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A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data
Author(s) -
Lange Jane M.,
Hubbard Rebecca A.,
Inoue Lurdes Y. T.,
Minin Vladimir N.
Publication year - 2015
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12252
Subject(s) - computer science , sampling (signal processing) , markov chain , statistics , point process , poisson distribution , disease , markov model , econometrics , data mining , artificial intelligence , machine learning , mathematics , medicine , pathology , filter (signal processing) , computer vision
Summary 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.