Events

Abstract:

This presentation introduces INLAjoint, a user-friendly R package that simplifies the fitting of various survival models using the computationally efficient Integrated Nested Laplace Approximations (INLA) method. INLA offers a significant speed advantage over traditional Markov Chain Monte Carlo (MCMC) methods while maintaining accuracy in parameter estimation. INLAjoint supports a wide range of survival models, including proportional hazards, multi-state, and joint models for multivariate longitudinal and survival data. Joint models, which link multiple regression submodels through correlated or shared random effects, can be computationally intensive. In this context, we underscore the significant reduction in computation time achieved by INLA when compared to MCMC, without compromising on accuracy.

Beyond model fitting, the talk provides practical guidance on using the INLAjoint R package, including detailed syntax examples. A key application of joint models is dynamic prediction, which involves estimating the risk of an event (e.g., death or disease progression) based on changes in longitudinal outcomes over time. INLAjoint enables the estimation of dynamic risk predictions and can incorporate updates to these predictions as new longitudinal data becomes available. This makes INLAjoint a valuable tool for analyzing complex health data.

Speaker:

Denis Rustand obtained his Ph.D. in Public Health, Biostatistics in 2020 at University of Bordeaux, France, where he developed the joint modeling framework for longitudinal and survival data in the context of cancer clinical trials data analysis. He is now a Post-Doctoral fellow at KAUST where he joined the INLA development team. He is the main developer and maintainer of the INLAjoint R package, a user-friendly interface to fit joint longitudinal-survival models with INLA. His research areas include Bayesian computational statistics, survival analysis and applications of statistics to medical research.

References:

Danilo Alvares, Janet Van Niekerk, Elias Teixeira Krainski, Håvard Rue, and Denis Rustand. Bayesian survival analysis with INLA. Statistics in Medicine 43, no. 20 (2024): 3975-4010.

Denis Rustand, Janet van Niekerk, Elias T. Krainski, and Håvard Rue. Joint Modeling of Multivariate Longitudinal and Survival Outcomes with the R package INLAjoint. Preprint arXiv:2402.08335 (2024).

Please contact doctoral researcher Abderrahim Oussama Batouche (abderrahim.batouche@helsinki.fi) if you have any questions.