Literature DB >> 33368533

A longitudinal Bayesian mixed effects model with hurdle Conway-Maxwell-Poisson distribution.

Tong Kang1, Jeremy Gaskins2, Steven Levy3, Somnath Datta1.   

Abstract

Dental caries (i.e., cavities) is one of the most common chronic childhood diseases and may continue to progress throughout a person's lifetime. The Iowa Fluoride Study (IFS) was designed to investigate the effects of various fluoride, dietary and nondietary factors on the progression of dental caries among a cohort of Iowa school children. We develop a mixed effects model to perform a comprehensive analysis of the longitudinal clustered data of IFS at ages 5, 9, 13, and 17. 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 distribution is used to share the temporal information for predictors in the fixed-effects model. The dependence among 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 statistical methodology. The results of this article provide novel tools to statistical practitioners and offer fresh insights to dental researchers on effects of various risk and protective factors on caries progression.
© 2020 John Wiley & Sons, Ltd.

Entities:  

Keywords:  Bayesian analysis; Conway-Maxwell-Poisson distribution; Hurdle model; longitudinal data; mixed effects model

Mesh:

Year:  2020        PMID: 33368533      PMCID: PMC9167575          DOI: 10.1002/sim.8844

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.497


  11 in total

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Authors:  S L Zeger; K Y Liang
Journal:  Stat Med       Date:  1992 Oct-Nov       Impact factor: 2.373

2.  Extension of the application of conway-maxwell-poisson models: analyzing traffic crash data exhibiting underdispersion.

Authors:  Dominique Lord; Srinivas Reddy Geedipally; Seth D Guikema
Journal:  Risk Anal       Date:  2010-04-20       Impact factor: 4.000

3.  On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data.

Authors:  C E Rose; S W Martin; K A Wannemuehler; B D Plikaytis
Journal:  J Biopharm Stat       Date:  2006       Impact factor: 1.051

4.  The zero-inflated negative binomial regression model with correction for misclassification: an example in caries research.

Authors:  Samuel M Mwalili; Emmanuel Lesaffre; Dominique Declerck
Journal:  Stat Methods Med Res       Date:  2007-08-14       Impact factor: 3.021

5.  Fixed effects, random effects and GEE: what are the differences?

Authors:  Joseph C Gardiner; Zhehui Luo; Lee Anne Roman
Journal:  Stat Med       Date:  2009-01-30       Impact factor: 2.373

6.  Zero-inflated and hurdle models of count data with extra zeros: examples from an HIV-risk reduction intervention trial.

Authors:  Mei-Chen Hu; Martina Pavlicova; Edward V Nunes
Journal:  Am J Drug Alcohol Abuse       Date:  2011-09       Impact factor: 3.829

7.  Analyzing clustered count data with a cluster specific random effect zero-inflated Conway-Maxwell-Poisson distribution.

Authors:  Hyoyoung Choo-Wosoba; Somnath Datta
Journal:  J Appl Stat       Date:  2017-04-08       Impact factor: 1.404

8.  A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use.

Authors:  Brian H Neelon; A James O'Malley; Sharon-Lise T Normand
Journal:  Stat Modelling       Date:  2010-12       Impact factor: 2.039

9.  Fluoride, beverages and dental caries in the primary dentition.

Authors:  S M Levy; J J Warren; B Broffitt; S L Hillis; M J Kanellis
Journal:  Caries Res       Date:  2003 May-Jun       Impact factor: 4.056

10.  A Bayesian approach for analyzing zero-inflated clustered count data with dispersion.

Authors:  Hyoyoung Choo-Wosoba; Jeremy Gaskins; Steven Levy; Somnath Datta
Journal:  Stat Med       Date:  2017-11-06       Impact factor: 2.373

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