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

Stat Med. 1998 May 15;17(9):1033-53. doi: 10.1002/(sici)1097-0258(19980515)17:9<1033::aid-sim784>;2-z.


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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Humans
  • Likelihood Functions*
  • Normal Distribution
  • ROC Curve*