title: Statistical models for longitudinal zero-inflated count data with applications to the substance abuse field.
abstract: 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.
authors: Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA. buu@umich.edu
authors: Anne
authors: A
authors: Buu
authors: Runze
authors: R
authors: Li
authors: Xianming
authors: X
authors: Tan
authors: Robert A
authors: RA
authors: Zucker
mesh_terms: D000293:Adolescent; D000437:Alcoholism / Q000453:epidemiology / Q000523:psychology*; D002648:Child; D005220:Fathers / Q000523:psychology; D006801:Humans; D008137:Longitudinal Studies / Q000706:statistics & numerical data*; D008297:Male; D008824:Michigan / Q000453:epidemiology; D015233:Models, Statistical; D016012:Poisson Distribution; D012107:Research Design; D018570:Risk Assessment; D012307:Risk Factors