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

Hai Liu, Wanzhu Tu

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1861-1882
Number of pages22
JournalAnnals of Applied Statistics
Volume6
Issue number4
DOIs
StatePublished - 2012

Fingerprint

Semiparametric Regression Model
Blood pressure
Blood Pressure
Bivariate Splines
Splines
Thin-plate Spline
Joint Model
Model Fitting
Nonlinear Effects
Semiparametric regression
Regression model
Childhood
Concurrent
Vary
Interaction

Keywords

  • Bootstrap
  • Factor-by-surface interaction
  • Mixed effects model
  • Paired outcomes
  • Penalized estimation
  • Thin plate spline

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

Cite this

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