On the binormal predictive receiver operating characteristic curve for the joint assessment of positive and negative predictive values

Gareth Hughes*

*Corresponding author for this work

Research output: Contribution to journalArticle

1 Citation (Scopus)
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Abstract

The predictive receiver operating characteristic (PROC) curve is a diagrammatic format with application in the statistical evaluation of probabilistic disease forecasts. The PROC curve differs from the more well-known receiver operating characteristic (ROC) curve in that it provides a basis for evaluation using metrics defined conditionally on the outcome of the forecast rather than metrics defined conditionally on the actual disease status. Starting from the binormal ROC curve formulation, an overview of some previously published binormal PROC curves is presented in order to place the PROC curve in the context of other methods used in statistical evaluation of probabilistic disease forecasts based on the analysis of predictive values; in particular, the index of separation (PSEP) and the leaf plot. An information theoretic perspective on evaluation is also outlined. Five straightforward recommendations are made with a view to aiding understanding and interpretation of the sometimes-complex patterns generated by PROC curve analysis. The PROC curve and related analyses augment the perspective provided by traditional ROC curve analysis. Here, the binormal ROC model provides the exemplar for investigation of the PROC curve, but potential application extends to analysis based on other distributional models as well as to empirical analysis.

Original languageEnglish
Article number593
JournalEntropy
Volume22
Issue number6
DOIs
Publication statusPrint publication - 26 May 2020

Keywords

  • Bayes' rule
  • Binormal
  • Diagnostic test
  • Evaluation
  • Expected mutual information
  • Leaf plot
  • Negative predictive value
  • Positive predictive value
  • Prevalence
  • PROC curve
  • ROC curve

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