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The Motivation for Alcohol Reward: Predictors of Progressive-Ratio Intravenous Alcohol Self-Administration in Humans

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A multi-level two-part random effects model, with application to an alcohol-dependence study.

Lei Liu¹, Jennie Z Ma, Bankole A Johnson

¹Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908-0717, USA. liulei@virginia.edu

Statistics in Medicine

|January 26, 2008

Summary

This summary is machine-generated.

Researchers developed a new multi-level two-part random effects model for analyzing semi-continuous longitudinal data. This statistical method addresses data with many zeros and positive values, improving analysis for complex health studies.

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Area of Science:

Biostatistics
Longitudinal Data Analysis
Statistical Modeling

Background:

Semi-continuous outcomes, common in health research, present unique statistical challenges.
Existing two-part random effects models are not well-suited for multi-level longitudinal data.
Analyzing data with a high proportion of zero values alongside continuous positive values requires specialized methods.

Purpose of the Study:

To propose a novel multi-level two-part random effects model.
To extend existing two-part models to accommodate hierarchical data structures.
To provide a robust statistical framework for analyzing complex longitudinal health data.

Main Methods:

Development of a multi-level two-part random effects model incorporating distinct random effects for different hierarchical levels.
Utilizing Gaussian quadrature technique for maximum likelihood estimation and inference.
Implementation in the freely available statistical software package 'aML'.

Main Results:

The proposed model effectively handles heterogeneity across different levels in multi-level data.
The Gaussian quadrature method provides a computationally feasible approach for estimation and inference.
The model was successfully applied to analyze daily drinking records in an alcohol-dependence treatment trial.

Conclusions:

The novel multi-level two-part random effects model offers a significant advancement for analyzing semi-continuous longitudinal data in hierarchical settings.
This statistical approach enhances the understanding of complex health behaviors and treatment outcomes.
The model's implementation in accessible software facilitates its application in diverse research areas.