In many case-control studies, it is common to utilize paired data when treatments are being evaluated. In this article, we propose and examine an efficient distribution-free test to compare two independent samples, where each is based on paired observations. We extend and modify the
density-based empirical likelihood ratio test presented by Gurevich and Vexler  to formulate an appropriate parametric likelihood ratio test statistic corresponding to the hypothesis of our interest and then to approximate the test statistic nonparametrically. We conduct an extensive Monte
Carlo study to evaluate the proposed test. The results of the performed simulation study demonstrate the robustness of the proposed test with respect to values of test parameters. Furthermore, an extensive power analysis via Monte Carlo simulations confirms that the proposed method outperforms
the classical and general procedures in most cases related to a wide class of alternatives. An application to a real paired data study illustrates that the proposed test can be efficiently implemented in practice.
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Document Type: Research Article
Department of Biostatisics, The State University of New York, Buffalo, NY, 14214, USA
The Department of Industrial Engineering and Management, SCE – Shamoon College of Engineering, Ashdod, 77245, Israel
June 1, 2013