Article first published online: 15 AUG 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 32, Issue 6, pages 1038–1053, 15 March 2013
How to Cite
Yang, L. and Gao, S. (2013), Bivariate random change point models for longitudinal outcomes. Statist. Med., 32: 1038–1053. doi: 10.1002/sim.5557
- Issue published online: 14 FEB 2013
- Article first published online: 15 AUG 2012
- Manuscript Accepted: 17 JUL 2012
- Manuscript Revised: 15 JUL 2012
- Manuscript Received: 14 NOV 2011
- National Institutes of Health. Grant Numbers: R01 AG019181, R01 AG09956, P30 AG10133
- random change point model;
- longitudinal bivariate outcomes;
- Bayesian method
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, such as CD4 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 between the two 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 Bayesian approach using 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. Copyright © 2012 John Wiley & Sons, Ltd.