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Stat Med. 1998 May 15;17(9):1033-53.

Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data.

Author information

1
Department of Radiology, University of Chicago Medical Center, IL 60637-1470, USA. c-metz@uchicago.edu

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.

PMID:
9612889
[Indexed for MEDLINE]

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