Bayesian survival analysis with INLA

TL;DR

This tutorial demonstrates how to fit various Bayesian survival models efficiently using INLA in R, covering models like proportional hazards, mixture cure, and joint models, with practical syntax examples.

Contribution

It introduces a comprehensive guide for implementing diverse Bayesian survival models with INLA, including a new joint model for complex data types.

Findings

Efficient implementation of multiple Bayesian survival models using INLA.

Introduction of a new joint model for semicontinuous markers, recurrent, and terminal events.

Practical syntax examples for complex survival analyses.

Abstract

This tutorial shows how various Bayesian survival models can be fitted using the integrated nested Laplace approximation in a clear, legible, and comprehensible manner using the INLA and INLAjoint R-packages. Such models include accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty, and joint models of longitudinal and survival data, originally presented in the article "Bayesian survival analysis with BUGS" (Alvares et al., 2021). In addition, we illustrate the implementation of a new joint model for a longitudinal semicontinuous marker, recurrent events, and a terminal event. Our proposal aims to provide the reader with syntax examples for implementing survival models using a fast and accurate approximate Bayesian inferential approach.