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Updated: Jun 12, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Analysis of longitudinal zero-inflated count data using overall marginalized hurdle models.

Jiyun Lee1, Eun Jin Jang2, Keunbaik Lee1

  • 1Department of Statistics, Sungkyunkwan University, Seoul, Korea.

Statistical Methods in Medical Research
|June 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces new models for analyzing zero-inflated count data, improving the assessment of overall covariate effects. The developed overall marginalized hurdle random effects models (OMHREMs) offer better insights into population-average effects.

Keywords:
Hurdle modelheterogeneous random effectsoverdispersed datazero inflation

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Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Longitudinal zero-inflated count data are common in medicine and social sciences.
  • Traditional hurdle models struggle to directly estimate marginal means and overall covariate effects.

Purpose of the Study:

  • Introduce overall marginalized hurdle random effects models (OMHREMs) for longitudinal zero-inflated count data.
  • Enable direct modeling of marginal means and assessment of population-average covariate effects.

Main Methods:

  • Developed OMHREMs extending traditional hurdle models.
  • Incorporated random effects to address data heterogeneity.
  • Evaluated model performance using simulation studies.

Main Results:

  • OMHREMs directly model the marginal mean for zero-inflated data.
  • The models provide population-average covariate effects, such as odds ratios.
  • Simulations demonstrated the performance of OMHREMs.

Conclusions:

  • OMHREMs offer an effective approach for analyzing longitudinal zero-inflated count data.
  • The models improve understanding of how covariates influence overall means.
  • Applied the method to systemic lupus erythematosus data for comparison.