A Bayesian approach for analyzing zero‐inflated clustered count data with dispersion

Hyoyoung Choo-Wosoba; Jeremy Gaskins; Steven Levy; Somnath Datta

doi:10.1002/sim.7541

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A Bayesian approach for analyzing zero‐inflated clustered count data with dispersion

Journal article

Peer reviewed

A Bayesian approach for analyzing zero‐inflated clustered count data with dispersion

Hyoyoung Choo-Wosoba, Jeremy Gaskins, Steven Levy and Somnath Datta

Statistics in medicine, Vol.37(5), pp.801-812

02/28/2018

DOI: 10.1002/sim.7541

PMCID: PMC5799048

PMID: 29108124

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Abstract

In practice, count data may exhibit varying dispersion patterns and excessive zero values; additionally, they may appear in groups or clusters sharing a common source of variation. We present a novel Bayesian approach for analyzing such data. To model these features, we combine the Conway‐Maxwell‐Poisson distribution, which allows both overdispersion and underdispersion, with a hurdle component for the zeros and random effects for clustering. We propose an efficient Markov chain Monte Carlo sampling scheme to obtain posterior inference from our model. Through simulation studies, we compare our hurdle Conway‐Maxwell‐Poisson model with a hurdle Poisson model to demonstrate the effectiveness of our Conway‐Maxwell‐Poisson approach. Furthermore, we apply our model to analyze an illustrative dataset containing information on the number and types of carious lesions on each tooth in a population of 9‐year‐olds from the Iowa Fluoride Study, which is an ongoing longitudinal study on a cohort of Iowa children that began in 1991.

Bayesian modeling

clustering

Conway‐Maxwell‐Poisson distribution

count data

zero inflation

Details

Title: Subtitle: A Bayesian approach for analyzing zero‐inflated clustered count data with dispersion
Creators: Hyoyoung Choo-Wosoba - National Institutes of Health
Jeremy Gaskins - University of Louisville
Steven Levy - University of Iowa
Somnath Datta - University of Florida
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.37(5), pp.801-812
DOI: 10.1002/sim.7541
PMID: 29108124
PMCID: PMC5799048
ISSN: 0277-6715
eISSN: 1097-0258
Number of pages: 12
Grant note: National Institutes of Health (1R03DE020839-01A1; 1R03DE022538-01; R01-DE09551; R01-DE12101; M01-RR000594)
Language: English
Date published: 02/28/2018
Academic Unit: Preventive and Community Dentistry; Epidemiology
Record Identifier: 9984367718702771

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