Maximum likelihood estimation of odds ratios in misclassified binary data with a validation substudy

Article type: Research Article

Authors: Rahardja, Dewi^{a; *} | Zhao, Yan D.^a | Zhang, Hongmei^b

Affiliations: [a] Department of Clinical Sciences and Simmons Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA | [b] Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA

Correspondence: [*] Corresponding author: 5323 Harry Hines Blvd-E1.402, Dallas, TX 75390-8822, USA. E-mail: rahardja@gmail.com.

Abstract: We consider misclassified binary data with a validation substudy. For such data various methods have been developed for estimating the odds ratio. It is well-known that the maximum likelihood estimator (MLE) of the odds ratio is efficient but requires iterative algorithms to compute. In this article, we derive a closed-form formula for the MLE and its asymptotic standard error. We compute the closed-form MLE on a data set that has been analyzed by other methods, and the results are compared.

Keywords: Binary data, confidence interval, invariance property of MLE, maximum likelihood estimation, misclassification, odds ratio

DOI: 10.3233/MAS-2011-0184

Journal: Model Assisted Statistics and Applications, vol. 6, no. 2, pp. 121-125, 2011