ArticleBiometrics2012
Dynamic logistic regression and dynamic model averaging for binary classification.
Article in Biometrics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed.
- Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.Diagnostic and prognostic research · 2026Review
- Predicting microscopic vehicle collision risks at toll plaza diverging area using bayesian dynamic logistic regressions.PloS one · 2025Article
- Dynamic updating of clinical survival prediction models in a changing environment.Diagnostic and prognostic research · 2023Article
- Automatic correction of performance drift under acquisition shift in medical image classification.Nature communications · 2023Article
- Article
- Article
- Development and validation of self-monitoring auto-updating prognostic models of survival for hospitalized COVID-19 patients.Nature communications · 2022Article
- Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.NPJ digital medicine · 2022Review
- A Comparison of Methods to Detect Changes in Prediction Models.Methods of information in medicine · 2022Article
- Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Anomaly Detection in Large-Scale Networks With Latent Space Models.Technometrics : a journal of statistics for the physical, chemical, and engineering sciences · 2022Article
- Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model - a large database study protocol.Diagnostic and prognostic research · 2021Article
- Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?Diagnostic and prognostic research · 2021Article
- A novel riboswitch classification based on imbalanced sequences achieved by machine learning.PLoS computational biology · 2020Article
- Dynamic models to predict health outcomes: current status and methodological challenges.Diagnostic and prognostic research · 2018Review
- Statistical Methods for Cohort Studies of CKD: Prediction Modeling.Clinical journal of the American Society of Nephrology : CJASN · 2017Review
- Predicting prolonged dose titration in patients starting warfarin.Pharmacoepidemiology and drug safety · 2016Article
- The prediction accuracy of dynamic mixed-effects models in clustered data.BioData mining · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We propose an online binary classification procedure for cases when there is uncertainty about the model to use and parameters within a model change over time. We account for model uncertainty through dynamic model averaging, a dynamic extension of Bayesian model averaging in which posterior model probabilities may also change with time. We apply a state-space model to the parameters of each model and we allow the data-generating model to change over time according to a Markov chain. Calibrating a "forgetting" factor accommodates different levels of change in the data-generating mechanism. We propose an algorithm that adjusts the level of forgetting in an online fashion using the posterior predictive distribution, and so accommodates various levels of change at different times. We apply our method to data from children with appendicitis who receive either a traditional (open) appendectomy or a laparoscopic procedure. Factors associated with which children receive a particular type of procedure changed substantially over the 7 years of data collection, a feature that is not captured using standard regression modeling. Because our procedure can be implemented completely online, future data collection for similar studies would require storing sensitive patient information only temporarily, reducing the risk of a breach of confidentiality.
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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.