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Discrimination and Calibration of Clinical Prediction Models

Discrimination and Calibration of Clinical Prediction Models Accurate information regarding prognosis is fundamental to optimal clinical care. The best approach to assess patient prognosis relies on prediction models that simultaneously consider a number of prognostic factors and provide an estimate of patients’ absolute risk of an event. Such prediction models should be characterized by adequately discriminating between patients who will have an event and those who will not and by adequate calibration ensuring accurate prediction of absolute risk. This Users’ Guide will help clinicians understand the available metrics for assessing discrimination, calibration, and the relative performance of different prediction models. This article complements existing Users’ Guides that address the development and validation of prediction models. Together, these guides will help clinicians to make optimal use of existing prediction models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA American Medical Association

Discrimination and Calibration of Clinical Prediction Models

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References (36)

Publisher
American Medical Association
Copyright
Copyright 2017 American Medical Association. All Rights Reserved.
ISSN
0098-7484
eISSN
1538-3598
DOI
10.1001/jama.2017.12126
pmid
29049590
Publisher site
See Article on Publisher Site

Abstract

Accurate information regarding prognosis is fundamental to optimal clinical care. The best approach to assess patient prognosis relies on prediction models that simultaneously consider a number of prognostic factors and provide an estimate of patients’ absolute risk of an event. Such prediction models should be characterized by adequately discriminating between patients who will have an event and those who will not and by adequate calibration ensuring accurate prediction of absolute risk. This Users’ Guide will help clinicians understand the available metrics for assessing discrimination, calibration, and the relative performance of different prediction models. This article complements existing Users’ Guides that address the development and validation of prediction models. Together, these guides will help clinicians to make optimal use of existing prediction models.

Journal

JAMAAmerican Medical Association

Published: Oct 10, 2017

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