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Statistical models for longitudinal zero‐inflated count data with applications to the substance abuse field
Author(s) -
Buu Anne,
Li Runze,
Tan Xianming,
Zucker Robert A.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5510
Subject(s) - count data , poisson distribution , covariate , longitudinal field , zero inflated model , computer science , poisson regression , statistics , zero (linguistics) , econometrics , longitudinal data , field (mathematics) , statistical model , random effects model , mathematics , data mining , population , medicine , linguistics , physics , philosophy , meta analysis , quantum mechanics , magnetic field , pure mathematics , environmental health
This study fills in the current knowledge gaps in statistical analysis of longitudinal zero‐inflated count data by providing a comprehensive review and comparison of the hurdle and zero‐inflated Poisson models in terms of the conceptual framework, computational advantage, and performance under different real data situations. The design of simulations represents the special features of a well‐known longitudinal study of alcoholism so that the results can be generalizable to the substance abuse field. When the hurdle model is more natural under the conceptual framework of the data, the zero‐inflated Poisson model tends to produce inaccurate estimates. Model performance improves with larger sample sizes, lower proportions of missing data, and lower correlations between covariates. The simulation also shows that the computational strength of the hurdle model disappears when random effects are included. Copyright © 2012 John Wiley & Sons, Ltd.