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

Lookup NU author(s): Dr Clare LendremORCiD, Dr Dennis LendremORCiD, Dr Arthur Pratt, Najib NaamaneORCiD, Dr Peter McMeekin, Professor Fai NgORCiD, Dr Joy AllenORCiD, Dr Michael Power, Professor John IsaacsORCiD



This is the authors' accepted manuscript of an article that has been published in its final definitive form by John Wiley and Sons Ltd, 2019.

For re-use rights please refer to the publisher's terms and conditions.


© 2019 John Wiley & Sons, Ltd.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.

Publication metadata

Author(s): Lendrem BC, Lendrem DW, Pratt AG, Naamane N, McMeekin P, Ng W-F, Allen AJ, Power M, Isaacs JD

Publication type: Article

Publication status: Published

Journal: Pharmaceutical Statistics

Year: 2019

Volume: 18

Issue: 6

Pages: 632-635

Print publication date: 17/11/2019

Online publication date: 23/06/2019

Acceptance date: 15/05/2019

Date deposited: 29/07/2019

ISSN (print): 1539-1604

ISSN (electronic): 1539-1612

Publisher: John Wiley and Sons Ltd


DOI: 10.1002/pst.1963


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