
pmid: 38922936
arXiv: 2212.01900
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.” 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.
FOS: Computer and information sciences, Models, Statistical, Bayesian inference, Bayes Theorem, Survival Analysis, Applications of statistics to biology and medical sciences; meta analysis, joint modeling, Methodology (stat.ME), time‐to‐event analysis, INLA, Humans, Computer Simulation, Longitudinal Studies, R-packages, time-to-event analysis, R‐packages, Statistics - Methodology, Software, Proportional Hazards Models
FOS: Computer and information sciences, Models, Statistical, Bayesian inference, Bayes Theorem, Survival Analysis, Applications of statistics to biology and medical sciences; meta analysis, joint modeling, Methodology (stat.ME), time‐to‐event analysis, INLA, Humans, Computer Simulation, Longitudinal Studies, R-packages, time-to-event analysis, R‐packages, Statistics - Methodology, Software, Proportional Hazards Models
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