title: Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data.
abstract: We show that truth-state runs in rank-ordered data constitute a natural categorization of continuously-distributed test results for maximum likelihood (ML) estimation of ROC curves. On this basis, we develop two new algorithms for fitting binormal ROC curves to continuously-distributed data: a true ML algorithm (LABROC4) and a quasi-ML algorithm (LABROC5) that requires substantially less computation with large data sets. Simulation studies indicate that both algorithms produce reliable estimates of the binormal ROC curve parameters a and b, the ROC-area index Az, and the standard errors of those estimates.
authors.affiliation: Department of Radiology, University of Chicago Medical Center, IL 60637-1470, USA. c-metz@uchicago.edu
authors.forename: C E
authors.initials: CE
authors.lastname: Metz
authors.forename: B A
authors.initials: BA
authors.lastname: Herman
authors.forename: J H
authors.initials: JH
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D006801:Humans; D016013:Likelihood Functions; D016011:Normal Distribution; D012372:ROC Curve