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Updated: Jan 14, 2026

Palatable Western-style Cafeteria Diet as a Reliable Method for Modeling Diet-induced Obesity in Rodents
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A latent class location-scale regression model with an application to calorie intake data.

Xingruo Zhang1, Juned Siddique2, Bonnie Spring3

  • 1Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA. xrzhang@uchicago.edu.

Journal of Behavioral Medicine
|January 13, 2026
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Summary

This study presents a new statistical model for analyzing longitudinal behavioral data. The latent class model accurately identifies hidden patterns in both average trends and individual variability, aiding subgroup discovery.

Keywords:
Eating behaviorsIntraindividual variabilityLongitudinal data analysisSubgrouping

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Area of Science:

  • Statistics
  • Behavioral Science
  • Biostatistics

Background:

  • Longitudinal data analysis often requires understanding both average trends (location) and individual variability (scale).
  • Identifying hidden subgroups within this data is crucial for accurate interpretation and intervention.
  • Existing methods may not fully capture complex patterns in both location and scale trajectories.

Purpose of the Study:

  • To introduce a novel location-scale regression model with latent classes for analyzing longitudinal behavioral data.
  • To develop a flexible statistical tool for identifying subgroups based on mean and variability trajectories.
  • To provide a practical method for understanding dietary behavior consistency in weight management.

Main Methods:

  • Developed a location-scale regression model incorporating latent classes in both location and scale components.
  • Employed a full Bayesian approach using Stan for parameter estimation.
  • Validated the model using simulation studies to assess precision, bias, and classification accuracy.

Main Results:

  • The latent class model provides more precise and informative results, particularly for the scale component, in data with hidden patterns.
  • Simulation studies demonstrate unbiased parameter estimates and high correct classification rates, even without inherent heterogeneity.
  • The model effectively subgroups subjects based on both mean and within-subject variability trajectories.

Conclusions:

  • The proposed latent class location-scale model is a practical and effective tool for analyzing complex longitudinal behavioral data.
  • It enables researchers to identify meaningful subgroups based on nuanced patterns in data trends and variability.
  • Application to calorie intake data highlights its utility in understanding dietary consistency for personalized weight management interventions.