Bayesian analysis of proportional hazards models built from monotone functions.

Published

Journal Article

We consider the usual proportional hazards model in the case where the baseline hazard, the covariate link, and the covariate coefficients are all unknown. Both the baseline hazard and the covariate link are monotone functions and thus are characterized using a dense class of such functions which arises, upon transformation, as a mixture of Beta distribution functions. We take a Bayesian approach for fitting such a model. Since interest focuses more upon the likelihood, we consider vague prior specifications including Jeffreys's prior. Computations are carried out using sampling-based methods. Model criticism is also discussed. Finally, a data set studying survival of a sample of lung cancer patients is analyzed.

Full Text

Duke Authors

Cited Authors

  • Gelfand, AE; Mallick, BK

Published Date

  • September 1995

Published In

Volume / Issue

  • 51 / 3

Start / End Page

  • 843 - 852

PubMed ID

  • 7548703

Pubmed Central ID

  • 7548703

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

International Standard Serial Number (ISSN)

  • 0006-341X

Digital Object Identifier (DOI)

  • 10.2307/2532986

Language

  • eng