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 Medjournal_title
Statistics in medicineauthors
Yang L,Gao Sdoi
10.1002/sim.5557subject
Has Abstractpub_date
2013-03-15 00:00:00pages
1038-53issue
6eissn
0277-6715issn
1097-0258journal_volume
32pub_type
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