A semiparametric regression model for paired longitudinal outcomes with application in childhood blood pressure development

TL;DR

This paper introduces a semiparametric joint modeling approach using bivariate thin plate splines to analyze the effects of height and weight on paired blood pressure measures in children, accounting for nonlinearities and correlations.

Contribution

It develops a novel joint modeling framework with flexible spline surfaces for paired longitudinal outcomes, addressing the confounding effects of height and weight in pediatric blood pressure analysis.

Findings

Identified nonlinear effects of height and weight on blood pressure.

Captured the correlation between systolic and diastolic blood pressure.

Demonstrated the method's application on real clinical data.

Abstract

This research examines the simultaneous influences of height and weight on longitudinally measured systolic and diastolic blood pressure in children. Previous studies have shown that both height and weight are positively associated with blood pressure. In children, however, the concurrent increases of height and weight have made it all but impossible to discern the effect of height from that of weight. To better understand these influences, we propose to examine the joint effect of height and weight on blood pressure. Bivariate thin plate spline surfaces are used to accommodate the potentially nonlinear effects as well as the interaction between height and weight. Moreover, we consider a joint model for paired blood pressure measures, that is, systolic and diastolic blood pressure, to account for the underlying correlation between the two measures within the same individual. The bivariate spline surfaces are allowed to vary across different groups of interest. We have developed related model fitting and inference procedures. The proposed method is used to analyze data from a real clinical investigation.