ArticleStatistics in medicine2014
Bayesian hierarchical model for multiple repeated measures and survival data: an application to Parkinson's disease.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- Plasma Tie2 is a tumor vascular response biomarker for VEGF inhibitors in metastatic colorectal cancer.Nature communications · 2018Trial
- A Bayesian Multilevel Joint Modeling of Longitudinal and Survival Outcomes in Cluster Randomized Controlled Trial Studies.Statistics in medicine · 2026Article
- Investigating the simultaneous effect of longitudinal biomarkers on long-term kidney transplant failure in Iranian kidney transplant patients: a multivariate joint model.BMC nephrology · 2025Article
- Dynamic Prediction Using Functional Latent Trait Joint Models for Multivariate Longitudinal Outcomes: An Application to Parkinson's Disease.Statistics in medicine · 2025Article
- Single-Index Mixed-Effects Model for Asymmetric Bivariate Clustered Data.Journal of the Indian Society for Probability and Statistics · 2024Article
- Predicting Verbal Learning and Memory Assessments of Older Adults Using Bayesian Hierarchical Models.Frontiers in psychology · 2022Article
- Multivariate Longitudinal Assessment of Kidney Function Outcomes on Graft Survival after Kidney Transplantation Using Multivariate Joint Modeling Approach: A Retrospective Cohort Study.Iranian journal of medical sciences · 2021Article
- Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.Statistics in medicine · 2020Article
- Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.The Lancet. Neurology · 2017Article
- Multidimensional latent trait linear mixed model: an application in clinical studies with multivariate longitudinal outcomes.Statistics in medicine · 2017Article
- DYNAMIC PREDICTION FOR MULTIPLE REPEATED MEASURES AND EVENT TIME DATA: AN APPLICATION TO PARKINSON'S DISEASE.The annals of applied statistics · 2017Article
- Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues.BMC medical research methodology · 2016Article
- Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model.Statistical methods in medical researchArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Multilevel item response theory models have been increasingly used to analyze the multivariate longitudinal data of mixed types (e.g., continuous and categorical) in clinical studies. To address the possible correlation between multivariate longitudinal measures and time to terminal events (e.g., death and dropout), joint models that consist of a multilevel item response theory submodel and a survival submodel have been previously developed. However, in multisite studies, multiple patients are recruited and treated by the same clinical site. There can be a significant site correlation because of common environmental and socioeconomic status, and similar quality of care within site. In this article, we develop and study several hierarchical joint models with the hazard of terminal events dependent on shared random effects from various levels. We conduct extensive simulation study to evaluate the performance of various models under different scenarios. The proposed hierarchical joint models are applied to the motivating deprenyl and tocopherol antioxidative therapy of Parkinsonism study to investigate the effect of tocopherol in slowing Parkinson's disease progression.
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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.