Classification of Disease States Under a Mixed-Effects Hidden Markov Model with Application to a Smoking Cessation Clinical Trial

• Activity Number: 175
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jesse Raffa*+ and Joel A. Dubin
• Companies: University of Washington and University of Waterloo
• Keywords: smoking cessation ; HMM ; random effects ; multivariate longitudinal data ; MCMC

Abstract

Longitudinal studies, where data on study subjects are collected over time, is increasingly involving multivariate longitudinal responses. Frequently, the heterogeneity observed in a longitudinal response can be attributed to underlying unobserved disease states in addition to 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 MCMC to fit such models. We apply this methodology to bivariate longitudinal response data from a smoking cessation clinical trial. Under these models we develop methods to classify observations by disease state. Simulation studies were performed to evaluate the properties of such models and their applications under a variety of realistic situations.