ArticleBiostatistics (Oxford, England)2024
Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations.
Article in Biostatistics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Social engagement, formal education, and cognitive outcomes among older adults in India: A longitudinal population-based study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Advancing multilevel Bayesian networks with efficient Bayesian inference.Statistical methods in medical research · 2026Article
- Joint Models of Two Longitudinal Biomarkers and Clustered Survival Data for Cluster-Informed Dynamic Prediction With Application to Periodontitis.Statistics in medicine · 2026Article
- Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments.Lifetime data analysis · 2026Article
- Deep learning embedded latent class joint modelling of time-to-event and longitudinal data.Statistical methods in medical research · 2026Article
- The impact of blood pressure variability and African Caribbean ethnicity on the progression of diabetic kidney disease in type 1 diabetes.Diabetologia · 2026Article
- Exploring the association between higher education and steeper cognitive decline in a nationally representative longitudinal study in India.American journal of epidemiology · 2026Article
- Spatial joint modelling of multivariate longitudinal outcomes and cure proportion using latent Gaussian model with application to dataset on HIV/AIDS patients.Scientific reports · 2026Article
- Particulate air pollution from different emission sources, cognitive performance, and cognitive declines in India.Environment international · 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
- Associations between blood-based neurodegenerative biomarkers and cognitive functioning and decline in India.Journal of Alzheimer's disease : JAD · 2025Article
- Validation of a Longitudinal Marker as a Surrogate Using Mediation Analysis and Joint Modeling: Evolution of the PSA as a Surrogate of the Disease-Free Survival.Biometrical journal. Biometrische Zeitschrift · 2025Article
- The Cox-Pólya-Gamma algorithm for flexible Bayesian inference of multilevel survival models.Biometrics · 2025Article
- Joint models in big data: simulation-based guidelines for required data quality in longitudinal electronic health records.BioData mining · 2025Article
- A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data.Biometrics · 2025Article
- Bayesian shared parameter joint models for heterogeneous populations.Statistics and computing · 2025Article
- A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage method.Statistics in medicine · 2024Article
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5 authors.
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Abstract
Modeling longitudinal and survival data jointly offers many advantages such as addressing measurement error and missing data in the longitudinal processes, understanding and quantifying the association between the longitudinal markers and the survival events, and predicting the risk of events based on the longitudinal markers. A joint model involves multiple submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. Their estimation is computationally expensive (particularly due to a multidimensional integration of the likelihood over the random effects distribution) so that inference methods become rapidly intractable, and restricts applications of joint models to a small number of longitudinal markers and/or random effects. We introduce a Bayesian approximation based on the integrated nested Laplace approximation algorithm implemented in the R package R-INLA to alleviate the computational burden and allow the estimation of multivariate joint models with fewer restrictions. Our simulation studies show that R-INLA substantially reduces the computation time and the variability of the parameter estimates compared with alternative estimation strategies. We further apply the methodology to analyze five longitudinal markers (3 continuous, 1 count, 1 binary, and 16 random effects) and competing risks of death and transplantation in a clinical trial on primary biliary cholangitis. R-INLA provides a fast and reliable inference technique for applying joint models to the complex multivariate data encountered in health research.
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