ArticleThe annals of applied statistics2017
DYNAMIC PREDICTION FOR MULTIPLE REPEATED MEASURES AND EVENT TIME DATA: AN APPLICATION TO PARKINSON'S DISEASE.
Article in The annals of applied statistics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials.CPT: pharmacometrics & systems pharmacology · 2024Trial
- 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
- Computationally efficient Bayesian inference for semi-parametric joint models of competing risks survival and skewed longitudinal data using integrated nested Laplace approximation.BMC medical research methodology · 2025Article
- Systematic review of prognostic models in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- DYNAMIC PREDICTION WITH MULTIVARIATE LONGITUDINAL OUTCOMES AND LONGITUDINAL MAGNETIC RESONANCE IMAGING DATA.The annals of applied statistics · 2025Article
- A sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma.CPT: pharmacometrics & systems pharmacology · 2025Article
- VARIANCE AS A PREDICTOR OF HEALTH OUTCOMES: SUBJECT-LEVEL TRAJECTORIES AND VARIABILITY OF SEX HORMONES TO PREDICT BODY FAT CHANGES IN PERI- AND POSTMENOPAUSAL WOMEN.The annals of applied statistics · 2024Article
- DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES.The annals of applied statistics · 2023Article
- Application of longitudinal item response theory models to modeling Parkinson's disease progression.CPT: pharmacometrics & systems pharmacology · 2022Article
- Dissecting the Domains of Parkinson's Disease: Insights from Longitudinal Item Response Theory Modeling.Movement disorders : official journal of the Movement Disorder Society · 2022Article
- Novel Approach to Movement Disorder Society-Unified Parkinson's Disease Rating Scale Monitoring in Clinical Trials: Longitudinal Item Response Theory Models.Movement disorders clinical practice · 2021Article
- Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Predicting the Survival of AIDS Patients Using Two Frameworks of Statistical Joint Modeling and Comparing Their Predictive Accuracy.Iranian journal of public health · 2020Article
- Bayesian joint modelling of longitudinal and time to event data: a methodological review.BMC medical research methodology · 2020Review
- Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data.BioMed research international · 2020Article
- A Bayesian approach for individual-level drug benefit-risk assessment.Statistics in medicine · 2019Article
- Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology.JCO precision oncology · 2019Article
- Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data.Computational statistics & data analysis · 2019Article
- Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.BMC medical research methodology · 2017Article
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Authors and funding
3 authors.
Funding
Abstract
In many clinical trials studying neurodegenerative diseases such as Parkinson's disease (PD), multiple longitudinal outcomes are collected to fully explore the multidimensional impairment caused by this disease. If the outcomes deteriorate rapidly, patients may reach a level of functional disability sufficient to initiate levodopa therapy for ameliorating disease symptoms. An accurate prediction of the time to functional disability is helpful for clinicians to monitor patients' disease progression and make informative medical decisions. In this article, we first propose a joint model that consists of a semiparametric multilevel latent trait model (MLLTM) for the multiple longitudinal outcomes, and a survival model for event time. The two submodels are linked together by an underlying latent variable. We develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting target patients' future outcome trajectories and risk of a survival event, based on their multivariate longitudinal measurements. Our proposed model is evaluated by simulation studies and is applied to the DATATOP study, a motivating clinical trial assessing the effect of deprenyl among patients with early PD.
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