Bivariate random change point models for longitudinal outcomes.

Abstract:

: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.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Yang L,Gao S

doi

10.1002/sim.5557

subject

Has Abstract

pub_date

2013-03-15 00:00:00

pages

1038-53

issue

6

eissn

0277-6715

issn

1097-0258

journal_volume

32

pub_type

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