A GEE-type approach to untangle structural and random zeros in predictors. (December 2019)

• Record Type: Journal Article
• Authors: Ye, Peng; Tang, Wan; He, Jiang; He, Hua
• Is Part Of: Statistical methods in medical research. Volume 28:Number 12(2019)
• Page Start: 3683
• Page End: 3696
• Publication Date: 2019-12
• Subjects: Generalized estimating equations -- mixture model -- structural zeros -- zero-inflated explanatory variables -- zero-inflated Poisson; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218812228
• Languages: English
• ISSNs: 0962-2802

Abstract

Count outcomes with excessive zeros are common in behavioral and social studies, and zero-inflated count models such as zero-inflated Poisson (ZIP) and zero-inflated Negative Binomial (ZINB) can be applied when such zero-inflated count data are used as response variable. However, when the zero-inflated count data are used as predictors, ignoring the difference of structural and random zeros can result in biased estimates. In this paper, a generalized estimating equation (GEE)-type mixture model is proposed to jointly model the response of interest and the zero-inflated count predictors. Simulation studies show that the proposed method performs well for practical settings and is more robust for model misspecification than the likelihood-based approach. A case study is also provided for illustration.