title: Multivariate longitudinal data analysis with mixed effects hidden Markov models.
abstract: Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies.
authors.affiliation: Department of Statistics, University of Washington, Seattle, Washington, U.S.A.
authors.forename: Jesse D
authors.initials: JD
authors.lastname: Raffa
authors.affiliation: Department of Statistics & Actuarial Science and School of Public Health & Health Systems, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Joel A
authors.initials: JA
authors.lastname: Dubin
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D015233:Models, Statistical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care / Q000379:methods*; D015995:Prevalence; D012907:Smoking / Q000453:epidemiology*; D016540:Smoking Cessation / Q000379:methods / Q000706:statistics & numerical data; D000074606:Smoking Prevention; D061485:Tobacco Use Cessation Devices / Q000706:statistics & numerical data; D016896:Treatment Outcome
keywords: Hidden Markov model; Hidden disease state; Longitudinal data; Markov chain Monte Carlo; Multivariate response; Smoking cessation