Bivariate random change point models for longitudinal outcomes

Lili Yang, Sujuan Gao

Research output: Contribution to journalArticle

6 Scopus citations


Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes, including biomarker measures, cognitive functions, and clinical symptoms. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. Univariate change point models have been used to model various clinical endpoints, suchasCD4 count in studying the progression of HIV infection and cognitive function in the elderly. We propose to use bivariate change point models for two longitudinal outcomes with a focus on the correlation betweenthetwo change points. We consider three types of change point models in the bivariate model setting: the broken-stick model, the Bacon-Watts model, and the smooth polynomial model. We adopt a Bayesianapproachusing a Markov chain Monte Carlo sampling method for parameter estimation and inference. We assess the proposed methods in simulation studies and demonstrate the methodology using data from a longitudinal study of dementia.

Original languageEnglish (US)
Pages (from-to)1038-1053
Number of pages16
JournalStatistics in Medicine
Issue number6
StatePublished - Mar 15 2013


  • Bayesian method
  • Longitudinal bivariate outcomes
  • Random change point model

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'Bivariate random change point models for longitudinal outcomes'. Together they form a unique fingerprint.

  • Cite this