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dc.contributor.authorRaffa, Jesse Daniel 16:00:49 (GMT) 16:00:49 (GMT)
dc.description.abstractLongitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a multivariate longitudinal response can be attributed to underlying unobserved disease states in addition to any between-subject differences. We propose modeling such disease states using a hidden Markov model (HMM) approach and expand upon previous work, which incorporated random effects into HMMs for the analysis of univariate longitudinal data, to the setting of a multivariate longitudinal response. Multivariate longitudinal data are modeled jointly using separate but correlated random effects between longitudinal responses of mixed data types in addition to a shared underlying hidden process. We use a computationally efficient Bayesian approach via Markov chain Monte Carlo (MCMC) to fit such models. We apply this methodology to bivariate longitudinal response data from a smoking cessation clinical trial. Under these models, we examine how to incorporate a treatment effect on the disease states, as well as develop methods to classify observations by disease state and to attempt to understand patient dropout. Simulation studies were performed to evaluate the properties of such models and their applications under a variety of realistic situations.en
dc.publisherUniversity of Waterlooen
dc.subjectmultivariate longitudinal dataen
dc.subjecthidden markov modelen
dc.subjectrandom effectsen
dc.subjectMarkov chain monte carloen
dc.titleMultivariate Longitudinal Data Analysis with Mixed Effects Hidden Markov Modelsen
dc.typeDoctoral Thesisen
dc.subject.programStatistics (Biostatistics)en and Actuarial Scienceen
uws-etd.degreeDoctor of Philosophyen

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