Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with applications

Hyoyoung Choo-Wosoba; Steven M Levy; Somnath Datta

doi:10.1111/biom.12436

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Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with applications

Journal article

Peer reviewed

Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with applications

Hyoyoung Choo-Wosoba, Steven M Levy and Somnath Datta

Biometrics, Vol.72(2), pp.606-618

06/2016

DOI: 10.1111/biom.12436

PMCID: PMC4948193

PMID: 26575079

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https://www.ncbi.nlm.nih.gov/pmc/articles/4948193View

Open Access

Abstract

Community water fluoridation is an important public health measure to prevent dental caries, but it continues to be somewhat controversial. The Iowa Fluoride Study (IFS) is a longitudinal study on a cohort of Iowa children that began in 1991. The main purposes of this study (http://www.dentistry.uiowa.edu/preventive-fluoride-study) were to quantify fluoride exposures from both dietary and nondietary sources and to associate longitudinal fluoride exposures with dental fluorosis (spots on teeth) and dental caries (cavities). We analyze a subset of the IFS data by a marginal regression model with a zero-inflated version of the Conway-Maxwell-Poisson distribution for count data exhibiting excessive zeros and a wide range of dispersion patterns. In general, we introduce two estimation methods for fitting a ZICMP marginal regression model. Finite sample behaviors of the estimators and the resulting confidence intervals are studied using extensive simulation studies. We apply our methodologies to the dental caries data. Our novel modeling incorporating zero inflation, clustering, and overdispersion sheds some new light on the effect of community water fluoridation and other factors. We also include a second application of our methodology to a genomic (next-generation sequencing) dataset that exhibits underdispersion.

Confidence Intervals

Data Interpretation, Statistical

Genomics

Humans

Models, Statistical

Biometry - methods

Regression Analysis

Computer Simulation

Fluoridation

Poisson Distribution

High-Throughput Nucleotide Sequencing

Drinking Water

Cluster Analysis

Details

Title: Subtitle: Marginal regression models for clustered count data based on zero-inflated Conway-Maxwell-Poisson distribution with applications
Creators: Hyoyoung Choo-Wosoba - Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, Kentucky 40202, U.S.A
Steven M Levy - Department of Preventive and Community Dentistry Department of Epidemiology, University of Iowa, Iowa City, Iowa 52242, U.S.A
Somnath Datta - Department of Biostatistics, University of Florida, Gainesville, Florida 32610, U.S.A
Resource Type: Journal article
Publication Details: Biometrics, Vol.72(2), pp.606-618
DOI: 10.1111/biom.12436
PMID: 26575079
PMCID: PMC4948193
NLM abbreviation: Biometrics
ISSN: 1541-0420
eISSN: 1541-0420
Publisher: United States
Grant note: R03 DE022538 / NIDCR NIH HHS M01 RR000059 / NCRR NIH HHS R01 DE009551 / NIDCR NIH HHS R03 DE020839 / NIDCR NIH HHS R01 DE012101 / NIDCR NIH HHS UL1 TR000442 / NCATS NIH HHS R56 DE012101 / NIDCR NIH HHS
Language: English
Date published: 06/2016
Academic Unit: Preventive and Community Dentistry; Epidemiology
Record Identifier: 9983917665402771

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