Between a ROC and a hard place: Teaching prevalence plots to understand real world biomarker performance in the clinic

B. Clare Lendrem, Dennis W. Lendrem, Arthur G. Pratt, Najib Naamane, Peter McMeekin, Wan Fai Ng, A. Joy Allen, Michael Power*, John Dudley Isaacs

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
2 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)632-635
Number of pages4
JournalPharmaceutical Statistics
Volume18
Issue number6
Early online date23 Jun 2019
DOIs
Publication statusPublished - 1 Nov 2019

Fingerprint Dive into the research topics of 'Between a ROC and a hard place: Teaching prevalence plots to understand real world biomarker performance in the clinic'. Together they form a unique fingerprint.

Cite this