title: Bayesian analysis of non-homogeneous Markov chains: application to mental health data.
abstract: In this paper we present a formal treatment of non-homogeneous Markov chains by introducing a hierarchical Bayesian framework. Our work is motivated by the analysis of correlated categorical data which arise in assessment of psychiatric treatment programs. In our development, we introduce a Markovian structure to describe the non-homogeneity of transition patterns. In doing so, we introduce a logistic regression set-up for Markov chains and incorporate covariates in our model. We present a Bayesian model using Markov chain Monte Carlo methods and develop inference procedures to address issues encountered in the analyses of data from psychiatric treatment programs. Our model and inference procedures are implemented to some real data from a psychiatric treatment study.
authors.affiliation: School of Business Administration, Ajou University, Suwon, Korea. sungmj@ajou.ac.kr
authors.forename: Minje
authors.initials: M
authors.lastname: Sung
authors.forename: Refik
authors.initials: R
authors.lastname: Soyer
authors.forename: Nguyen
authors.initials: N
authors.lastname: Nhan
mesh_terms: D000293:Adolescent; D001499:Bayes Theorem; D002648:Child; D006801:Humans; D008390:Markov Chains; D001523:Mental Disorders / Q000628:therapy*; D008603:Mental Health; D009010:Monte Carlo Method