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Evaluating the exposure and disease relationship with adjustment for different types of exposure misclassification: a
1Department of Biostatistics, The Rollins School of Public Health, 1518 Clifton Road, N.E., Emory University, Atlanta, Georgia 30322, USA. akosins@sph.emory.edu
Abstract:
Misclassification of exposure can lead to biased results in the epidemiologic research. Available methods accounting for misclassification often require the use of a gold standard or assume non-differential misclassification of exposure. We present a regression approach which can detect and account for different types of misclassification when estimating the exposure and disease relationship. This approach uses two imperfect measures of a dichotomous exposure and does not require a gold standard. Standard statistical packages with a logistic regression module can be used for estimation of parameters through the EM algorithm process. Two examples are used to illustrate the methodology.