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