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Modelling bivariate count series with excess zeros.

Andy H Lee1, Kui Wang, Kelvin K W Yau

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Curtin University of Technology, GPO Box U 1987, Perth, WA 6845, Australia. andy.lee@curtin.edu.au

Mathematical Biosciences
|July 19, 2005
PubMed
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This study introduces a new statistical model for analyzing paired count data with many zeros, common in biosciences. The model accurately assesses interventions by accounting for excess zeros and time-dependent patterns in injury data.

Area of Science:

  • Biostatistics
  • Occupational Health
  • Epidemiology

Background:

  • Bivariate count time series data frequently exhibit excess zeros, deviating from standard Poisson distributions.
  • Ignoring excess zeros in analyses can lead to biased parameter estimates and erroneous conclusions in bioscience research.

Purpose of the Study:

  • To present a novel class of bivariate zero-inflated Poisson autoregression models.
  • To address both excess zeros and serial dependency in bivariate count data.
  • To evaluate the effectiveness of interventions in occupational health studies.

Main Methods:

  • Development of bivariate zero-inflated Poisson autoregression models.
  • Incorporation of an autoregressive correlation structure for serial dependency.

Related Experiment Videos

  • Parameter estimation using an Expectation-Maximization (EM) algorithm and residual maximum likelihood (REML).

    • Main Results:

    • The proposed model effectively accommodates excess zeros and temporal correlations in bivariate count data.
    • Application to an occupational health study demonstrated the model's utility in analyzing injury counts.
    • The method successfully evaluated a participatory ergonomics intervention's impact on injury incidence.

    Conclusions:

    • The bivariate zero-inflated Poisson autoregression model provides a robust framework for analyzing complex count data in biosciences.
    • This approach enhances the accuracy of intervention effectiveness evaluation in occupational health.
    • The model facilitates a simultaneous assessment of overall injury reduction and specific injury rate changes.