Comparative Study of Joint Empirical Likelihood Confidence Regions for the Evaluation of Continuous-Scale Diagnostic Tests in the Presence of Verification Bias

• Activity Number: 232
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Binhuan Wang*+ and Gengsheng Qin
• Companies: Georgia State University and Georgia State University
• Keywords: Confidence region ; empirical likelihood ; estimating equation ; ROC ; verification bias

Abstract

In a continuous-scale diagnostic test, when a cut-off level is given, the performance of the test in distinguishing diseased subjects from non-diseased subjects can be evaluated by its sensitivity and specificity. The joint inferences for sensitivity and specificity as well as cut-off level play an important role in the assessment of the diagnostic accuracy of the test. Most current study on this topic focuses on complete data cases. However, in some studies, only part of subjects that are given their screening test results ultimately have their true disease status verified. In addition, the verification may depend on the test result and the subject's observed characteristics. Directly applying full data methods to verified subjects would result in biased estimates, known as verification bias. In this paper, we propose various bias-corrected joint empirical likelihood confidence regions for sensitivity and specificity with verification-biased data. Thorough simulation studies are conducted to compare the finite sample performance of proposed confidence regions in terms of coverage probabilities, and some suggestions are provided accordingly. Finally, a real example is provided.