Don't be misled: 3 misconceptions about external validation of clinical prediction models

Hannah M. la Roi-Teeuw*, Florien S. van Royen, Anne de Hond, Anum Zahra, Sjoerd de Vries, Richard Bartels, Alex J. Carriero, Sander van Doorn, Zoë S. Dunias, Ilse Kant, Tuur Leeuwenberg, Ruben Peters, Laura Veerhoek, Maarten van Smeden, Kim Luijken

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorAcademicpeer-review

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Abstract

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.

Original languageEnglish
Article number111387
Number of pages6
JournalJournal of Clinical Epidemiology
Volume172
DOIs
Publication statusPublished - Aug 2024

Keywords

  • Artificial intelligence
  • Clinical algorithm
  • Clinical prediction model
  • External validation
  • Internal validation
  • Machine learning
  • Model updating
  • Prediction model
  • Regression modelling
  • Study design

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