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

doi:10.1136/bmj-2021-069249

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

Public Health

Research output: Contribution to journal › Article › Academic › peer-review

5 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 language	English
Article number	e069249
Journal	The BMJ
DOIs	https://doi.org/10.1136/bmj-2021-069249
Publication status	E-pub ahead of print - 24 May 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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Cite this

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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.",

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AU - Van Geloven, Nan

AU - Giardiello, Daniele

AU - Bonneville, Edouard F.

AU - Teece, Lucy

AU - Ramspek, Chava L.

AU - Van Smeden, Maarten

AU - Snell, Kym I.E.

AU - Van Calster, Ben

AU - Pohar-Perme, Maja

AU - Riley, Richard D.

AU - Putter, Hein

AU - Steyerberg, Ewout

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

Abstract

Bibliographical note

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