  The area under a receiver operating characteristic curve (AUC) is a useful
tool to assess the performance of continuous-scale diagnostic tests on binary
classification. In this article, we propose an empirical likelihood (EL) method
to construct confidence intervals for the AUC from data collected by ranked set
sampling (RSS). The proposed EL-based method enables inferences without
assumptions required in existing nonparametric methods and takes advantage of
the sampling efficiency of RSS. We show that for both balanced and unbalanced
RSS, the EL-based point estimate is the Mann-Whitney statistic, and confidence
intervals can be obtained from a scaled chi-square distribution. Simulation
studies and two case studies on diabetes and chronic kidney disease data
suggest that using the proposed method and RSS enables more efficient inference
on the AUC.
