Fitting and interpreting continuous-time latent Markov models for panel data.

ABSTRACT: Multistate models characterize disease processes within an individual. Clinical studies often observe the disease status of individuals at discrete time points, making exact times of transitions between disease states unknown. Such panel data pose considerable modeling challenges. Assuming the disease process progresses accordingly, a standard continuous-time Markov chain (CTMC) yields tractable likelihoods, but the assumption of exponential sojourn time distributions is typically unrealistic. More flexible semi-Markov models permit generic sojourn distributions yet yield intractable likelihoods for panel data in the presence of reversible transitions. One attractive alternative is to assume that the disease process is characterized by an underlying latent CTMC, with multiple latent states

SUBMITTER: Lange JM 

PROVIDER: S-EPMC3795797 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature