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Modelling childhood growth using fractional polynomials and linear splines
Kate Tilling1, Corrie Macdonald-Wallis, Debbie A Lawlor
1School of Social and Community Medicine and MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Annals of Nutrition & Metabolism
|November 22, 2014
Summary
This study models childhood growth using multilevel fractional polynomials and linear splines, revealing growth patterns and social class disparities from birth to age 10.
Area of Science:
- Biostatistics
- Developmental Biology
- Epidemiology
Background:
- Growing interest in modeling life course growth and associated factors.
- Introduction of multilevel models for childhood growth analysis.
- Utilizing fractional polynomials for smooth functions and linear splines for distinct growth phases.
Purpose of the Study:
- To demonstrate and compare multilevel models for childhood growth.
- To analyze the relationship between parental social class and height from birth to 10 years.
- To identify optimal modeling approaches for growth trajectories.
Main Methods:
- Applied multilevel fractional polynomial and linear spline models.
- Analyzed height data from 5,588 girls in the Avon Longitudinal Study of Parents and Children (ALSPAC).
- Identified specific model parameters and knot points for growth phases.
Main Results:
- Both models indicated an initial rapid growth rate that decelerated over time.
- Observed a birth length disparity between manual and non-manual social classes, which changed until age 1, then increased.
- Fractional polynomial model identified specific age-related powers, while linear spline model used knot points at 3, 12, and 36 months.
Conclusions:
- Multilevel fractional polynomials provide a more realistic smooth growth function.
- Linear spline models offer enhanced interpretability for growth phases.
- Both methods effectively summarize individual growth trajectories and exposure relationships.
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