Analyzing longitudinal clustered count data with zero inflation: Marginal modeling using the Conway-Maxwell-Poisson distribution.

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 nondietary factors on the caries development of a cohort of Iowa school children at the ages of 5, 9, and 13. To analyze the multiyear IFS data, we propose a novel longitudinal method of a generalized estimating equations based marginal regression model. We use a zero-inflated model with a Conway-Maxwell-Poisson (CMP) distribution, which has the flexibility to account for all levels of dispersion. The parameters of interest

SUBMITTER: Kang T 

PROVIDER: S-EPMC9161738 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature