A Bayesian semiparametric model for bivariate sparse longitudinal data

Kiranmoy Das, Runze Li, Subhajit Sengupta, Rongling Wu

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Mixed-effects models have recently become popular for analyzing sparse longitudinal data that arise naturally in biological, agricultural and biomedical studies. Traditional approaches assume independent residuals over time and explain the longitudinal dependence by random effects. However, when bivariate or multivariate traits are measured longitudinally, this fundamental assumption is likely to be violated because of intertrait dependence over time. We provide a more general framework where the dependence of the observations from the same subject over time is not assumed to be explained completely by the random effects of the model. We propose a novel, mixed model-based approach and estimate the error-covariance structure nonparametrically under a generalized linear model framework. We use penalized splines to model the general effect of time, and we consider a Dirichlet process mixture of normal prior for the random-effects distribution. We analyze blood pressure data from the Framingham Heart Study where body mass index, gender and time are treated as covariates. We compare our method with traditional methods including parametric modeling of the random effects and independent residual errors over time. We conduct extensive simulation studies to investigate the practical usefulness of the proposed method. The current approach is very helpful in analyzing bivariate irregular longitudinal traits.

Original languageEnglish (US)
Pages (from-to)3899-3910
Number of pages12
JournalStatistics in Medicine
Volume32
Issue number22
DOIs
StatePublished - Sep 30 2013

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Statistics and Probability

Cite this