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Novel metrics for growth model selection.

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This study introduces a new framework for comparing statistical models of childhood growth, finding functional models superior to linear mixed effects models for predictive accuracy in growth data analysis.

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Area of Science:

  • Biostatistics
  • Developmental Biology
  • Child Health

Background:

  • Statistical modeling of childhood growth data presents challenges due to a wide array of potential models.
  • A lack of comprehensive frameworks hinders the comparison of non-nested models and assessment of their performance.
  • This study addresses the need for robust methods to evaluate and compare different statistical approaches to growth modeling.

Purpose of the Study:

  • To propose a novel framework for comparing non-nested statistical models used in childhood growth data analysis.
  • To introduce new metrics for assessing predictive accuracy based on modified mean squared error (MSE) criteria.
  • To quantitatively compare the performance of linear mixed effects models and functional regression models.

Main Methods:

  • Developed three novel predictive accuracy metrics: normalized, age-adjusted, and weighted mean squared error (MSE).
  • Compared linear mixed effects models against functional regression models using these metrics.
  • Assessed prediction accuracy across different growth phases (early, late, in-range, new individuals) using training and testing datasets from 215 Peruvian children (birth to 2 years).

Main Results:

  • Functional regression models consistently outperformed linear mixed effects models in all tested scenarios.
  • Functional concurrent regression (FCR) and functional principal component analysis models demonstrated approximately 6% lower prediction errors compared to linear mixed effects models.
  • Weighted subject-specific MSEs by infant growth rates revealed FCR as the top-performing model across all scenarios.

Conclusions:

  • The proposed framework enables quantitative comparison of non-nested statistical models for childhood growth.
  • Novel MSE-based metrics allow for weighting subgroups of interest, facilitating the selection of the best-performing model.
  • This approach aids researchers in choosing the most appropriate growth model for specific applications and research questions.