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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 170120, 8 pages
Research Article

Validation Study Methods for Estimating Odds Ratio in Tables When Exposure is Misclassified

1Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran
2Department of Biostatistics, Infertility Research Center, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran

Received 27 September 2013; Revised 27 November 2013; Accepted 30 November 2013

Academic Editor: Edelmira Valero

Copyright © 2013 Bijan Nouri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Misclassification of exposure variables in epidemiologic studies may lead to biased estimation of parameters and loss of power in statistical inferences. In this paper, the inverse matrix method, as an efficient method of the correction of odds ratio for the misclassification of a binary exposure, was generalized to nondifferential misclassification and tables. Methods. Simple estimates for predictive values when misclassification is nondifferential are presented. Using them, we estimated the corrected log odds ratio and its variance for tables, using the inverse matrix method. A two-step weighted likelihood method was also developed. Moreover, we compared the matrix and inverse matrix methods to the maximum likelihood (MLE) method using a simulation study. Results. In all situations, the inverse matrix method proved to be more efficient than the matrix method. Matrix and inverse matrix methods for nondifferential situations are more efficient than differential misclassification. Conclusions. Although MLE is optimal among all of the methods, it is computationally difficult and requires programming. On the other hand, the inverse matrix method with a simple closed-form presents acceptable efficiency.