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Activity Number: 522 - Contributed Poster Presentations: Biometrics Section
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #304869
Title: A Longitudinal Bayesian Mixed Effects Model with Hurdle Conway-Maxwell-Poisson Distribution
Author(s): Tong Kang* and Somnath Datta and Jeremy T. Gaskins
Companies: University of Florida and University of Florida and University of Louisville
Keywords: Longitudinal data; Bayesian analysis; mixed effects model; hurdle model; Conway-Maxwell-Poisson distribution

Mixed effects models are commonly used to analyze longitudinal clustered data. When the data contain excessive zeros, the hurdle mixture model separates zero values and non-zero values and provides a straightforward interpretation. We combine a Bayesian hurdle framework with the Conway-Maxwell-Poisson regression model, which can flexibly account for all levels of dispersion. Extensive simulation studies are conducted to assess the effectiveness of our approach. We also compare the Conway-Maxwell-Poisson model with other models, such as the hurdle Poisson model, the hurdle negative binomial model, and the hurdle quasi-Poisson model. Moreover, we apply our methodology to perform a comprehensive analysis on a longitudinal dental dataset of the Iowa Fluoride Study. The study was designed to investigate the effects of various dietary and non-dietary factors on the progression of dental caries among a cohort of Iowa children at the ages of 5, 9, 13 and 17. The results offer potentially new insights to both statistical practitioners and dental researchers.

Authors who are presenting talks have a * after their name.

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