Efficient Bayesian inference of survival models with integrated nested Laplace approximations through the R package INLAjoint

Denis Rustand (KAUST)

Wednesday 8th May 13:00-14:00 Maths 311B

Abstract

This talk introduces INLAjoint, a user-friendly R package designed for fitting various survival models, harnessing the computational efficiency of the integrated nested Laplace approximations (INLA) methodology. INLA is as a powerful alternative to Markov chain Monte Carlo (MCMC) for Bayesian inference, ensuring both speed and accuracy in parameter estimation. The showcased survival models include proportional hazards, multi-state and joint models for multivariate longitudinal and survival data. A joint model involves multiple regression submodels (one for each longitudinal/survival outcome) usually linked together through correlated or shared random effects. This makes their estimation process rather complex, time-consuming, and sometimes even unfeasible, especially when dealing with many outcomes. In this context, we underscore the significant reduction in computation time achieved by INLA when compared to MCMC, without compromising on accuracy.

Beyond a comprehensive overview of model fitting, the talk emphasizes a practical and hands-on implementation approach. Detailed syntax examples are provided to effectively use the INLAjoint R package. A key application of joint models is the dynamic prediction of the risk of an event, such as death or disease progression, based on changes in the longitudinal outcome(s) over time. INLAjoint allows for the estimation of dynamic risk predictions and can incorporate changes in the longitudinal outcome(s) to update future risk predictions. This makes INLAjoint a valuable tool for analyzing complex health data.

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