Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

Karel G.M. Moons, Andre Pascal Kengne, Mark Woodward, Patrick Royston, Yvonne Vergouwe, Douglas G. Altman, Diederick E. Grobbee

Research output: Contribution to journalReview articlepeer-review

Abstract

Prediction models are increasingly used to complement clinical reasoning and decision making in modern medicine in general, and in the cardiovascular domain in particular. Developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in targeted patients. The adoption of such models must guide physician's decision making and an individual's behaviour, and consequently improve individual outcomes and the cost-effectiveness of care. In a series of two articles we review the consecutive steps generally advocated for risk prediction model research. This first article focuses on the different aspects of model development studies, from design to reporting, how to estimate a model's predictive performance and the potential optimism in these estimates using internal validation techniques, and how to quantify the added or incremental value of new predictors or biomarkers (of whatever type) to existing predictors. Each step is illustrated with empirical examples from the cardiovascular field.

Original languageEnglish (US)
Pages (from-to)683-690
Number of pages8
JournalHeart
Volume98
Issue number9
DOIs
StatePublished - May 2012

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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