GEE-based zero-inflated generalized Poisson model for clustered over or under-dispersed count data

ABSTRACT

The zero-inflated regression models such as zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB) or zero-inflated generalized Poisson (ZIGP) regression models can model the count data with excess zeros. The ZINB model can handle over-dispersed and the ZIGP model can handle the over or under-dispersed count data with excess zeros as well. Moreover, the count data may be correlated because of data collection procedure or special study design. The clustered sampling approach is one of the examples in which the correlation among subjects could be defined. In such situations, a marginal model using generalized estimating equation (GEE) approach can incorporate these correlations and lead up to the relationships at the population level. In this study, the GEE-based zero-inflated generalized Poisson regression model was proposed to fit over and under-dispersed clustered count data with excess zeros.

KEYWORDS:

• Dispersion
• expectation–solution algorithm
• generalized estimating equation
• generalized Poisson regression
• zero-inflation

Acknowledgments

This study was adapted from a PhD thesis at Hamadan University of Medical sciences.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Abbas Moghimbeigi http://orcid.org/0000-0002-3803-3663

Additional information

Funding

The study was funded by Vice-chancellor for Research and Technology, Hamadan University of Medical Sciences (Grant No.9609286079).