Title

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

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. © 2011 - IOS Press and the authors. All rights reserved.

Publication Title

Model Assisted Statistics and Applications

Share

COinS