Analysis of longitudinal zero-inflated count data using overall marginalized hurdle models.

Longitudinal zero-inflated count data frequently arise in various fields such as medicine and social sciences. Standard hurdle models separate zero and positive counts, but fail to directly infer marginal means, limiting their ability to assess overall covariate effects. This paper introduces overall marginalized hurdle random effects models (OMHREMs) for zero-inflated count data, which extends the traditional hurdle model by directly modeling the marginal mean while considering random effects to account for heterogeneity. OMHREMs enable population-average effects of covariates like odds ratio, providing how covariates influence the overall mean in zero-inflated count data. Through simulation studies, we evaluate the performance of OMHREMs. Furthermore, we apply our approach to systemic lupus erythematosus data to compare its effectiveness against existing models.