The effects of misclassification in routine healthcare databases on the accuracy of prognostic prediction models: a case study of the CHA2DS2-VASc score in atrial fibrillation

The effects of misclassification in routine healthcare databases on the accuracy of prognostic prediction models: a case study of the CHA2DS2-VASc score in atrial fibrillation

Abstract Background Research on prognostic prediction models frequently uses data from routine healthcare. However, potential misclassification of predictors when using such data may strongly affect the studied associations. There is no doubt that such misclassification could lead to the derivation...

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Bibliographic Details
Main Authors: S. van Doorn, T. B. Brakenhoff, K. G. M. Moons, F. H. Rutten, A. W. Hoes, R. H. H. Groenwold, G. J. Geersing
Format: Article
Language:English
Published: BMC 2017-11-01
Series:Diagnostic and Prognostic Research
Subjects:
Routine care data
Validation
Prediction model
Atrial fibrillation
CHA2DS2-VASc
Misclassification
Online Access:http://link.springer.com/article/10.1186/s41512-017-0018-x

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http://link.springer.com/article/10.1186/s41512-017-0018-x

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