biostudies-literatureRamspek CLUK National Institute for Health Research (NIHR) Applied Research Collaboration East MidlandsNational Institute for Health Research School for Primary Care ResearchDutch Kidney FoundationNational Institute for Health Research (NIHR)NIHR or the Department of Health and Social CareCenter for Innovative Medicine (CIMED) and ALF Medicin615-625https://www.ebi.ac.uk/biostudies/studies/S-EPMC9082803biostudies-literatureUnknown51(2)<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.International journal of epidemiologyLessons learnt when accounting for competing events in the external validation of time-to-event prognostic models.PMC908280316OKG12SPCR-142Teece LSnell KIEvan Diepen Mvan Smeden MEvans MRamspek CLRiley RDvan Geloven NfalseLessons learnt when accounting for competing events in the external validation of time-to-event prognostic models.<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.2022-01-01T00:00:00Z2022 May2025-04-04T20:21:56.257Z2025-04-04T20:21:56.257ZS-EPMC90828033491969110.1093/ije/dyab256