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 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.
Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA. wenqi_wu@baylor.edu.
Wenqi
W
Wu
Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA. james_stamey@baylor.edu.
James
J
Stamey
Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX, 76706, USA. david.kahle@gmail.com.
David
D
Kahle
D001499:Bayes Theorem; D004812:Epidemiologic Methods; D008962:Models, Theoretical; D016012:Poisson Distribution
count data; misclassification; overdispersion