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Bivariate random change point models for longitudinal outcomes
Author(s) -
Yang Lili,
Gao Sujuan
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5557
Subject(s) - bivariate analysis , univariate , random effects model , markov chain monte carlo , bayesian probability , inference , computer science , econometrics , bayesian inference , statistics , mathematics , medicine , multivariate statistics , meta analysis , artificial intelligence
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.