Bayesian analysis of proportional hazards models built from monotone functions. Academic Article

abstract

• 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.

authors

• Mallick, Bani

published proceedings

• Biometrics

altmetric score

• 3

author list (cited authors)

• Gelfand, A. E., & Mallick, B. K.

citation count

• 66

complete list of authors

• Gelfand, AE||Mallick, BK

publication date

• September 1995

publisher

• JSTOR  Publisher

published in

• Biometrics  Journal

keywords

• Algorithms
• Bayes Theorem
• Humans
• Lung Neoplasms
• Markov Chains
• Mathematics
• Models, Statistical
• Monte Carlo Method
• Probability
• Proportional Hazards Models
• Survival Rate

PubMed Central ID

• 7548703

Digital Object Identifier (DOI)

• 10.2307/2532986

start page

• 843

end page

• 852

volume

• 51

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.2307%2F2532986