ArticleStatistics in medicine2017
Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.BMC medical research methodology · 2025Pooled it
- Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer's disease with Fortasyn Connect.BMC medical research methodology · 2019Trial
- 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
- Bayesian adaptive selection of basis functions for functional data representation.Journal of applied statistics · 2024Article
- Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.JAMIA open · 2021Review
- A marginal estimate for the overall treatment effect on a survival outcome within the joint modeling framework.Statistics in medicine · 2020Article
- Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study.Biometrics · 2020Article
- Time to poor treatment outcome and its predictors among drug-resistant tuberculosis patients on second-line anti-tuberculosis treatment in Amhara region, Ethiopia: retrospective cohort study.BMC public health · 2019Article
- Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.Statistics in medicine · 2019Article
- Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data.Computational statistics & data analysis · 2019Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
Functional data are increasingly collected in public health and medical studies to better understand many complex diseases. Besides the functional data, other clinical measures are often collected repeatedly. Investigating the association between these longitudinal data and time to a survival event is of great interest to these studies. In this article, we develop a functional joint model (FJM) to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. The parameters of FJM are estimated in a maximum likelihood framework via expectation maximization algorithm. The proposed FJM provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. The FJM is evaluated by a simulation study and is applied to the Alzheimer's Disease Neuroimaging Initiative study, a motivating clinical study testing whether serial brain imaging, clinical, and neuropsychological assessments can be combined to measure the progression of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.
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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.