title: Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: an example of smoking cessation.
abstract: Count data with skewness and many zeros are common in substance abuse and addiction research. Zero-adjusting models, especially zero-inflated models, have become increasingly popular in analyzing this type of data. This paper reviews and compares five mixed-effects Poisson family models commonly used to analyze count data with a high proportion of zeros by analyzing a longitudinal outcome: number of smoking quit attempts from the New Hampshire Dual Disorders Study. The findings of our study indicated that count data with many zeros do not necessarily require zero-inflated or other zero-adjusting models. For rare event counts or count data with small means, a simpler model such as the negative binomial model may provide a better fit.
authors: Dartmouth Psychiatric Research Center, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA. Haiyi.Xie@Dartmouth.Edu
authors: Haiyi
authors: H
authors: Xie
authors: Jill
authors: J
authors: Tao
authors: Gregory J
authors: GJ
authors: McHugo
authors: Robert E
authors: RE
authors: Drake
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D009513:New Hampshire; D016012:Poisson Distribution; D012107:Research Design; D016540:Smoking Cessation / Q000706:statistics & numerical data*