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Lookup NU author(s): Dr Clare LendremORCiD, Dr Dennis LendremORCiD, Dr Arthur PrattORCiD, 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.
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© 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.
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
URL: https://doi.org/10.1002/pst.1963
DOI: 10.1002/pst.1963
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