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Biostatistics of generalized estimating equations in developmental medicine and child neurology

Camille Eugénie Dieu1, Giovanni Briganti1

  • 1Department of Computational Medicine and Neuropsychiatry, Faculty of Medicine, Pharmacy and Biomedical Sciences, University of Mons, Mons, Belgium.

Developmental Medicine and Child Neurology
|November 8, 2025
PubMed

Insights

Generalized estimating equations (GEEs) offer a robust method for analyzing longitudinal data in paediatric research. This study demonstrates GEEs using a postnatal depression trial, confirming oestrogen

Area of Science:

  • Developmental Medicine
  • Paediatric Neurology
  • Biostatistics

Background:

  • Longitudinal data analysis in paediatrics presents unique challenges due to correlated outcomes.
  • Generalized estimating equations (GEEs) are a powerful tool for such data but require clear guidance for non-statistical researchers.
  • Existing methods may not fully address the complexities of paediatric longitudinal studies.

Purpose of the Study:

  • To provide a practical guide for using GEEs in paediatric research.
  • To illustrate GEE application with a reproducible workflow for continuous and binary outcomes.
  • To compare different correlation structures and their impact on analysis.

Main Methods:

  • Review of core GEE concepts tailored for paediatric applications.
  • Application of GEEs to a randomized trial of oestrogen versus placebo for postnatal depression.
  • Comparison of exchangeable and autoregressive working correlation structures.
  • Use of R code for a reproducible workflow.

Main Results:

  • GEEs provided stable marginal estimates across correlation structures when the mean model was correctly specified.
  • Oestrogen was significantly associated with lower odds of postnatal depression compared to placebo.
  • Statistical choices primarily influenced efficiency and standard errors, not effect sizes.

Conclusions:

  • GEEs offer a robust and interpretable framework for analyzing correlated longitudinal data in paediatric research.
  • Transparent reporting of working correlations and understanding marginal effects are crucial for practical application.
  • This guide and reproducible example empower clinicians and researchers to effectively utilize GEEs.

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