title: 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 process is observed at a combination of informative and non-informative sampling times, with possible misclassification error. We demonstrate that the model is computationally tractable and devise an expectation-maximization algorithm for parameter estimation. Using simulated data, we show how estimates from our joint observation and disease transition model lead to less biased and more precise estimates of the disease rate parameters. We apply the model to a study of secondary breast cancer events, utilizing mammography and biopsy records from a sample of women with a history of primary breast cancer.
authors.affiliation: Department of Bioststatistics, University of Washington, Seattle, Washington, U.S.A.
authors.forename: Jane M
authors.initials: JM
authors.lastname: Lange
authors.affiliation: Department of Bioststatistics, University of Washington, Seattle, Washington, U.S.A.|Biostatistics Unit, Group Health Research Institute, Seattle, Washington, U.S.A.
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: Hubbard
authors.affiliation: Department of Bioststatistics, University of Washington, Seattle, Washington, U.S.A.
authors.forename: Lurdes Y T
authors.initials: LYT
authors.lastname: Inoue
authors.affiliation: Departments of Statistics and Biology, University of Washington, Seattle, Washington, U.S.A.
authors.forename: Vladimir N
authors.initials: VN
authors.lastname: Minin
mesh_terms: D001943:Breast Neoplasms / Q000453:epidemiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000453:epidemiology*; D011159:Population Surveillance / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Disease process; Electronic medical records; Informative observations; Markov-modulated Poisson process; Multistate model; Panel data