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Updated: Jan 22, 2026

In Situ Visualization of Axon Growth and Growth Cone Dynamics in Acute Ex Vivo Embryonic Brain Slice Cultures
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Commentary: Methods for calculating growth trajectories and constructing growth centiles.

T J Cole1

  • 1UCL Great Ormond Street Institute of Child Health, London, UK.

Statistics in Medicine
|July 13, 2019
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Statistical models for analyzing early life growth data are reviewed. While some models excel at detecting short-term growth changes, others provide better long-term predictions for health outcomes.

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Area of Science:

  • Pediatrics
  • Biostatistics
  • Growth Monitoring

Background:

  • Accurate statistical analysis of growth data is crucial for understanding child development and health.
  • Existing growth trajectory models and centile construction methods require critical evaluation for optimal application.

Purpose of the Study:

  • To review and critically assess statistical methods for analyzing early life growth data.
  • To evaluate growth trajectory models and centile construction techniques using real-world datasets.

Main Methods:

  • Analysis of four growth trajectory models (Laird-Ware, SITAR, brokenstick, FACE) applied to length data.
  • Review of centile construction methods with worked examples using birthweight and fetal head circumference data from the INTERGROWTH-21st project.
  • Evaluation of the GAMLSS software for centile analysis.

Main Results:

  • Brokenstick and FACE models showed better fit for length data compared to Laird-Ware and SITAR.
  • Concerns raised regarding the timescale of growth faltering detection and its relevance to long-term health outcomes.
  • Poor reporting quality in fetal centile studies identified; GAMLSS software demonstrated power and potential for misuse.
  • Longitudinal fetal head circumference centiles were found to be biased.

Conclusions:

  • The choice of growth model depends on whether short-term fluctuations or long-term growth summaries are prioritized.
  • Recommendations for good practice in fetal centile studies are provided to improve data quality and reporting.
  • Further research is needed to fully understand the predictive value of different growth models for later health outcomes.