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A marginalized zero-inflated Poisson regression model with overall exposure effects.

D Leann Long1, John S Preisser, Amy H Herring

  • 1Department of Biostatistics, West Virginia University, Morgantown, WV, U.S.A.

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|September 16, 2014
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Summary

This study introduces a marginalized zero-inflated Poisson (ZIP) model for public health research. This new approach improves the analysis of count data with excess zeros, offering better insights into exposure effects in the overall population.

Keywords:
incidencemarginalized modelsunprotected intercoursezero inflation

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Area of Science:

  • Biostatistics
  • Public Health
  • Epidemiology

Background:

  • Zero-inflated Poisson (ZIP) regression is common in public health for count data with excess zeros.
  • ZIP model coefficients offer latent class interpretations but are suboptimal for marginal mean inference.
  • Quantifying overall exposure effects in mixture populations using standard ZIP models is challenging.

Purpose of the Study:

  • To develop a marginalized zero-inflated Poisson (ZIP) model for direct population mean count modeling.
  • To enable straightforward inference for overall exposure effects and robust variance estimation.
  • To provide a more suitable method for analyzing count data with excess zeros in public health.

Main Methods:

  • Development of a marginalized zero-inflated Poisson (ZIP) model for independent responses.
  • Direct modeling of the population mean count.
  • Assessment of maximum likelihood estimation performance through simulation studies.
  • Comparison with existing methods for estimating overall exposure effects.

Main Results:

  • The marginalized ZIP model allows direct inference on population mean counts.
  • Empirical robust variance estimation is provided for overall log-incidence density ratios.
  • Simulation studies demonstrated the performance of the proposed marginalized ZIP model.

Conclusions:

  • The marginalized ZIP model offers improved inference for overall exposure effects compared to standard ZIP models.
  • This approach is applicable to public health studies involving count data with excess zeros.
  • The model was successfully applied to analyze the impact of a safer sex counseling intervention on unprotected sexual act counts.