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

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


Abstract

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.

Citation

(2021). Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models. International Journal of Epidemiology, 615-625. https://doi.org/10.1093/ije/dyab256

Acceptance Date Nov 24, 2021
Publication Date Dec 17, 2021
Journal International Journal of Epidemiology
Print ISSN 0300-5771
Publisher Oxford University Press
Pages 615-625
DOI https://doi.org/10.1093/ije/dyab256
Keywords Prediction, prognostic model, external validation, competing risks, calibration, discrimination
Publisher URL https://doi.org/10.1093/ije/dyab256

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