Guide to evaluating performance of prediction models for recurrent clinical events
Inappropriate models can lead to incorrect conclusions which disadvantage patients. Therefore, prediction models for outcomes associated with chronic conditions should include all repeated events. Such models can be evaluated via the promoted numerical and graphical approaches alongside modified calibration measures.
Authors
- Anthony G Marson (ORCID: https://orcid.org/0000-0002-6861-8806)
- Laura Bonnett (ORCID: https://orcid.org/0000-0002-6981-9212)
- Victoria Watson (ORCID: https://orcid.org/0000-0003-2519-4588)
- Alexandra Hunt (ORCID: https://orcid.org/0000-0001-5510-0285)
- Jane L. Hutton (ORCID: https://orcid.org/0000-0003-0963-9997)
- John Blakey (ORCID: https://orcid.org/0000-0003-2551-8984)
- Thomas Spain
Institutions
- University of Liverpool (GB)
- Curtin University (AU)
- Sir Charles Gairdner Hospital (AU)
- University of Warwick (GB)
Publication Details
- Journal
- Diagnostic and Prognostic Research
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1186/s41512-025-00187-7
- Citations
- 1
- Primary Topic
- Epilepsy research and treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- National Institute for Health and Care Research
- Academy of Medical Sciences