Four overlooked errors in ROC analysis: How to prevent and avoid

Authors

Zhuoqiao He, Edith Cowan UniversityFollow
Qingying Zhang
Manshu Song, Edith Cowan UniversityFollow
Xuerui Tan
Wei Wang, Edith Cowan UniversityFollow

Author Identifier

Manshu Song: https://orcid.org/0000-0003-1433-7192

Wei Wang: https://orcid.org/0000-0002-1430-1360

Document Type

Journal Article

Publication Title

BMJ Evidence-Based Medicine

PubMed ID

39304213

Publisher

BMJ Publishing Group

School

Centre for Precision Health / School of Medical and Health Sciences

RAS ID

72545

Funders

National Natural Science Foundation of China (82073659) / Funding for Guangdong Medical Leading Talent / First Affiliated Hospital / Shantou University Medical College, China / Grant for Key Disciplinary Project of Clinical Medicine under the High-level University Development Program, Guangdong / Western Australian Future Health Research and Innovation Fund (WANMA/Ideas2023-24/10) / Guangdong Graduate Education Innovation Plan Project (2021SFKC039) / ECU-SUMC collaborative PhD project

Comments

He, Z., Zhang, Q., Song, M., Tan, X., & Wang, W. (2025). Four overlooked errors in ROC analysis: how to prevent and avoid. BMJ Evidence-Based Medicine, 30(3), 208-211. https://doi.org/10.1136/bmjebm-2024-113078

Abstract

Diagnostic tests are frequently applied within clinical practice to assist with disease diagnosis, differential diagnosis, disease grading and prognosis evaluation. Receiver operating characteristic (ROC) curve analysis is one common approach for analysing discriminative performance of a diagnostic test, where it can determine the optimal cut-off value with the best diagnostic performance.1 However, as a majority of clinicians are non-statisticians, several errors have been observed in clinical research when applying ROC curves. These errors may be misleading in the selection of diagnostic tests and disease diagnosis, thus adding to patient burden. To address these errors, clinicians do not need a deep understanding of the intricate mathematical formulas of ROC analysis, but should develop basic knowledge and skills to prevent or avoid commonly overlooked mistakes. This article aims to guide clinicians to avoid common pitfalls in ROC analysis.

DOI

10.1136/bmjebm-2024-113078

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.