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

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 probabilitie... Full description

Journal Title: Heart 2012-05, Vol.98 (9), p.683-690
Main Author: Moons, Karel G M
Other Authors: Kengne, Andre Pascal , Woodward, Mark , Royston, Patrick , Vergouwe, Yvonne , Altman, Douglas G , Grobbee, Diederick E
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: London: BMJ Publishing Group Ltd and British Cardiovascular Society
ID: ISSN: 1355-6037
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recordid: cdi_proquest_miscellaneous_1009129281
title: Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker
format: Article
creator:
  • Moons, Karel G M
  • Kengne, Andre Pascal
  • Woodward, Mark
  • Royston, Patrick
  • Vergouwe, Yvonne
  • Altman, Douglas G
  • Grobbee, Diederick E
subjects:
  • Abridged Index Medicus
  • added value
  • Biological and medical sciences
  • biomarkers
  • Biomarkers - analysis
  • Cardiology. Vascular system
  • Cardiovascular diseases
  • Cardiovascular Diseases - diagnosis
  • Cardiovascular Diseases - metabolism
  • Care and treatment
  • clinical hypertension
  • Cost-Benefit Analysis
  • Decision Making
  • Decision Support Techniques
  • diabetes
  • epidemiology
  • Estimates
  • general practice
  • Health risk assessment
  • Heart attacks
  • Humans
  • internal validation
  • Medical sciences
  • model development
  • model improvement
  • Models
  • Models, Theoretical
  • obesity
  • Prediction model
  • Predictive Value of Tests
  • prevention
  • reclassification
  • Reproducibility of Results
  • Research
  • Researchers
  • Risk
  • risk prediction
  • Studies
ispartof: Heart, 2012-05, Vol.98 (9), p.683-690
description: 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.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1355-6037
fulltext: fulltext
issn:
  • 1355-6037
  • 1468-201X
url: Link


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descriptionPrediction 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.
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publisherLondon: BMJ Publishing Group Ltd and British Cardiovascular Society
subjectAbridged Index Medicus ; added value ; Biological and medical sciences ; biomarkers ; Biomarkers - analysis ; Cardiology. Vascular system ; Cardiovascular diseases ; Cardiovascular Diseases - diagnosis ; Cardiovascular Diseases - metabolism ; Care and treatment ; clinical hypertension ; Cost-Benefit Analysis ; Decision Making ; Decision Support Techniques ; diabetes ; epidemiology ; Estimates ; general practice ; Health risk assessment ; Heart attacks ; Humans ; internal validation ; Medical sciences ; model development ; model improvement ; Models ; Models, Theoretical ; obesity ; Prediction model ; Predictive Value of Tests ; prevention ; reclassification ; Reproducibility of Results ; Research ; Researchers ; Risk ; risk prediction ; Studies
ispartofHeart, 2012-05, Vol.98 (9), p.683-690
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24model improvement
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abstractPrediction 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.
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doi10.1136/heartjnl-2011-301246