Marginalized zero-inflated negative binomial regression with application to dental caries.
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A marginalized zero-inflated negative binomial regression model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation.Abstract:
The zero-inflated negative binomial regression model (ZINB) is often employed in diverse fields such as dentistry, health care utilization, highway safety, and medicine to examine relationships between exposures of interest and overdispersed count outcomes exhibiting many zeros. The regression coefficients of ZINB have latent class interpretations for a susceptible subpopulation at risk for the disease/condition under study with counts generated from a negative binomial distribution and for a non-susceptible subpopulation that provides only zero counts. The ZINB parameters, however, are not well-suited for estimating overall exposure effects, specifically, in quantifying the effect of an explanatory variable in the overall mixture population. In this paper, a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses is proposed to model the population marginal mean count directly, providing straightforward inference for overall exposure effects based on maximum likelihood estimation. Through simulation studies, the finite sample performance of MZINB is compared with marginalized zero-inflated Poisson, Poisson, and negative binomial regression. The MZINB model is applied in the evaluation of a school-based fluoride mouthrinse program on dental caries in 677 children.read more
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Corrigendum: The zero‐inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology
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References
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Zero-inflated Poisson regression, with an application to defects in manufacturing
TL;DR: Zero-inflated Poisson (ZIP) regression as discussed by the authors is a model for counting data with excess zeros, which assumes that with probability p the only possible observation is 0, and with probability 1 − p, a Poisson(λ) random variable is observed.
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Specification and testing of some modified count data models
TL;DR: These alternatives permit more flexible specification of the data-generating process (dgp) than do familiar count data models, and provide a natural means for modeling data that are over- or underdispersed by the standards of the basic models.
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Studies on Dental Caries. I. Dental Status and Dental Needs of Elementary School Children.
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The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology
TL;DR: In this article, a zero-inflated Poisson (ZIP) model is proposed for frequency counts in a dental epidemiological study in Belo Horizonte, Brazil, which evaluated various programmes for reducing caries.
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Random effect models for repeated measures of zero-inflated count data:
Yongyi Min,Alan Agresti +1 more
TL;DR: In this article, random effect models for repeated measurements of zero-inflated count responses are discussed. But, the problem of extra zeros, the correlation between measurements upon the same subject at different occasions needs to be taken into account.