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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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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

American Journal of Physical Anthropology
|January 4, 2013
PubMed
Summary
This summary is machine-generated.

Mixed effects models offer advanced analysis for serial anthropometric data, outperforming traditional regression. These models effectively characterize individual and average growth curves, revealing environmental influences on infant development.

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Area of Science:

  • Biostatistics
  • Human Growth and Development
  • Epidemiology

Background:

  • Serial anthropometric data, crucial for understanding growth, presents analytical challenges due to its hierarchical nature.
  • Conventional general linear regression models often fail to adequately capture the complexities of longitudinal growth patterns.

Purpose of the Study:

  • To demonstrate the utility of mixed effects models for analyzing serial anthropometric data.
  • To compare the performance of mixed effects models against conventional regression for growth curve characterization.
  • To explore the influence of environmental factors on infant growth trajectories using mixed effects models.

Main Methods:

  • Application of mixed effects models to serial body weight data from infants.
  • Comparison of model fit between mixed effects models and conventional regression.
  • Analysis of infant growth patterns in relation to maternal education using mixed effects models.

Main Results:

  • Mixed effects models provide a superior fit for serial growth data compared to conventional regression.
  • Maternal education significantly influences infant growth patterns within the first six months of life.
  • Mixed effects models effectively predict individual growth curves and estimate sample average curves.

Conclusions:

  • Mixed effects models are a powerful tool for analyzing serial growth data, offering simultaneous prediction of individual and average growth curves.
  • These models facilitate the investigation of environmental influences on growth trajectories.
  • The findings underscore the importance of considering maternal education in early infant development.