Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models

Chava L Ramspek*, Lucy Teece, Kym I E Snell, Marie Evans, Richard D Riley, Maarten van Smeden, Nan van Geloven, Merel van Diepen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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BACKGROUND: External validation of prognostic models is necessary to assess the accuracy and generalizability of the model to new patients. If models are validated in a setting in which competing events occur, these competing risks should be accounted for when comparing predicted risks to observed outcomes.

METHODS: We discuss existing measures of calibration and discrimination that incorporate competing events for time-to-event models. These methods are illustrated using a clinical-data example concerning the prediction of kidney failure in a population with advanced chronic kidney disease (CKD), using the guideline-recommended Kidney Failure Risk Equation (KFRE). The KFRE was developed using Cox regression in a diverse population of CKD patients and has been proposed for use in patients with advanced CKD in whom death is a frequent competing event.

RESULTS: When validating the 5-year KFRE with methods that account for competing events, it becomes apparent that the 5-year KFRE considerably overestimates the real-world risk of kidney failure. The absolute overestimation was 10%age points on average and 29%age points in older high-risk patients.

CONCLUSIONS: It is crucial that competing events are accounted for during external validation to provide a more reliable assessment the performance of a model in clinical settings in which competing risks occur.

Original languageEnglish
Pages (from-to)615-625
Number of pages11
JournalInternational Journal of Epidemiology
Issue number2
Early online date17 Dec 2021
Publication statusPublished - Apr 2022

Bibliographical note

© The Author(s) 2021. Published by Oxford University Press on behalf of the International Epidemiological Association.


  • Prediction
  • prognostic model
  • external validation
  • competing risks
  • calibration
  • discrimination


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