ArticleStatistical methods in medical research2021
Functional survival forests for multivariate longitudinal outcomes: Dynamic prediction of Alzheimer's disease progression.
Article in Statistical methods in medical research, 2021. 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
- 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
- A personalized prediction of longitudinal growth using People-Like-Me methods.Computers in biology and medicine · 2025Article
- Dynamic Prognostic Models for Colorectal Cancer With Liver Metastases.JAMA network open · 2025Article
- Dynamic Prediction of an Event Using Multiple Longitudinal Markers: A Model Averaging Approach.Statistics in medicine · 2025Article
- A comparative study of methods for dynamic survival analysis.Frontiers in neurology · 2025Article
- tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Dynamic prediction of survival using multivariate functional principal component analysis: A strict landmarking approach.Statistical methods in medical research · 2024Article
- Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data.BMC cancer · 2023Article
- 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
- Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.Academic radiology · 2023Review
- Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods.Scientific reports · 2022Article
- 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
- Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.BMC medical research methodology · 2021Article
- Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort.Frontiers in big data · 2021Review
Corrections and comments
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3 authors.
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Abstract
The random survival forest (RSF) is a non-parametric alternative to the Cox proportional hazards model in modeling time-to-event data. In this article, we developed a modeling framework to incorporate multivariate longitudinal data in the model building process to enhance the predictive performance of RSF. To extract the essential features of the multivariate longitudinal outcomes, two methods were adopted and compared: multivariate functional principal component analysis and multivariate fast covariance estimation for sparse functional data. These resulting features, which capture the trajectories of the multiple longitudinal outcomes, are then included as time-independent predictors in the subsequent RSF model. This non-parametric modeling framework, denoted as functional survival forests, is better at capturing the various trends in both the longitudinal outcomes and the survival model which may be difficult to model using only parametric approaches. These advantages are demonstrated through simulations and applications to the Alzheimer's Disease Neuroimaging Initiative.
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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.