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Murine Drinking Models in the Development of Pharmacotherapies for Alcoholism: Drinking in the Dark and Two-bottle Choice
07:31

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Published on: January 7, 2019

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

David C Atkins1, Scott A Baldwin, Cheng Zheng

  • 1Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98105, USA. datkins@uw.edu

Psychology of Addictive Behaviors : Journal of the Society of Psychologists in Addictive Behaviors
|August 22, 2012
PubMed
Summary
This summary is machine-generated.

This tutorial introduces mixed models for analyzing longitudinal substance use data. It addresses challenges with skewed count outcomes and repeated measures, offering practical solutions for researchers studying addictive behaviors.

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Area of Science:

  • Behavioral Science
  • Biostatistics
  • Psychology

Background:

  • Studying addictive behaviors requires analyzing how substance use changes over time, whether due to interventions or naturalistic progression.
  • Longitudinal substance use data often feature skewed count outcomes with excess zeros, posing analytical challenges for traditional methods.
  • Repeated assessments in longitudinal studies necessitate specialized statistical approaches to accurately model temporal trends.

Purpose of the Study:

  • To provide a tutorial on advanced statistical methods for analyzing longitudinal substance use data.
  • To demonstrate the application of Poisson, zero-inflated, and hurdle mixed models for count outcomes with repeated measures.
  • To offer practical guidance and code examples for researchers in the field of addictive behaviors.

Main Methods:

  • Focuses on hierarchical or multilevel modeling techniques, specifically Poisson, zero-inflated, and hurdle mixed models.
  • Utilizes two distinct example datasets: one on drinking consequences post-intervention and another on daily drinking patterns.
  • Provides accompanying code for R, SAS, Mplus, Stata, and SPSS to facilitate model implementation.

Main Results:

  • The tutorial illustrates how mixed models can effectively handle the complexities of longitudinal substance use data, including skewed distributions and excess zeros.
  • Demonstrates the practical application of the discussed models using real-world examples of drinking behavior.
  • Highlights the availability of datasets and analysis code for replication and further research.

Conclusions:

  • Mixed models, including Poisson, zero-inflated, and hurdle variants, are powerful tools for analyzing longitudinal substance use data with count outcomes.
  • These methods offer robust solutions for researchers investigating changes in addictive behaviors over time.
  • The provided resources aim to enhance the analytical capabilities of researchers studying substance use and addiction.