Bayesian Nonparametric Nonproportional Hazards Survival Modeling

Maria De Iorio, Wesley O. Johnson, Peter Müller, Gary L. Rosner

Research output: Contribution to journalArticle

Abstract

We develop a dependent Dirichlet process model for survival analysis data. A major feature of the proposed approach is that there is no necessity for resulting survival curve estimates to satisfy the ubiquitous proportional hazards assumption. An illustration based on a cancer clinical trial is given, where survival probabilities for times early in the study are estimated to be lower for those on a high-dose treatment regimen than for those on the low dose treatment, while the reverse is true for later times, possibly due to the toxic effect of the high dose for those who are not as healthy at the beginning of the study.

Original languageEnglish (US)
Pages (from-to)762-771
Number of pages10
JournalBiometrics
Volume65
Issue number3
DOIs
StatePublished - Sep 1 2009

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Keywords

  • Censoring
  • Dependent Dirichlet process
  • Markov chain Monte Carlo

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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