ArticleInternational statistical review = Revue internationale de statistique2019
Review and Comparison of Computational Approaches for Joint Longitudinal and Time-to-Event Models.
Article in International statistical review = Revue internationale de statistique, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Bayesian joint modelling of multiple longitudinal biomarkers and survival time of cardiac patients under clinical follow-up.BMC cardiovascular disorders · 2025Article
- Bayesian inference for nonlinear mixed-effects location scale and interval-censoring cure-survival models: An application to pregnancy miscarriage.Statistical methods in medical research · 2025Article
- OUTCOME-GUIDED DISEASE SUBTYPING BY GENERATIVE MODEL AND WEIGHTED JOINT LIKELIHOOD IN TRANSCRIPTOMIC APPLICATIONS.The annals of applied statistics · 2024Article
- jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data.BMC medical research methodology · 2024Article
- Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.Biostatistics (Oxford, England) · 2024Article
- Modeling unmeasured baseline information in observational time-to-event data subject to delayed study entry.Statistical methods in medical research · 2023Article
- A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior.Entropy (Basel, Switzerland) · 2020Article
- Age and life expectancy clocks based on machine learning analysis of mouse frailty.Nature communications · 2020Article
- Genetics of physiological dysregulation: findings from the long life family study using joint models.Aging · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Joint models for longitudinal and time-to-event data are useful in situations where an association exists between a longitudinal marker and an event time. These models are typically complicated due to the presence of shared random effects and multiple submodels. As a consequence, software implementation is warranted that is not prohibitively time consuming. While methodological research in this area continues, several statistical software procedures exist to assist in the fitting of some joint models. We review the available implementation for frequentist and Bayesian models in the statistical programming languages R, SAS, and Stata. A description of each procedure is given including estimation techniques, input and data requirements, available options for customization, and some available extensions, such as competing risks models. The software implementations are compared and contrasted through extensive simulation, highlighting their strengths and weaknesses. Data from an ongoing trial on adrenal cancer patients is used to study different nuances of software fitting on a practical example.
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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.