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Activity Number:
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66
- Novel Bayesian Methodology with Health Applications
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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ENAR
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Abstract #313324
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Title:
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A Longitudinal Bayesian Mixed Effects Model with Hurdle Conway-Maxwell-Poisson Distribution
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Author(s):
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Jeremy Thomas Gaskins* and Tong Kang and Somnath Datta and Steven Levy
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Companies:
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University of Louisville and Department of Biostatistics, University of Florida and University of Florida and Department of Preventive and Community Dentistry, University of Iowa
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Keywords:
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longitudinal;
Bayesian;
overdispersion;
mixed models
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Abstract:
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Dental caries (i.e., cavities) is one of the most common chronic childhood diseases, which can progress throughout a person’s lifetime. The Iowa Fluoride 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 school children at the ages of 5, 9, 13 and 17. We use a mixed effects model to perform a comprehensive analysis on the longitudinal clustered data of the Iowa Fluoride Study. We combine a Bayesian hurdle framework with the Conway-Maxwell-Poisson regression model, which can account for both excessive zeros and various levels of dispersion. A hierarchical shrinkage prior is used to share the temporal information for predictors in the fixed-effects model. The dependence between teeth of each individual child is modeled through a sparse covariance structure of the random effects across time. Moreover, we obtain the parameter estimates and credible intervals from a Gibbs sampler. Simulation studies are conducted to assess the accuracy and effectiveness of our approach.
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Authors who are presenting talks have a * after their name.
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