ArticleBiometrics2015
A joint model for multistate disease processes and random informative observation times, with applications to electronic medical records data.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A Comparison of Methods for Modeling Multistate Cancer Progression Using Screening Data with Censoring after Intervention.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- An Overview and Recent Developments in the Analysis of Multistate Processes.Statistics in medicine · 2026Review
- Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.Biostatistics (Oxford, England) · 2026Article
- Assessing treatment efficacy for interval-censored endpoints using multistate semi-Markov models fit to multiple data streams.Biostatistics (Oxford, England) · 2025Article
- Joint modeling of multistate survival processes with informative examination scheme: application to progressions in diabetes.BMC medical research methodology · 2025Article
- Variable Selection for Progressive Multistate Processes Under Intermittent Observation.Statistics in medicine · 2025Article
- Breast density and risk of breast cancer: masking and detection bias.American journal of epidemiology · 2025Article
- Methodological challenges in studying disease processes using observational cohort data.Japanese journal of statistics and data science · 2025Article
- Learning and visualizing chronic latent representations using electronic health records.BioData mining · 2022Article
- Informative presence bias in analyses of electronic health records-derived data: a cautionary note.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Treatment response after palliative radiotherapy for bleeding gastric cancer: a multicenter prospective observational study (JROSG 17-3).Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2022Observational
- Independence conditions and the analysis of life history studies with intermittent observation.Biostatistics (Oxford, England) · 2021Article
- Bayesian inference for continuous-time hidden Markov models with an unknown number of states.Statistics and computing · 2021Article
- The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.Statistics in medicine · 2020Article
- ESTIMATING AND COMPARING CANCER PROGRESSION RISKS UNDER VARYING SURVEILLANCE PROTOCOLS.The annals of applied statistics · 2018Article
- A state transition framework for patient-level modeling of engagement and retention in HIV care using longitudinal cohort data.Statistics in medicine · 2018Article
- Time-homogeneous Markov process for HIV/AIDS progression under a combination treatment therapy: cohort study, South Africa.Theoretical biology & medical modelling · 2018Article
- Measuring concurrency using a joint multistate and point process model for retrospective sexual history data.Statistics in medicine · 2016Article
- Doctor AI: Predicting Clinical Events via Recurrent Neural Networks.JMLR workshop and conference proceedings · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.