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Journal ArticleDOI

Bayesian analysis of generalized odds-rate hazards models for survival data

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TLDR
A class of nonproportional hazards models known as generalized odds-rate class of regression models, which is general enough to include several commonly used models, such as proportional hazards model, proportional odds model, and accelerated life time model are considered.
Abstract
In the analysis of censored survival data Cox proportional hazards model (1972) is extremely popular among the practitioners. However, in many real-life situations the proportionality of the hazard ratios does not seem to be an appropriate assumption. To overcome such a problem, we consider a class of nonproportional hazards models known as generalized odds-rate class of regression models. The class is general enough to include several commonly used models, such as proportional hazards model, proportional odds model, and accelerated life time model. The theoretical and computational properties of these models have been re-examined. The propriety of the posterior has been established under some mild conditions. A simulation study is conducted and a detailed analysis of the data from a prostate cancer study is presented to further illustrate the proposed methodology.

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Citations
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Journal ArticleDOI

Minorize–maximize algorithm for the generalized odds rate model for clustered current status data

TL;DR: In this article , a unified methodology to analyze complex data subject to clustering is presented, where the time to event is assumed to follow a semiparametric generalized odds rate (GOR) model.

Semiparametric Regression Analysis of Bivariate Interval-Censored Data

TL;DR: This dissertation develops efficient statistical approaches for regression analysis of bivariate interval censored data, in which the two survival times of interest are correlated and both have an interval-censored data structure.
Journal ArticleDOI

An EM algorithm for analyzing right-censored survival data under the semiparametric proportional odds model

TL;DR: The semiparametric proportional odds (PO) model is a popular alternative to Cox's proportional hazards model for analyzing survival data as discussed by the authors, and has been shown to outperform other models.
Journal ArticleDOI

A flexible Bayesian non-parametric approach for fitting the odds to case II interval-censored data

TL;DR: A flexible Bayesian non-parametric procedure is introduced for the estimation of the odds under interval censoring, case II using Bernstein polynomials to introduce a prior for modeling the odds and a novel and easy-to-implement sampling manner based on the Markov chain Monte Carlo algorithms to study the posterior distributions.
References
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Book ChapterDOI

Regression Models and Life-Tables

TL;DR: The analysis of censored failure times is considered in this paper, where the hazard function is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time.
Proceedings Article

Information Theory and an Extention of the Maximum Likelihood Principle

H. Akaike
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI

Information Theory and an Extension of the Maximum Likelihood Principle

TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.
Book

Analysis of Survival Data

David Cox, +1 more
TL;DR: In this article, the authors give a concise account of the analysis of survival data, focusing on new theory on the relationship between survival factors and identified explanatory variables and conclude with bibliographic notes and further results that can be used for student exercises.
Journal ArticleDOI

Adaptive Rejection Sampling for Gibbs Sampling

TL;DR: In this paper, the authors proposed a method for rejection sampling from any univariate log-concave probability density function, which is adaptive: as sampling proceeds, the rejection envelope and the squeezing function converge to the density function.
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