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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A doubly-inflated Poisson regression for correlated count data.

Erfan Ghasemi1, Alireza Akbarzadeh Baghban2, Farid Zayeri3

  • 1Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

This study introduces a new statistical model for analyzing count data with excess zeros and other inflated values, specifically designed for correlated health data. The proposed method, a Doubly-Inflated Poisson model with random effects, shows strong performance in simulations and a dental study.

Keywords:
Count dataPoisson regressioncorrelated datadoubly-inflatedzero-inflated

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Health Data Analysis

Background:

  • Count data analysis is crucial in many research fields, often exhibiting excess zeros (zero-inflated data).
  • Standard Poisson models can fail with over-dispersion and do not handle inflation at points other than zero.
  • Existing doubly-inflated models are limited to cross-sectional data, neglecting correlations common in health studies.

Purpose of the Study:

  • To introduce a novel statistical model: Doubly-Inflated Poisson models with random effects.
  • To address correlated count data with inflation at zero and other points.
  • To evaluate the proposed model's performance through simulations and a real-world dental study.

Main Methods:

  • Development of a Doubly-Inflated Poisson model incorporating random effects.
  • Simulation studies to assess model performance under various scenarios.
  • Application of the model to analyze correlated count data from a dental study.

Main Results:

  • The proposed Doubly-Inflated Poisson model with random effects demonstrates effective handling of correlated, inflated count data.
  • Simulation results confirm the model's superior performance compared to existing methods.
  • Successful application to a dental study highlights its practical utility in health research.

Conclusions:

  • The new model provides a robust framework for analyzing complex count data prevalent in health and medical research.
  • It effectively accounts for both zero-inflation and inflation at other points, along with data correlation.
  • This method offers a valuable tool for researchers dealing with correlated, doubly-inflated count data.