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Open AccessArticle

A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data

Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA
Author to whom correspondence should be addressed.
Academic Editors: Igor Burstyn and Gheorghe Luta
Int. J. Environ. Res. Public Health 2015, 12(9), 10648-10661;
Received: 30 May 2015 / Revised: 17 July 2015 / Accepted: 25 August 2015 / Published: 28 August 2015
(This article belongs to the Special Issue Methodological Innovations and Reflections-1)
Count data are subject to considerable sources of what is often referred to as non-sampling error. Errors such as misclassification, measurement error and unmeasured confounding can lead to substantially biased estimators. It is strongly recommended that epidemiologists not only acknowledge these sorts of errors in data, but incorporate sensitivity analyses into part of the total data analysis. We extend previous work on Poisson regression models that allow for misclassification by thoroughly discussing the basis for the models and allowing for extra-Poisson variability in the form of random effects. Via simulation we show the improvements in inference that are brought about by accounting for both the misclassification and the overdispersion. View Full-Text
Keywords: misclassification; count data; overdispersion misclassification; count data; overdispersion
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MDPI and ACS Style

Wu, W.; Stamey, J.; Kahle, D. A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data. Int. J. Environ. Res. Public Health 2015, 12, 10648-10661.

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