A tutorial on count regression and zero-altered count models for longitudinal substance use data

[Correction Notice: An Erratum for this article was reported in Vol 27(2) of Psychology of Addictive Behaviors (see record 2013-21666-002). The URL for the supplemental material was incorrect throughout the text due to a production error. Supplemental material for this article is available at: http://dx.doi.org/10.1037/a0029508.supp. The online version of this article has been corrected.] 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.