ArticleStatistics in medicine2019
Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective.CPT: pharmacometrics & systems pharmacology · 2026Pooled 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
- A Comparison of Testing Procedures for Detecting Short- and Long-Term Effects of Biomarkers for Cancer Screening.Statistics in medicine · 2026Article
- A Functional Joint Model for Survival and Multivariate Sparse Functional Data in Multi-Cohort Alzheimer's Disease Study.Statistics in medicine · 2026Article
- Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach.Statistics in medicine · 2025Article
- Characterizing bidirectional transitions in mild cognitive impairment and post-reversion based on longitudinal neuroimaging and cognitive assessments.Alzheimer's & dementia : the journal of the Alzheimer's Association · 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
- Supervised Functional Principal Component Analysis Under the Mixture Cure Rate Model: An Application to Alzheimer'S Disease.Statistics in medicine · 2025Article
- A comparative study of methods for dynamic survival analysis.Frontiers in neurology · 2025Article
- Modeling correlated pairs of mammogram images.Statistics in medicine · 2024Article
- Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.Statistical methods in medical research · 2024Article
- Functional principal component analysis and sparse-group LASSO to identify associations between biomarker trajectories and mortality among hospitalized SARS-CoV-2 infected individuals.BMC medical research methodology · 2023Article
- Neural network on interval-censored data with application to the prediction of Alzheimer's disease.Biometrics · 2023Article
- Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study.BMC medicine · 2023Article
- A classification for complex imbalanced data in disease screening and early diagnosis.Statistics in medicine · 2022Article
- Deep learning for the dynamic prediction of multivariate longitudinal and survival data.Statistics in medicine · 2022Article
- Clinically approved combination immunotherapy: Current status, limitations, and future perspective.Current research in immunology · 2022Review
- Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer's disease progression.Statistical methods in medical research · 2021Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
This paper is motivated by combining serial neurocognitive assessments and other clinical variables for monitoring the progression of Alzheimer's disease (AD). We propose a novel framework for the use of multiple longitudinal neurocognitive markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not applicable when there is a large number of longitudinal outcomes. We introduce various approaches based on functional principal component for dimension reduction and feature extraction from multiple longitudinal outcomes. We use these features to extrapolate the health outcome trajectories and use scores on these features as predictors in a Cox proportional hazards model to conduct predictions over time. We propose a personalized dynamic prediction framework that can be updated as new observations collected to reflect the patient's latest prognosis, and thus intervention could be initiated in a timely manner. Simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the robustness of the method for the prediction of future health outcomes and risks of target events under various scenarios.
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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.