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Related Experiment Video

Updated: May 23, 2026

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

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

Published on: April 28, 2022

Analyzing repeated measures semi-continuous data, with application to an alcohol dependence study.

Lei Liu¹, Robert L Strawderman², Bankole A Johnson³

¹Department of Preventive Medicine, Northwestern University, Chicago, USA lei.liu@northwestern.edu.

Statistical Methods in Medical Research

|April 5, 2012

Summary

This study enhances statistical models for semi-continuous data, improving analysis of repeated measures common in health research. The generalized gamma distribution offered the best fit for daily drinking records, outperforming standard log-normal models.

Keywords:

Hierarchical model generalized linear mixed model longitudinal data analysis mixed model model comparison non-nested model

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Area of Science:

Statistics
Biostatistics
Econometrics

Background:

Repeated measures of semi-continuous data present analytical challenges due to excess zeros and skewed positive values.
Traditional two-part random effects models assume a log-normal distribution for positive values, which may not always be appropriate.
Existing models require flexible distributional assumptions to better capture data characteristics.

Purpose of the Study:

To extend two-part random effects models for semi-continuous data.
To evaluate alternative distributions for the positive component: generalized gamma, log-skew-normal, and Box-Cox transformed normal.
To investigate the impact of heteroscedasticity on model performance.

Main Methods:

Application of three extended two-part random effects models to semi-continuous data.
Utilized maximum likelihood estimation implemented in SAS Proc NLMIXED.
Employed likelihood ratio tests for non-nested hypotheses to compare model fit.

Main Results:

All three extended models demonstrated a significantly better fit than the standard log-normal model.
Strong evidence of heteroscedasticity was detected in the data.
The generalized gamma distribution provided the best overall fit, although pairwise comparisons showed no statistically significant differences.

Conclusions:

Extended two-part random effects models offer improved flexibility for analyzing semi-continuous data.
The generalized gamma distribution is a promising alternative for modeling positive values in such data.
Accounting for heteroscedasticity is crucial when analyzing repeated measures of semi-continuous outcomes.