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Capturing infant and child growth dynamics with P-splines mixed effects models
María Alejandra Hernandez1,2, Zheyuan Li3, Tim J Cole4
1MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
International Journal of Obesity (2005)
|June 12, 2026
Summary
Penalised B-splines (P-splines) offer a flexible method for analyzing early life growth trajectories and identifying key growth features. This study guides their application in epidemiology, providing accessible tools for growth modeling.
Area of Science:
- Epidemiology
- Developmental Biology
- Biostatistics
Background:
- Understanding early life growth is crucial for the developmental origins of obesity.
- Penalised B-splines (P-splines) offer a flexible alternative to standard B-splines for modeling complex growth patterns, mitigating overfitting risks.
- P-splines are underutilized in epidemiology due to a lack of guidance and accessible software.
Purpose of the Study:
- To provide a comprehensive guide for applying P-spline linear mixed effects models to analyze early life growth trajectories.
- To extract key growth features from longitudinal data.
- To demonstrate the utility of P-splines in epidemiological research.
Main Methods:
- P-spline linear mixed effects models were developed using sparse matrices for efficient estimation.
- The models were applied to repeated height, weight, and body mass index (BMI) measurements up to age 10 years in a Southeast Asian birth cohort (n=1014).
- Key growth parameters such as peak growth velocity, and timing and magnitude of peak and rebound BMI were estimated.
Main Results:
- Boys exhibited higher infant peak height and weight velocity compared to girls.
- Infancy peak BMI, childhood rebound BMI, and ages at peak and rebound BMI were comparable between sexes.
- Maternal height, maternal early-pregnancy weight, and birth weight were associated with specific growth parameters, including peak growth velocity and rebound BMI timing and magnitude.
Conclusions:
- P-splines simplify knot selection, presenting a valuable method for robust growth modeling.
- The study provides software, code, and datasets to encourage the adoption of P-splines in epidemiological research.
- This approach facilitates a deeper understanding of early life growth dynamics and their determinants.
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