Lessons learnt when accounting for competing events in the external validation of time-to-event prognostic models
International Journal of Epidemiology, vol. 51, pp. 615–625
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.
Authors 8
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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Affiliation as printed
Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, UK
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Affiliation as printed
Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
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Karolinska University Hospital · Karolinska Institutet
Affiliation as printed
Division of Renal Medicine, Department of Clinical Science, Intervention and Technology, Karolinska Institutet and Karolinska University hospital, Stockholm, Sweden
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Affiliation as printed
Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
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Utrecht University · University Medical Center Utrecht
Affiliation as printed
Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
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