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  • Acknowledgments. We are grateful to the Editor, Associate Editor and two reviewers for their thoughtful comments and valuable suggestions. Dr Gengsheng Qin’s work is supported in part by the US National Science Foundation. Dr Xin Huang’s work was partially supported by a PUHR RA fellowship from Georgia State University.

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As new diagnostic tests are developed and marketed, it is very important to be able to compare the accuracy of a given two continuous-scale diagnostic tests. An effective method to evaluate the difference between the diagnostic accuracy of two tests is to compare partial areas under the receiver operating characteristic curves (AUCs). In this paper, we review existing parametric methods. Then, we propose a new semiparametric method and a new nonparametric method to investigate the difference between two partial AUCs. For the difference between two partial AUCs under each method, we derive a normal approximation, define an empirical log-likelihood ratio, and show that the empirical log-likelihood ratio follows a scaled chi-square distribution. We construct five confidence intervals for the difference based on normal approximation, bootstrap, and empirical likelihood methods. Finally, extensive simulation studies are conducted to compare the finite-sample performances of these intervals, and a real example is used as an application of our recommended intervals. The simulation results indicate that the proposed hybrid bootstrap and empirical likelihood intervals outperform other existing intervals in most cases.