ReviewBMC medical research methodology2020
Bayesian joint modelling of longitudinal and time to event data: a methodological review.
Review in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 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.
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
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Joint modelling on clinical trials in clinical journals: a systematic review.BMC medical research methodology · 2026Pooled it
- Joint modelling of longitudinal processes and time-to-event outcomes in heart failure: systematic review and exemplar examining the relationship between serum digoxin levels and mortality.BMC medical research methodology · 2023Pooled it
- Informative presence and observation in routine health data: A review of methodology for clinical risk prediction.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Prognostic associations between patient-reported outcomes and overall survival among patients with advanced squamous non-small cell lung cancer: results from the RATIONALE-307 trial.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Trial
- Longitudinal FEVInternational journal of chronic obstructive pulmonary diseaseTrial
- A new integrative learning framework for integrating multiple secondary outcomes into primary outcome analysis: a case study on liver health.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- A Bayesian multilevel joint model for predicting neonatal head circumference and body mass index: a case study in Dalahu County, Iran.BMC pregnancy and childbirth · 2026Article
- Longitudinal evaluation of serial ctDNA kinetics using joint modeling in patients with metastatic breast cancer.NPJ precision oncology · 2026Article
- Joint modeling of longitudinal and time-to-event data for dynamic disease risk prediction using proteomics.Protein science : a publication of the Protein Society · 2026Article
- Adjusting for intercurrent events using Bayesian joint models for longitudinal outcomes in clinical trials.Contemporary clinical trials communications · 2026Article
- Spatial joint modelling of multivariate longitudinal outcomes and cure proportion using latent Gaussian model with application to dataset on HIV/AIDS patients.Scientific reports · 2026Article
- Exploring the optimal follow-up time for resectable colorectal cancer patients: a multicenter, five-year longitudinal cohort study.Scientific reports · 2026Article
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- Bayesian inference for nonlinear mixed-effects location scale and interval-censoring cure-survival models: An application to pregnancy miscarriage.Statistical methods in medical research · 2025Article
- Integrating Dynamic Red Blood Cell Distribution Width Monitoring and β-Blocker Therapy for Mortality Prediction in Intensive Care Unit Cardiomyopathy Patients: A Bayesian Multivariate Joint Model and Machine Learning Study.Diagnostics (Basel, Switzerland) · 2025Article
- Joint Latent Class Models: A Tutorial on Practical Applications in Clinical Research.Statistics in medicine · 2025Article
- A Joint Model for (Un)Bounded Longitudinal Markers, Competing Risks, and Recurrent Events Using Patient Registry Data.Statistics in medicine · 2025Article
- Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data.Cancer immunology, immunotherapy : CII · 2025Article
- Joint modelling of longitudinal data: a scoping review of methodology and applications for non-time to event data.BMC medical research methodology · 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundIn clinical research, there is an increasing interest in joint modelling of longitudinal and time-to-event data, since it reduces bias in parameter estimation and increases the efficiency of statistical inference. Inference and prediction from frequentist approaches of joint models have been extensively reviewed, and due to the recent popularity of data-driven Bayesian approaches, a review on current Bayesian estimation of joint model is useful to draw recommendations for future researches.
methodsWe have undertaken a comprehensive review on Bayesian univariate and multivariate joint models. We focused on type of outcomes, model assumptions, association structure, estimation algorithm, dynamic prediction and software implementation.
resultsA total of 89 articles have been identified, consisting of 75 methodological and 14 applied articles. The most common approach to model the longitudinal and time-to-event outcomes jointly included linear mixed effect models with proportional hazards. A random effect association structure was generally used for linking the two sub-models. Markov Chain Monte Carlo (MCMC) algorithms were commonly used (93% articles) to estimate the model parameters. Only six articles were primarily focused on dynamic predictions for longitudinal or event-time outcomes.
conclusionMethodologies for a wide variety of data types have been proposed; however the research is limited if the association between the two outcomes changes over time, and there is also lack of methods to determine the association structure in the absence of clinical background knowledge. Joint modelling has been proved to be beneficial in producing more accurate dynamic prediction; however, there is a lack of sufficient tools to validate the prediction.
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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.