using MC-SIMEX method. Simulation studies are used to investigate the efficiency of parameters under given assumption of missing data mechanism. We illustrate the method by using “National Health and Nutrition Examination Survey” dataset.


Eastern North American Region International Biometric Society (ENAR)


Atlanta, GA

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