Discussion paper. Conditional growth charts

TL;DR

This paper introduces a flexible semiparametric quantile regression model for personalized growth charts, capable of handling irregular data, covariates, and outliers, with new inference and assessment tools.

Contribution

It develops a novel global semiparametric model for conditional growth charts that improves customization and robustness over traditional methods.

Findings

Model effectively estimates conditional quantiles without strict distributional assumptions.

Proposed tests and tools enhance inference and assessment for longitudinal growth data.

Model shows potential for practical application in personalized growth chart analysis.

Abstract

Growth charts are often more informative when they are customized per subject, taking into account prior measurements and possibly other covariates of the subject. We study a global semiparametric quantile regression model that has the ability to estimate conditional quantiles without the usual distributional assumptions. The model can be estimated from longitudinal reference data with irregular measurement times and with some level of robustness against outliers, and it is also flexible for including covariate information. We propose a rank score test for large sample inference on covariates, and develop a new model assessment tool for longitudinal growth data. Our research indicates that the global model has the potential to be a very useful tool in conditional growth chart analysis.