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

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 so...

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Bibliographic Details
Published inInternational journal of environmental research and public health Vol. 12; no. 9; pp. 10648 - 10661
Main Authors Wu, Wenqi, Stamey, James, Kahle, David
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 28.08.2015
MDPI
Subjects
Bayes Theorem
Bayesian analysis
Data analysis
Epidemiologic Methods
Errors
Models, Theoretical
Poisson Distribution
overdispersion
count data
misclassification
Online AccessGet full text
ISSN1660-4601
1661-7827
1660-4601
DOI10.3390/ijerph120910648

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Summary: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.
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ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph120910648