ArticleJournal of statistical computation and simulation2016
Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome.
Article in Journal of statistical computation and simulation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Joint analysis of blood pressure changes over time and survival of hypertensive patients under treatment at Sawula hospital in Southern Ethiopia.BMC public health · 2025Article
- Incidence and predictors of chronic kidney disease among hypertensive patients in Ethiopia: A Bayesian multivariate joint model.PloS one · 2025Article
- JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA.The annals of applied statistics · 2024Article
- Dynamic risk score modeling for multiple longitudinal risk factors and survival.Computational statistics & data analysis · 2024Article
- Joint modeling of longitudinal changes of pulse rate and body temperature with time to recovery of pneumonia patients under treatment: a prospective cohort study.BMC infectious diseases · 2023Article
- The analysis of multiple outcomes, multiple variables and variables selection in hematopoietic cell transplantation studies.Best practice & research. Clinical haematology · 2023Review
- Joint Modeling of Blood Pressure Measurements and Survival Time to Cardiovascular Disease Complication among Hypertension Patients Follow-up at DebreTabor Hospital, Ethiopia.Vascular health and risk management · 2023Article
- A Joint Model for Unbalanced Nested Repeated Measures with Informative Drop-Out Applied to Ambulatory Blood Pressure Monitoring Data.BioMed research international · 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
- Pathway testing for longitudinal metabolomics.Statistics in medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation-maximization (EM) algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure (BP) measures and time to coronary artery disease (CAD).
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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.