Mixtures of Polya trees for flexible spatial frailty survival modelling

Luping Zhao, Timothy E. Hanson, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

Mixtures of Polya trees offer a very flexible nonparametric approach for modelling time-to-event data. Many such settings also feature spatial association that requires further sophistication, either at the point level or at the lattice level. In this paper, we combine these two aspects within three competing survival models, obtaining a data analytic approach that remains computationally feasible in a fully hierarchical Bayesian framework using Markov chain Monte Carlo methods. We illustrate our proposed methods with an analysis of spatially oriented breast cancer survival data from the Surveillance, Epidemiology and End Results program of the National Cancer Institute. Our results indicate appreciable advantages for our approach over competing methods that impose unrealistic parametric assumptions, ignore spatial association or both.

Original languageEnglish (US)
Pages (from-to)263-276
Number of pages14
JournalBiometrika
Volume96
Issue number2
DOIs
StatePublished - Jun 2009

Keywords

  • Areal data
  • Bayesian modelling
  • Breast cancer
  • Conditionally autoregressive model
  • Log pseudo marginal likelihood
  • Nonparametric modelling

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