ArticleStatistics in medicine2014
A Bayesian approach to joint analysis of multivariate longitudinal data and parametric accelerated failure time.
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 12 papers.
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Who cites it
12 citing papers in PubMed, 28 citations in OpenAlex.
- Dynamic Prediction Using Functional Latent Trait Joint Models for Multivariate Longitudinal Outcomes: An Application to Parkinson's Disease.Statistics in medicine · 2025Article
- Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective.Journal of applied statistics · 2022Article
- A flexible joint model for multiple longitudinal biomarkers and a time-to-event outcome: With applications to dynamic prediction using highly correlated biomarkers.Biometrical journal. Biometrische Zeitschrift · 2021Article
- Incorporating longitudinal biomarkers for dynamic risk prediction in the era of big data: A pseudo-observation approach.Statistics in medicine · 2020Article
- Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective.Statistical methods in medical research · 2018Article
- Estimating the Evolution of Disease in the Parkinson's Progression Markers Initiative.Neuro-degenerative diseases · 2018Article
- Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions.Statistical methods in medical research · 2017Article
- Bayesian multivariate augmented Beta rectangular regression models for patient-reported outcomes and survival data.Statistical methods in medical research · 2017Article
- Bayesian joint ordinal and survival modeling for breast cancer risk assessment.Statistics in medicine · 2016Article
- Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues.BMC medical research methodology · 2016Article
- Predicting Clinical Binary Outcome Using Multivariate Longitudinal Data: Application to Patients with Newly Diagnosed Primary Open-Angle Glaucoma.Journal of biometrics & biostatistics · 2015Article
- Bayesian hierarchical model for multiple repeated measures and survival data: an application to Parkinson's disease.Statistics in medicine · 2014Article
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Authors and funding
1 author at 1 institution in 1 country.
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Abstract
Impairment caused by Parkinson's disease (PD) is multidimensional (e.g., sensoria, functions, and cognition) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of PD use multiple categorical and continuous longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we consider a joint random-effects model for the correlated outcomes. A multilevel item response theory model is used for the multivariate longitudinal outcomes and a parametric accelerated failure time model is used for the failure time because of the violation of proportional hazard assumption. These two models are linked via random effects. The Bayesian inference via MCMC is implemented in 'BUGS' language. Our proposed method is evaluated by a simulation study and is applied to DATATOP study, a motivating clinical trial to determine if deprenyl slows the progression of PD.
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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.