Additive quantile regression for clustered data with an application to children's physical activity

TL;DR

This paper introduces a new additive mixed model for quantile regression tailored for clustered data, demonstrated through an application to children's physical activity and validated via simulations.

Contribution

The paper proposes a novel additive mixed model for quantile regression specifically designed for clustered data, extending existing methods.

Findings

Effective modeling of children's physical activity data.

Superior performance in simulations compared to existing methods.

Applicable to large-scale accelerometer datasets.

Abstract

Additive models are flexible regression tools that handle linear as well as nonlinear terms. The latter are typically modelled via smoothing splines. Additive mixed models extend additive models to include random terms when the data are sampled according to cluster designs (e.g., longitudinal). These models find applications in the study of phenomena like growth, certain disease mechanisms and energy consumption in humans, when repeated measurements are available. In this paper, we propose a novel additive mixed model for quantile regression. Our methods are motivated by an application to physical activity based on a dataset with more than half million accelerometer measurements in children of the UK Millennium Cohort Study. In a simulation study, we assess the proposed methods against existing alternatives.