ArticleInternational journal of epidemiology2021
Reflection on modern methods: Dynamic prediction using joint models of longitudinal and time-to-event data.
Article in International journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.BMC medical research methodology · 2025Pooled it
- Weight Loss, Lifestyle Intervention, and Metformin Affect Longitudinal Relationship of Insulin Secretion and Sensitivity.The Journal of clinical endocrinology and metabolism · 2022 · on this mapTrial
- The SHEP score for pediatric hypertensive encephalopathy: a prediction model or a near-event warning score?Pediatric nephrology (Berlin, Germany) · 2026Article
- Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments.Lifetime data analysis · 2026Article
- Longitudinal evaluation of serial ctDNA kinetics using joint modeling in patients with metastatic breast cancer.NPJ precision oncology · 2026Article
- Clinical correlates for major bleeding during veno-arterial extracorporeal membrane oxygenation support: a time-dependent perspective.Critical care (London, England) · 2026Article
- Dynamic prediction of paroxysmal atrial fibrillation onset using longitudinal sample entropy in joint models.BMC medical research methodology · 2026Article
- The therapy-conditioned host: a state-transition framework for second primary cancer evolution.Frontiers in oncology · 2026Article
- Toward personalized medicine in AD/ADRD through genetic-exposome dementia risk assessments.NPJ dementia · 2026Review
- From digital twins to clinically trustworthy twins: a clinical-claim-based validation framework for personalized digital health.Frontiers in digital health · 2026Article
- On the Importance of a Clear Definition of Time Horizon for Time-to-Event Dynamic Predictions: A Systematic Review and a Concrete Illustration in Kidney Transplantation.Clinical epidemiology · 2026Article
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- The Role of Peritoneal Interleukin-6 in Predicting Patient Survival on Peritoneal Dialysis.Kidney international reports · 2025Article
- Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach.Statistics in medicine · 2025Article
- Prognostic scoring systems in chronic myeloid leukaemia.Leukemia · 2025Review
- A different perspective on studying stroke predictors: joint models for longitudinal and time-to-event data in a type 2 diabetes mellitus cohort.Cardiovascular diabetology · 2025Article
- A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data.Statistics in medicine · 2025Article
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- A sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma.CPT: pharmacometrics & systems pharmacology · 2025Article
- A Bayesian Joint Model of Multiple Nonlinear Longitudinal and Competing Risks Outcomes for Dynamic Prediction in Multiple Myeloma: Joint Estimation and Corrected Two-Stage Approaches.Statistics in medicine · 2025Article
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Authors and funding
4 authors.
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Abstract
Individualized prediction is a hallmark of clinical medicine and decision making. However, most existing prediction models rely on biomarkers and clinical outcomes available at a single time. This is in contrast to how health states progress and how physicians deliver care, which relies on progressively updating a prognosis based on available information. With the use of joint models of longitudinal and survival data, it is possible to dynamically adjust individual predictions regarding patient prognosis. This article aims to introduce the reader to the development of dynamic risk predictions and to provide the necessary resources to support their implementation and assessment, such as adaptable R code, and the theory behind the methodology. Furthermore, measures to assess the predictive performance of the derived predictions and extensions that could improve the predictions are presented. We illustrate personalized predictions using an online dataset consisting of patients with chronic liver disease (primary biliary cirrhosis).
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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.