title: Time-varying Markov regression random-effect model with Bayesian estimation procedures: Application to dynamics of functional recovery in patients with stroke.
abstract: The rates of functional recovery after stroke tend to decrease with time. Time-varying Markov processes (TVMP) may be more biologically plausible than time-invariant Markov process for modeling such data. However, analysis of such stochastic processes, particularly tackling reversible transitions and the incorporation of random effects into models, can be analytically intractable. We make use of ordinary differential equations to solve continuous-time TVMP with reversible transitions. The proportional hazard form was used to assess the effects of an individual's covariates on multi-state transitions with the incorporation of random effects that capture the residual variation after being explained by measured covariates under the concept of generalized linear model. We further built up Bayesian directed acyclic graphic model to obtain full joint posterior distribution. Markov chain Monte Carlo (MCMC) with Gibbs sampling was applied to estimate parameters based on posterior marginal distributions with multiple integrands. The proposed method was illustrated with empirical data from a study on the functional recovery after stroke.
authors.affiliation: Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan.
authors.forename: Shin-Liang
authors.initials: SL
authors.lastname: Pan
authors.forename: Hsiu-Hsi
authors.initials: HH
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D011336:Probability; D016016:Proportional Hazards Models; D011446:Prospective Studies; D020127:Recovery of Function; D012044:Regression Analysis; D000071939:Stroke Rehabilitation; D013624:Taiwan; D013997:Time Factors