ArticleStatistical methods in medical research2019
Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Joint Modeling of Longitudinal and Survival Data in Public Health and Biomedical Research: A Systematic Review.International journal of environmental research and public health · 2026Pooled 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
- 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
- Shape-Based Partially Linear Single-Index Cox Model for Alzheimer's Disease Conversion.Statistics in medicine · 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
- Ensemble survival analysis for preclinical cognitive decline prediction in Alzheimer's disease using longitudinal biomarkers.Journal of Alzheimer's disease : JAD · 2025Article
- Pattern Mixture Sensitivity Analyses via Multiple Imputations for Non-Ignorable Dropout in Joint Modeling of Cognition and Risk of Dementia.Statistics in medicine · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- Functional analysis within latent states: A novel framework for analysing functional time series data.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
- BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.The annals of applied statistics · 2023Article
- Multivariate functional mixed model with MRI data: An application to Alzheimer's disease.Statistics in medicine · 2023Article
- Article
- Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective.Journal of applied statistics · 2022Article
- Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.Statistics in medicine · 2021Article
- Reflection on modern methods: Dynamic prediction using joint models of longitudinal and time-to-event data.International journal of epidemiology · 2021Article
- Dynamic prediction based on variability of a longitudinal biomarker.BMC medical research methodology · 2021Article
- Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles.BMC bioinformatics · 2021Article
- Robust Bayesian Analysis of Early-Stage Parkinson's Disease Progression Using DaTscan Images.IEEE transactions on medical imaging · 2021Article
- Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort.Frontiers in big data · 2021Review
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2 authors.
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Abstract
In the study of Alzheimer's disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients' disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects' future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian functional joint model provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer's Disease Neuroimaging Initiative study.
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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.