Binary regression with differentially misclassified response and exposure variables |
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| Authors: | Li Tang Robert H. Lyles Caroline C. King David D. Celentano Yungtai Lo |
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| Affiliation: | 1. Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, U.S.A.;2. Department of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University, Atlanta, U.S.A.;3. Division of Reproductive Health, Centers for Disease Control and Prevention, Atlanta, U.S.A.;4. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.;5. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, U.S.A. |
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| Abstract: | Misclassification is a long‐standing statistical problem in epidemiology. In many real studies, either an exposure or a response variable or both may be misclassified. As such, potential threats to the validity of the analytic results (e.g., estimates of odds ratios) that stem from misclassification are widely discussed in the literature. Much of the discussion has been restricted to the nondifferential case, in which misclassification rates for a particular variable are assumed not to depend on other variables. However, complex differential misclassification patterns are common in practice, as we illustrate here using bacterial vaginosis and Trichomoniasis data from the HIV Epidemiology Research Study (HERS). Therefore, clear illustrations of valid and accessible methods that deal with complex misclassification are still in high demand. We formulate a maximum likelihood (ML) framework that allows flexible modeling of misclassification in both the response and a key binary exposure variable, while adjusting for other covariates via logistic regression. The approach emphasizes the use of internal validation data in order to evaluate the underlying misclassification mechanisms. Data‐driven simulations show that the proposed ML analysis outperforms less flexible approaches that fail to appropriately account for complex misclassification patterns. The value and validity of the method are further demonstrated through a comprehensive analysis of the HERS example data. Copyright © 2015 John Wiley & Sons, Ltd. |
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| Keywords: | likelihood logistic regressions misclassification odds ratio |
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