ArticleBiometrics2022
Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 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
- Longitudinal Sparse Single-Omics Factor Analysis for High-Dimensional Blood Biomarkers in Alzheimer's Disease.Statistics in medicine · 2026Article
- Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.Journal of the American Statistical Association · 2026Article
- A Functional Joint Model for Survival and Multivariate Sparse Functional Data in Multi-Cohort Alzheimer's Disease Study.Statistics in medicine · 2026Article
- Joint modeling of high-dimensional longitudinal data and survival using supervised low-rank tensor decomposition.Biostatistics (Oxford, England) · 2026Article
- Dynamic Prediction Using Functional Latent Trait Joint Models for Multivariate Longitudinal Outcomes: An Application to Parkinson's Disease.Statistics in medicine · 2025Article
- Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach.Statistics in medicine · 2025Article
- Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer's Disease.Statistics in medicine · 2025Article
- A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data.Biometrics · 2025Article
- Characterizing heterogeneity in Alzheimer's disease progression: a semiparametric model.Scientific reports · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies.Statistics in biosciences · 2024Article
- Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach.BMC medical research methodology · 2024Article
- Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.Statistics and its interface · 2024Article
- Recurrent event analysis in the presence of real-time high frequency data via random subsampling.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Latent factor model for multivariate functional data.Biometrics · 2023Article
- 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
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model to identify the shared progression pattern and outcome-specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model. Data used in preparation of this article were obtained from the ADNI database.
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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.