A general class of hierarchical ordinal regression models with applications to correlated ROC analysis

Hemant Ishwaran, Constantine A. Gatsonis

Research output: Contribution to journalArticle

65 Scopus citations

Abstract

The authors discuss a general class of hierarchical ordinal regression models that includes both location and scale parameters, allows link functions to be selected adaptively as finite mixtures of normal cumulative distribution functions, and incorporates flexible correlation structures for the latent scale variables. Exploiting the well-known correspondence between ordinal regression models and parametric ROC (Receiver Operating Characteristic) curves makes it possible to use a hierarchical ROC (HROC) analysis to study multilevel clustered data in diagnostic imaging studies. The authors present a Bayesian approach to model fitting using Markov chain Monte Carlo methods and discuss HROC applications to the analysis of data from two diagnostic radiology studies involving multiple interpreters.

Original languageEnglish (US)
Pages (from-to)731-750
Number of pages20
JournalCanadian Journal of Statistics
Volume28
Issue number4
DOIs
StatePublished - Dec 2000
Externally publishedYes

Keywords

  • Bayesian hierarchical model
  • Gibbs sampling
  • HROC model
  • Ordinal categorical data
  • Ordinal regression
  • ROC curve

ASJC Scopus subject areas

  • Statistics and Probability

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