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- W2131624435 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." @default.
- W2131624435 created "2016-06-24" @default.
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- W2131624435 date "2013-07-01" @default.
- W2131624435 modified "2023-10-16" @default.
- W2131624435 title "Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: An example of smoking cessation" @default.
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- W2131624435 doi "https://doi.org/10.1016/j.jsat.2013.01.005" @default.
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