On Bayesian inference for proportional hazards models using noninformative priors

In this article, we investigate the properties of the posterior distribution under the uniform improper prior for two commonly used proportional hazards models; the Weibull regression model and the extreme value regression model. We allow the observations to be right censored. We obtain sufficient c...

Full description

Saved in:
Bibliographic Details
Published inLifetime data analysis Vol. 6; no. 4; pp. 331 - 341
Main Authors Kim, S W, Ibrahim, J G
Format Journal Article
LanguageEnglish
Published United States Springer Nature B.V 01.12.2000
Subjects
Bayes Theorem
Bayesian analysis
Cancer
Generalized linear models
Humans
Hypothesis testing
Likelihood Functions
Lung Neoplasms - drug therapy
Lung Neoplasms - mortality
Medical prognosis
Models, Statistical
Proportional Hazards Models
Public health
Regression Analysis
Simulation
Studies
Survival Analysis
Theorems
Online AccessGet full text

Cover

More Information
Summary:In this article, we investigate the properties of the posterior distribution under the uniform improper prior for two commonly used proportional hazards models; the Weibull regression model and the extreme value regression model. We allow the observations to be right censored. We obtain sufficient conditions for the existence of the posterior moment generating function of the regression coefficients. A dataset involving a lung cancer clinical trial and a simulation are presented to illustrate our results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1380-7870
1572-9249
DOI:10.1023/A:1026505331236