Related Experiment Video
Updated: May 15, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
Modeling physical growth using mixed effects models
William Johnson1, Nagalla Balakrishna, Paula L Griffiths
1Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, 55454, USA. wojohnso@umn.edu
Abstract:
This article demonstrates the use of mixed effects models for characterizing individual and sample average growth curves based on serial anthropometric data. These models are advancement over conventional general linear regression because they effectively handle the hierarchical nature of serial growth data. Using body weight data on 70 infants in the Born in Bradford study, we demonstrate how a mixed effects model provides a better fit than a conventional regression model. Further, we demonstrate how mixed effects models can be used to explore the influence of environmental factors on the sample average growth curve. Analyzing data from 183 infant boys (aged 3-15 months) from rural South India, we show how maternal education shapes infant growth patterns as early as within the first 6 months of life. The presented analyses highlight the utility of mixed effects models for analyzing serial growth data because they allow researchers to simultaneously predict individual curves, estimate sample average curves, and investigate the effects of environmental exposure variables.
More Related Videos
08:03Midface Hypoplasia and Cranial Base Morphology in Syndromic Craniosynostosis: A Comparative Analysis Study Using a Predictive Regression Model
Published on: November 4, 2025
06:52Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
Published on: September 17, 2019
Related Concept Videos
Exponential Equations for Modeling Growth
Modeling with Differential Equations
Population Growth
Growth Models with Integration: Problem Solving
Mechanistic Models: Compartment Models in Individual and Population Analysis
Pharmacodynamic Models: Additive and Proportional Drug Effect Model