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Mathematical modelling of human growth: A comparative study.

Shumei Guo1,2, Roger M Siervogel2,3, Alex F Roche2,3

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Kernel regression effectively models human stature growth, capturing midgrowth and pubertal spurts better than some parametric models. This nonparametric method offers flexibility for individual growth trajectory analysis.

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Area of Science:

  • Human Growth and Development
  • Biostatistics
  • Nonparametric Statistics

Background:

  • Parametric models often impose restrictive assumptions on growth curve shapes.
  • Kernel regression offers a flexible nonparametric alternative for analyzing serial data.
  • Understanding human stature growth requires accurate modeling of developmental spurts.

Purpose of the Study:

  • To compare the performance of kernel regression against two parametric models for fitting human stature growth data.
  • To evaluate the ability of kernel regression and parametric models to quantify growth spurt timing, magnitude, and duration.
  • To assess the utility of kernel regression in capturing the midgrowth spurt.

Main Methods:

  • Serial stature measurements from 227 participants in the Fels Longitudinal Study were analyzed.
  • Two parametric models (including one that does not account for the midgrowth spurt) and nonparametric kernel regression were fitted.
  • Growth parameters (timing, rate, duration of spurts) were derived and compared across models.

Main Results:

  • Kernel regression provided good fits, comparable to the triple logistic model, and accurately estimated midgrowth spurt parameters.
  • The Preece-Baines model, which does not account for the midgrowth spurt, showed discrepancies in pubertal spurt timing and velocity.
  • Kernel regression indicated an earlier onset and faster velocity increase for the midgrowth spurt compared to parametric models.

Conclusions:

  • Kernel regression is a valuable tool for modeling individual human stature growth, particularly for capturing complex growth patterns like the midgrowth spurt.
  • Nonparametric kernel regression demonstrates superior flexibility and accuracy over certain parametric models in human growth analysis.
  • The findings support the use of kernel regression for detailed quantification of human growth dynamics.