Bayesian survival analysis with INLA

Danilo Alvares; Janet van Niekerk; Elias Teixeira Krainski; Håvard Rue; Denis Rustand

doi:10.1002/sim.10160

Bayesian survival analysis with INLA

Danilo Alvares^*, Janet van Niekerk, Elias Teixeira Krainski, Håvard Rue, Denis Rustand

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.” 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.

Original language	English (US)
Pages (from-to)	3975-4010
Number of pages	36
Journal	Statistics in medicine
Volume	43
Issue number	20
DOIs	https://doi.org/10.1002/sim.10160
State	Published - Sep 10 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.

Keywords

Bayesian inference
INLA
joint modeling
R-packages
time-to-event analysis

ASJC Scopus subject areas

Epidemiology
Statistics and Probability

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10.1002/sim.10160

Cite this

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title = "Bayesian survival analysis with INLA",

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.” 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.",

keywords = "Bayesian inference, INLA, joint modeling, R-packages, time-to-event analysis",

author = "Danilo Alvares and {van Niekerk}, Janet and Krainski, {Elias Teixeira} and H{\aa}vard Rue and Denis Rustand",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.",

year = "2024",

month = sep,

day = "10",

doi = "10.1002/sim.10160",

language = "English (US)",

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AU - Krainski, Elias Teixeira

AU - Rue, Håvard

AU - Rustand, Denis

PY - 2024/9/10

Y1 - 2024/9/10

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AB - 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.

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KW - joint modeling

KW - R-packages

KW - time-to-event analysis

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JO - Statistics in medicine

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Bayesian survival analysis with INLA

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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