Validation of prediction models in the presence of competing risks: a guide through modern methods

Nan Van Geloven, Daniele Giardiello, Edouard F. Bonneville, Lucy Teece, Chava L. Ramspek, Maarten Van Smeden, Kym I.E. Snell, Ben Van Calster, Maja Pohar-Perme, Richard D. Riley, Hein Putter, Ewout Steyerberg*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)

Abstract

Thorough validation is pivotal for any prediction model before it can be advocated for use in medical practice. For time-to-event outcomes such as breast cancer recurrence, death from other causes is a competing risk. Model performance measures must account for such competing events. In this article, we present a comprehensive yet accessible overview of performance measures for this competing event setting, including the calculation and interpretation of statistical measures for calibration, discrimination, overall prediction error, and clinical usefulness by decision curve analysis. All methods are illustrated for patients with breast cancer, with publicly available data and R code.

Original languageEnglish
Article numbere069249
JournalThe BMJ
Volume377
Early online date24 May 2022
DOIs
Publication statusPublished - 2022

Bibliographical note

Funding:
No specific funding was given to this study. The research of
MPP is supported by the Slovenian Research Agency (grant P3-0154).

Publisher Copyright:
© Published by the BMJ Publishing Group Limited.

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