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Activity Number: 392 - Bayesian Analysis of Complex, Structured Health and Social Data
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #322049
Title: Analyzing Dental Fluorosis Data Using a Novel Bayesian Model for Clustered Longitudinal Outcomes with an Inflated Category
Author(s): Tong Kang* and Jeremy Gaskins and Steven Levy and Somnath Datta
Companies: Bristol Myers Squibb and University of Louisville and University of Iowa and University of Florida
Keywords: Bayesian modeling; categorical regression; clustered data; hurdle model; ordinal variable

We propose a Bayesian hurdle mixed-effects model to analyze longitudinal ordinal data under a complex multilevel structure. This research was motivated by data gathered from the Iowa Fluoride Study to establish the relationships between fluorosis status and potential risk/protective factors. Dental fluorosis is characterized by spots on tooth enamel and is due to excessive fluoride intake during enamel formation. The observations not only exhibit a complex hierarchical structure, but also have a large proportion of zero values that are likely to follow different statistical patterns from non-zero categories. Therefore, we develop a hurdle model to consider the zero category separately, while a proportional odds model is used for the positive categories. The estimated parameters are obtained from a Gibbs sampler using OpenBUGS software. Our model is compared with two popular methods for ordinal data: the proportional odds model and the partial proportional odds model. We perform a comprehensive data analysis and evaluate the accuracy and effectiveness of our methodology through simulation studies. Our discoveries provide novel insights to statisticians and dental practitioners.

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

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