Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data.
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.
Metz
C E
CE
Department of Radiology, University of Chicago Medical Center, IL 60637-1470, USA. c-metz@uchicago.edu
Herman
B A
BA
Shen
J H
JH
D000465:Algorithms; D006801:Humans; D016013:Likelihood Functions; D016011:Normal Distribution; D012372:ROC Curve