Associating Growth in Infancy and Cognitive Performance in Early Childhood: A functional data analysis approach

TL;DR

This study uses functional data analysis to explore how infancy growth trajectories relate to early childhood IQ, revealing associations and dynamic patterns without relying heavily on prior population models.

Contribution

It introduces a semi-parametric functional response model to assess growth-IQ associations and analyze growth measurement correlations over time.

Findings

Identified specific infancy growth patterns linked to higher IQ scores.

Demonstrated the effectiveness of functional data analysis in growth and cognitive studies.

Revealed time-varying dynamics in growth measurements and their relation to IQ.

Abstract

Physical growth traits can be naturally represented by continuous functions. In a large dataset of infancy growth patterns, we develop a practical approach to infer statistical associations between growth-trajectories and IQ performance in early childhood. The main objective of this study is to show how to assess physical growth curves and detect if particular infancy growth patterns are associated with differences in IQ (Full-scale WASI scores) in later ages using a semi-parametric functional response model. Additionally, we investigate the association between different growth measurements in terms of their cross-correlation with each other, their correlation with later IQ, as well as their time-varying dynamics. This analysis framework can easily incorporate or learn population information in a non-parametric way, rendering the existence of prior population charts partially redundant.