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Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Bayesian survival analysis with INLA.

Danilo Alvares1, Janet van Niekerk2, Elias Teixeira Krainski2

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Statistics in Medicine
|June 26, 2024
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Summary
This summary is machine-generated.

This tutorial demonstrates fitting Bayesian survival models using integrated nested Laplace approximation (INLA) with R-packages. It covers various models, offering syntax examples for fast and accurate Bayesian inference in survival analysis.

Keywords:
Bayesian inferenceINLAR‐packagesjoint modelingtime‐to‐event analysis

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Area of Science:

  • Biostatistics
  • Computational Statistics
  • Survival Analysis

Background:

  • Bayesian survival analysis offers a flexible framework for modeling time-to-event data.
  • Traditional methods can be computationally intensive, especially for complex models.
  • The integrated nested Laplace approximation (INLA) provides an efficient alternative for Bayesian inference.

Purpose of the Study:

  • To provide clear syntax examples for fitting various Bayesian survival models using INLA.
  • To illustrate the application of the INLA and INLAjoint R-packages for survival data analysis.
  • To demonstrate a novel joint model for longitudinal semicontinuous markers, recurrent events, and terminal events.

Main Methods:

  • Utilizing the integrated nested Laplace approximation (INLA) for approximate Bayesian inference.
  • Implementing survival models through the INLA and INLAjoint R-packages.
  • Applying established models (accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, frailty) and a new joint model.

Main Results:

  • Demonstrated the practical implementation of diverse Bayesian survival models using INLA.
  • Provided reproducible syntax examples for accelerated failure time, proportional hazards, mixture cure, competing risks, multi-state, and frailty models.
  • Successfully illustrated a new joint model for complex longitudinal and survival data scenarios.

Conclusions:

  • INLA offers a fast and accurate approach for Bayesian survival analysis.
  • The INLA and INLAjoint R-packages facilitate the implementation of advanced survival models.
  • This tutorial serves as a practical guide for researchers applying Bayesian methods to survival data.