ReviewDiagnostic and prognostic research2018
Dynamic models to predict health outcomes: current status and methodological challenges.
Review in Diagnostic and prognostic research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 4 of them syntheses that pooled it.
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.
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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
54 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine learning in the diagnosis and prognosis of transient ischaemic attack: a systematic review.BMC neurology · 2026Pooled it
- Exploring the role of health-related quality of life measures in predictive modelling for oncology: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Pooled it
- Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.BMC medical research methodology · 2022Pooled it
- Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis.BMJ (Clinical research ed.) · 2022Pooled it
- Comparison of Joint and Landmark Modeling for Predicting Cancer Progression in Men With Castration-Resistant Prostate Cancer: A Secondary Post Hoc Analysis of the PREVAIL Randomized Clinical Trial.JAMA network open · 2021Trial
- Scoping review of methodology for aiding generalisability and transportability of clinical prediction models.Diagnostic and prognostic research · 2026Review
- Distilling Knowledge in Gastroenterology: An Artificial Intelligence System for Assisting Colonoscopy and Pathology Report Review.Mayo Clinic proceedings. Digital health · 2026Article
- Development and validation of multivariable risk adjustment models for return of spontaneous circulation and survival to hospital discharge following out-of-hospital cardiac arrest in England.European heart journal. Quality of care & clinical outcomes · 2026Article
- Leveraging transformer-based artificial intelligence for enhanced anesthetic decision-making in orthopedic surgery.Frontiers in medicine · 2026Article
- Perceptions of Biomechanical Devices in Collegiate Baseball Pitchers and Training Staff: A Qualitative Study.International journal of sports physical therapy · 2026Article
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- Prediction and Classification the Risk of Stroke Patients Using Machine Learning Techniques: A Retrospective Cross-Sectional Study.Health science reports · 2025Article
- Recommendations for Validating Omics Prediction Models: Insights from a Lung Cancer RNA Biomarker Study.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025Article
- Update of a prediction model for postoperative shoulder stiffness after arthroscopic rotator cuff repair.Communications medicine · 2025Article
- Interactions between zoonotic pathogens and infectious disease spread: Why understanding mechanisms and modelling matters more than ever.Biosafety and health · 2025Review
- Mortality Prediction Performance Under Geographical, Temporal, and COVID-19 Pandemic Dataset Shift: External Validation of the Global Open-Source Severity of Illness Score Model.Critical care explorations · 2025Article
- The reporting quality and methodological quality of dynamic prediction models for cancer prognosis.BMC medical research methodology · 2025Article
- Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.PLoS medicine · 2025Observational
- Epidemiology and clinical management of acute diarrhoea in dogs under primary veterinary care in the UK.PloS one · 2025Article
- tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDisease populations, clinical practice, and healthcare systems are constantly evolving. This can result in clinical prediction models quickly becoming outdated and less accurate over time. A potential solution is to develop 'dynamic' prediction models capable of retaining accuracy by evolving over time in response to observed changes. Our aim was to review the literature in this area to understand the current state-of-the-art in dynamic prediction modelling and identify unresolved methodological challenges.
methodsMEDLINE, Embase and Web of Science were searched for papers which used or developed dynamic clinical prediction models. Information was extracted on methods for model updating, choice of update windows and decay factors and validation of models. We also extracted reported limitations of methods and recommendations for future research.
resultsWe identified eleven papers that discussed seven dynamic clinical prediction modelling methods which split into three categories. The first category uses frequentist methods to update models in discrete steps, the second uses Bayesian methods for continuous updating and the third, based on varying coefficients, explicitly describes the relationship between predictors and outcome variable as a function of calendar time. These methods have been applied to a limited number of healthcare problems, and few empirical comparisons between them have been made.
conclusionDynamic prediction models are not well established but they overcome one of the major issues with static clinical prediction models, calibration drift. However, there are challenges in choosing decay factors and in dealing with sudden changes. The validation of dynamic prediction models is still largely unexplored terrain.
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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.