A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data
A Tutorial on Count Regression and Zero-Altered Count Models for Longitudinal Substance Use Data
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DOI：
10.1037/a0029508
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发表时间：
2013-03-01
影响因子：
3.4
通讯作者：
Neighbors, Clayton
中科院分区：
文献类型：
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作者：
Atkins, David C.;Baldwin, Scott A.;Neighbors, Clayton
成瘾行为研究中的关键研究问题涉及这些行为如何随着时间的推移而变化：无论是干预的结果还是在自然环境中。计数结果的组合通常与许多零强烈偏斜（例如，使用天数、总饮酒次数、饮酒后果次数）进行重复评估（例如，干预后的纵向随访或每日日记数据）对数据分析提出了挑战。本文提供了一个关于分析纵向物质使用数据的方法的教程，重点是泊松，零膨胀和栅栏混合模型，这些模型是分层或多级模型的类型。在整个过程中使用了两个示例数据集，分别侧重于干预后的饮酒相关后果和过去30天的每日饮酒。这两个数据集以及R，SAS，Mplus，Stata和SPSS代码显示如何拟合模型可在补充网站上获得。
Critical research questions in the study of addictive behaviors concern how these behaviors change over time: either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website.