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Marginalized zero-inflated negative binomial regression with application to dental caries.

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A new marginalized zero-inflated negative binomial regression (MZINB) model directly estimates overall exposure effects. This approach offers straightforward inference for population-level analysis, outperforming traditional models in simulations.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Traditional zero-inflated negative binomial regression (ZINB) models are used for overdispersed count data with excess zeros in fields like healthcare and public health.
  • ZINB model parameters are not ideal for estimating overall exposure effects in the total population, limiting their application in public health evaluations.
  • Existing models struggle to quantify the impact of explanatory variables on the entire population mixture, hindering comprehensive analysis.

Purpose of the Study:

  • To propose a marginalized zero-inflated negative binomial regression (MZINB) model for independent responses.
  • To enable direct modeling of the population marginal mean count for straightforward inference on overall exposure effects.
  • To evaluate the performance of the MZINB model compared to other regression models.

Main Methods:

  • Development of the marginalized zero-inflated negative binomial regression (MZINB) model.
  • Maximum likelihood estimation for parameter estimation in the MZINB model.
  • Simulation studies comparing MZINB with marginalized zero-inflated Poisson, Poisson, and negative binomial regression models.
  • Application of the MZINB model to evaluate a school-based fluoride mouthrinse program's effect on dental caries.

Main Results:

  • The proposed MZINB model provides straightforward inference for overall exposure effects.
  • Simulation studies demonstrated the finite sample performance of MZINB against alternative regression models.
  • The MZINB model was successfully applied to analyze dental caries data from a school-based fluoride program.

Conclusions:

  • The MZINB model is a valuable tool for analyzing overdispersed count data with excess zeros, particularly for estimating overall population exposure effects.
  • This model facilitates more direct and interpretable inference in public health and epidemiological studies.
  • The MZINB approach offers advantages over traditional ZINB models for population-level effect estimation.