Article Text

other Versions

PDF
Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker
  1. Karel G M Moons1,
  2. Andre Pascal Kengne1,2,3,
  3. Mark Woodward2,4,
  4. Patrick Royston5,
  5. Yvonne Vergouwe1,
  6. Douglas G Altman6,
  7. Diederick E Grobbee1
  1. 1Julius Centre for Health Sciences and Primary Care, UMC Utrecht, Utrecht, The Netherlands
  2. 2Cardiovascular Division, The George Institute for Global Health, University of Sydney, Sydney, Australia
  3. 3NCRP for Cardiovascular and Metabolic Diseases, South African Medical Research Council, University of Cape Town, Cape Town, South Africa
  4. 4Department of Epidemiology, Johns Hopkins University, Baltimore, USA
  5. 5MRC Clinical Trials Unit, London, UK
  6. 6Centre for Statistics in Medicine, University of Oxford, Oxford, UK
  1. Correspondence to Professor Karel Moons, Julius Center for Health Sciences and Primary Care, UMC Utrecht, PO Box 85500; 3508 GA Utrecht. The Netherlands; k.g.m.moons{at}umcutrecht.nl

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.

  • Prediction model
  • risk prediction
  • model development
  • internal validation
  • model improvement
  • reclassification
  • added value
  • biomarkers
  • obesity
  • clinical hypertension
  • prevention
  • diabetes
  • general practice
  • epidemiology

Statistics from Altmetric.com

Footnotes

  • KGMM and APK contributed equally.

  • Funding Karel GM Moons receives funding from the Netherlands Organisation for Scientific Research (project 9120.8004 and 918.10.615).

  • Competing interests None.

  • Provenance and peer review Commissioned; externally peer reviewed.

Request permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Linked Articles