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

DOI10.1111/biom.12252zbMath1419.62384OpenAlexW1956239603WikidataQ30860620 ScholiaQ30860620MaRDI QIDQ3465728

Rebecca Hubbard, Vladimir N. Minin, Jane M. Lange, Lurdes Y. T. Inoue

Publication date: 22 January 2016

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://biostats.bepress.com/uwbiostat/paper401

zbMATH Keywords

panel datamultistate modelMarkov-modulated Poisson processelectronic medical recordsdisease processinformative observations

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Markov processes: estimation; hidden Markov models (62M05)

Related Items (10)

Estimating and comparing cancer progression risks under varying surveillance protocols ⋮ Life history analysis with multistate models: A review and some current issues ⋮ Bayesian clustering for continuous‐time hidden Markov models ⋮ Bayesian latent multi‐state modeling for nonequidistant longitudinal electronic health records ⋮ Meeting the assumptions of inverse-intensity weighting for longitudinal data subject to irregular follow-up: suggestions for the design and analysis of clinic-based cohort studies ⋮ A Bayesian accelerated failure time model for interval censored three-state screening outcomes ⋮ Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification ⋮ Bayesian inference for continuous-time hidden Markov models with an unknown number of states ⋮ Unnamed Item ⋮ Least-square estimators in linear regression models under negatively superadditive dependent random observations

Uses Software

• R
• numDeriv

Cites Work

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