Dartmouth Psychiatric Research Center, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA.
J Subst Abuse Treat. 2013 Jul;45(1):99-108. doi: 10.1016/j.jsat.2013.01.005. Epub 2013 Feb 28.
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.
在物质滥用和成瘾研究中，偏态和大量零值的计数数据很常见。零调整模型，特别是零膨胀模型，在分析这类数据时变得越来越流行。本文通过分析纵向结果：新罕布什尔州双重障碍研究中戒烟尝试的次数，回顾和比较了五种常用的混合效应泊松家族模型，用于分析高比例零值的计数数据。我们的研究结果表明，大量零值的计数数据不一定需要零膨胀或其他零调整模型。对于罕见事件计数或均值较小的计数数据，简单的模型，如负二项式模型，可能提供更好的拟合。
