Journal article
Analyzing longitudinal clustered count data with zero inflation: marginal modeling using the Conway-Maxwell-Poisson distribution
Biometrical journal, Vol.63(4), pp.761-786
01/04/2021
DOI: 10.1002/bimj.202000061
PMCID: PMC9161738
PMID: 33393147
Abstract
Biological and medical researchers often collect count data in clusters
at multiple time points. The data can exhibit excessive zeros and a wide range
of dispersion levels. In particular, our research was motivated by a dental
dataset with such complex data features: the Iowa Fluoride Study (IFS). The
study was designed to investigate the effects of various dietary and non-dietary
factors on the caries development of a cohort of Iowa school children at the
ages of 5, 9, and 13. To analyze the multi-year IFS data, we propose a novel
longitudinal method of a GEE-based marginal regression model. We use a
zero-inflated mixture model with a Conway-Maxwell-Poisson (CMP) distribution,
which has the flexibility to account for all levels of dispersion. The
parameters of interest are estimated through a modified expectation-solution
algorithm to account for the clustered and temporal correlation structure. We
fit the proposed zero-inflated CMP model and perform a comprehensive secondary
analysis of the IFS dataset. It resulted in a number of notable conclusions that
also make clinical sense. Additionally, we demonstrated the superiority of this
modeling approach over two other popular competing models: the zero-inflated
Poisson and negative binomial models. In the simulation studies, we further
evaluate the performance of our point estimators, the variance estimators, and
that of the large sample confidence intervals for the parameters of interest. It
is also justified that our longitudinal CMP model can correctly identify the
time-varying dispersion patterns.
Details
- Title: Subtitle
- Analyzing longitudinal clustered count data with zero inflation: marginal modeling using the Conway-Maxwell-Poisson distribution
- Creators
- Tong Kang - University of FloridaSteven M. Levy - University of IowaSomnath Datta - University of Florida
- Resource Type
- Journal article
- Publication Details
- Biometrical journal, Vol.63(4), pp.761-786
- DOI
- 10.1002/bimj.202000061
- PMID
- 33393147
- PMCID
- PMC9161738
- NLM abbreviation
- Biom J
- ISSN
- 0323-3847
- eISSN
- 1521-4036
- Grant note
- DOI: 10.13039/100000072, name: National Institute of Dental and Craniofacial Research, award: 1R03DE026757‐01A1
- Language
- English
- Date published
- 01/04/2021
- Academic Unit
- Preventive and Community Dentistry; Epidemiology
- Record Identifier
- 9984367624502771
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