Approximate Bayesian Inference for Survival Models

Sara Martino*, Rupali Akerkar, Haavard Rue

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Bayesian analysis of time-to-event data, usually called survival analysis, has received increasing attention in the last years. In Cox-type models it allows to use information from the full likelihood instead of from a partial likelihood, so that the baseline hazard function and the model parameters can be jointly estimated. In general, Bayesian methods permit a full and exact posterior inference for any parameter or predictive quantity of interest. On the other side, Bayesian inference often relies on Markov chain Monte Carlo (MCMC) techniques which, from the user point of view, may appear slow at delivering answers. In this article, we show how a new inferential tool named integrated nested Laplace approximations can be adapted and applied to many survival models making Bayesian analysis both fast and accurate without having to rely on MCMC-based inference.

Original languageEnglish (US)
Pages (from-to)514-528
Number of pages15
JournalScandinavian Journal of Statistics
Volume38
Issue number3
DOIs
StatePublished - Sep 1 2011

Keywords

  • Approximate inference
  • Bayesian hazard rate model
  • Geoadditive hazard regression
  • Laplace approximation
  • Latent Gaussian fields

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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