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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Modelling heterogeneity in clustered count data with extra zeros using compound Poisson random effect.

Renjun Ma¹, M Tariqul Hasan, Gary Sneddon

¹Department of Mathematics and Statistics, University of New Brunswick, Fredericton, NB, Canada E3B 5A3. renjun@unb.ca

Statistics in Medicine

|May 23, 2009

Summary

This study introduces a new zero-inflated Poisson model for clustered health data, effectively handling excessive zeros and complex data structures in health-care utilization. The proposed method improves modeling accuracy for hierarchical count data.

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Area of Science:

Biostatistics
Health Services Research
Statistical Modeling

Background:

Traditional Poisson mixed models struggle with excessive zeros common in clustered health-care utilization data.
Existing methods often fail to adequately capture the hierarchical structure and heterogeneity present in such data.

Purpose of the Study:

To develop a flexible three-level random effects zero-inflated Poisson model for clustered health-care utilization data.
To accommodate both zero and positive counts within a hierarchical framework using a compound Poisson distribution for random effects.

Main Methods:

Proposed a three-level random effects zero-inflated Poisson model.
Utilized a compound Poisson distribution for subject-level random effects to handle zero and positive components.
Developed a quasi-likelihood approach for model parameter estimation.

Main Results:

The developed model effectively handles excessive zeros and the hierarchical structure of clustered health-care data.
Variance components decomposition clearly reflects the data's hierarchical organization.
Illustrative analysis of health-care utilization data and simulation studies demonstrate the method's performance.

Conclusions:

The proposed three-level random effects zero-inflated Poisson model offers a robust approach for analyzing clustered health-care utilization data.
The compound Poisson distribution provides a suitable mechanism for modeling complex zero-inflated count data.
The quasi-likelihood estimation method is effective for parameter estimation in this complex hierarchical model.