Overall marginalized models for longitudinal zero-inflated count data

TL;DR

This paper introduces marginalized zero-inflated Poisson and negative binomial models with random effects for longitudinal zero-inflated count data, providing clearer covariate effect interpretation and handling overdispersion.

Contribution

The paper develops novel marginalized zero-inflated models with random effects, enhancing interpretability and flexibility for longitudinal zero-inflated count data analysis.

Findings

Models effectively capture covariate effects on zero-inflated data.

Simulation studies demonstrate accurate inference under various random effects.

Application to lupus data shows practical utility of the models.

Abstract

To analyze longitudinal zero-inflated count data, we extend existing models by introducing marginalized zero-inflated Poisson (MZIP) models with random effects, which explicitly capture the marginal effect of covariates and address limitations of previous methods. These models provide a clearer interpretation of the overall mean effect of covariates on zero-inflated count data. To further accommodate overdispersion, we develop marginalized zero-inflated negative binomial (MZINB) models. Both models incorporate subject-specific heterogeneity through a flexible random effects covariance structure. Simulation studies are conducted to evaluate the performance of the MZIP and MZINB models, comparing their inference under both homogeneous and heterogeneous random effects. Finally, we illustrate the applicability of the proposed models through an analysis of systemic lupus erythematosus data.