Between a ROC and a hard place: Teaching prevalence plots to understand real world biomarker performance in the clinic. (23rd June 2019)

• Record Type: Journal Article
• Authors: Lendrem, B. Clare; Lendrem, Dennis W.; Pratt, Arthur G.; Naamane, Najib; McMeekin, Peter; Ng, Wan‐Fai; Allen, A. Joy; Power, Michael; Isaacs, John Dudley
• Is Part Of: Pharmaceutical statistics. Volume 18:Number 6(2019)
• Page Start: 632
• Page End: 635
• Publication Date: 2019-06-23
• Subjects: assay performance -- biomarker -- prevalence -- ROC AUC -- sensitivity -- specificity; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.1963
• Languages: English
• ISSNs: 1539-1604

Abstract

Summary: The Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) of the ROC curve are widely used in discovery to compare the performance of diagnostic and prognostic assays. The ROC curve has the advantage that it is independent of disease prevalence. However, in this note, we remind scientists and clinicians that the performance of an assay upon translation to the clinic is critically dependent upon that very same prevalence. Without an understanding of prevalence in the test population, even robust bioassays with excellent ROC characteristics may perform poorly in the clinic. While the exact prevalence in the target population is not always known, simple plots of candidate assay performance as a function of prevalence rate give a better understanding of the likely real‐world performance and a greater understanding of the likely impact of variation in that prevalence on translation to the clinic.