From algorithms to action: improving patient care requires causality

Wouter A. C. van Amsterdam, Pim A. de Jong, Joost J. C. Verhoeff, Tim Leiner, Rajesh Ranganath

Research output: Contribution to journalComment/Letter to the editorAcademic

Abstract

In cancer research there is much interest in building and validating outcome prediction models to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee on Cancer do not protect against prediction models that are accurate during development and validation but harmful when used for decision making. We explain why this is the case and how to build and validate models that are useful for decision making.
Original languageEnglish
Article number111
JournalBMC medical informatics and decision making
Volume24
Issue number1
DOIs
Publication statusPublished - 1 Dec 2024

Keywords

  • Causal inference
  • Oncology
  • Prediction research
  • Prognosis research
  • Tailored treatment decision making

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