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-Reference-Cited by-同舟云学术

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

Published:2017-11-16 Issue:1 Volume:1 Page:
ISSN:2397-7523
Container-title:Diagnostic and Prognostic Research
language:en
Short-container-title:Diagn Progn Res

Author:

van Doorn S.^ORCID,Brakenhoff T. B.,Moons K. G. M.,Rutten F. H.,Hoes A. W.,Groenwold R. H. H.,Geersing G. J.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,General Mathematics

Link

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

Reference29 articles.

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2. Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. doi: 10.1371/journal.pmed.1001885 .

3. Buonaccorsi JP. Measurement Error. New York: CRC Press; 2010.

4. Gustafson P. Measurement error and misclassification in statistics and epidemiology: impacts and Bayesian adjustments. New York: CRC Press; 2003.

5. Gustafson P, Greenland S. Misclassification. Handbook of epidemiology. New York: Springer; 2014. p. 639–58.

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