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Analyzing longitudinal clustered count data with zero inflation: Marginal modeling using the Conway-Maxwell-Poisson

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This study introduces a new statistical model for analyzing complex biological count data, especially useful for dental research with excessive zeros and varying dispersion. The proposed method offers superior performance over existing models.

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Dental Research

Background:

  • Biological and medical research frequently involves clustered count data collected over time.
  • Such data often exhibit excess zeros and wide dispersion, posing analytical challenges.
  • The Iowa Fluoride Study (IFS), investigating caries development in children, presents these complex data features.

Purpose of the Study:

  • To propose a novel longitudinal statistical method for analyzing complex count data with excessive zeros and dispersion.
  • To analyze the multiyear Iowa Fluoride Study (IFS) dataset using the proposed method.
  • To compare the performance of the new method against existing models.

Main Methods:

  • Development of a generalized estimating equations-based marginal regression model.
  • Utilizing a zero-inflated Conway-Maxwell-Poisson (CMP) distribution for flexibility in dispersion.
  • Employing a modified expectation-solution algorithm for parameter estimation in clustered and temporal data.

Main Results:

  • The zero-inflated CMP model was successfully fitted to the IFS dataset, yielding clinically relevant conclusions.
  • The proposed model demonstrated superior performance compared to zero-inflated Poisson and negative binomial models.
  • Simulation studies confirmed the accuracy of point estimators, variance estimators, and confidence intervals.

Conclusions:

  • The novel longitudinal zero-inflated CMP model effectively analyzes complex count data with excess zeros and time-varying dispersion.
  • This approach provides a robust tool for analyzing longitudinal dental studies like the IFS.
  • The method offers improved performance over traditional models for such data structures.