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Updated: Mar 3, 2026
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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Multilevel growth curve models that incorporate a random coefficient model for the level 1 variance function.
Harvey Goldstein1, George Leckie1, Christopher Charlton1
11 Centre for Multilevel Modelling, University of Bristol, Bristol, UK.
Statistical Methods in Medical Research
|May 2, 2017
Summary
This study introduces a flexible Bayesian model for analyzing longitudinal growth data, accounting for individual-specific growth patterns and variability over time. The model effectively captures individual differences in growth events and characteristics.
Area of Science:
- Biostatistics
- Human Growth and Development
- Longitudinal Data Analysis
Background:
- Longitudinal growth data analysis often requires flexible models to capture individual variations.
- Existing models may not fully account for individual-specific timing of growth events or within-individual variability as a function of age.
Purpose of the Study:
- To develop and present a flexible Bayesian model for repeated measures longitudinal growth data.
- To incorporate individual-specific random effects for growth trends, timing of events, and within-individual variability.
- To apply the model to height data in boys and weight data in pregnant women.
Main Methods:
- A Bayesian statistical model was developed.
- The model includes random effects for the mean growth function, individual age-alignment, and within-individual variance.
- The model was applied to two distinct longitudinal datasets: boys' heights and pregnant women's weight.
Main Results:
- For boys' height, the model identified a mean age-alignment of 11.4 years, coinciding with pubertal growth onset.
- Within-individual height variance decreased from 0.24 cm² at age 9 to 0.07 cm² at age 16.
- Pregnancy weight changes were characterized by regression splines with significant woman-specific random effects for within-individual variation.
Conclusions:
- The proposed Bayesian model offers a flexible extension for analyzing longitudinal growth data.
- It effectively describes both within- and between-individual differences in growth patterns.
- The model enhances the characterization of growth trajectories and associated variability.
Keywords:
Avon Longitudinal Study of Parents and ChildrenHeteroscedasticitymultilevel modelrepeated measuresvariance modelMore Related Videos
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