The separation of timescales in Bayesian survival modeling of the time-varying effect of a time-dependent exposure

Sebastien J P A Haneuse, Kyle D. Rudser, Daniel L. Gillen

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

In this paper, we apply flexible Bayesian survival analysis methods to investigate the risk of lymphoma associated with kidney transplantation among patients with end-stage renal disease. Of key interest is the potentially time-varying effect of a time-dependent exposure: transplant status. Bayesian modeling of the baseline hazard and the effect of transplant requires consideration of 2 timescales: time since study start and time since transplantation, respectively. Previous related work has not dealt with the separation of multiple timescales. Using a hierarchical model for the hazard function, both timescales are incorporated via conditionally independent stochastic processes; smoothing of each process is specified via intrinsic conditional Gaussian autoregressions. Features of the corresponding posterior distribution are evaluated from draws obtained via a Metropolis-Hastings-Green algorithm.

Original languageEnglish (US)
Pages (from-to)400-410
Number of pages11
JournalBiostatistics
Volume9
Issue number3
DOIs
StatePublished - Jul 2008

Keywords

  • Bayesian survival analysis
  • Conditional autoregression
  • Nonproportional hazards
  • Reversible jump Markov chain Monte Carlo

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