Evaluating Diagnostic Accuracy of Binary Medical Tests in Multi-Reader Multi-Case Study

Multi-reader multi-case (MRMC) studies are typically conducted to compare the diagnostic performance of medical modalities, which are evaluated by multiple readers interpreting a common set of cases. One of the primary goals of MRMC analysis for binary diagnostic tests is to compare sensitivities and specificities across different imaging modalities. However, the complex correlation structure that is inherent in MRMC data poses significant challenges for analysis. In practice, a generalized estimating equation, a generalized linear mixed model, and McNemar's test are often used in MRMC analysis. In this paper, we explain the theoretical properties of conditional logistic regression applied to MRMC studies and explore its relationship with Cochran's $Q$ and McNemar's tests. We illustrate the characteristics of the proposed method through extensive simulation studies and real data analysis.