A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable-Reference-Cited by-同舟云学术

A comparison of sensitivity-specificity imputation, direct imputation and fully Bayesian analysis to adjust for exposure misclassification when validation data are unavailable

Published:2017-03-14 Issue:3 Volume:46 Page:1063-1072
ISSN:0300-5771
Container-title:International Journal of Epidemiology
language:en
Short-container-title:

Author:

Corbin Marine,Haslett Stephen,Pearce Neil,Maule Milena,Greenland Sander

Publisher

Oxford University Press (OUP)

Subject

General Medicine,Epidemiology

Link

http://academic.oup.com/ije/article-pdf/46/3/1063/19678568/dyx027.pdf

Reference29 articles.

1. Validity in epidemiologic studies,2008

2. Bayesian perspectives for epidemiologic research: III. Bias analysis via missing-data methods;Int J Epidemiol,2009

3. Multiple-imputation for measurement-error correction;Int J Epidemiol,2006

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