A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data.

ABSTRACT: Multistate models are used to characterize individuals' natural histories through diseases with discrete states. Observational data resources based on electronic medical records pose new opportunities for studying such diseases. However, these data consist of observations of the process at discrete sampling times, which may either be pre-scheduled and non-informative, or symptom-driven and informative about an individual's underlying disease status. We have developed a novel joint observation and disease transition model for this setting. The disease process is modeled according to a latent continuous-time Markov chain; and the observation process, according to a Markov-modulated Poisson process with observation rates that depend on the individual's underlying disease status. The disease p

SUBMITTER: Lange JM 

PROVIDER: S-EPMC4415156 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature