Journal ArticleDOI
A New Bayesian Model For Survival Data With a Surviving Fraction
TLDR
In this article, the authors consider right-censored survival data for populations with a surviving (cure) fraction and propose a model that is quite different from the standard mixture model for cure rates.Abstract:
We consider Bayesian methods for right-censored survival data for populations with a surviving (cure) fraction. We propose a model that is quite different from the standard mixture model for cure rates. We provide a natural motivation and interpretation of the model and derive several novel properties of it. First, we show that the model has a proportional hazards structure, with the covariates depending naturally on the cure rate. Second, we derive several properties of the hazard function for the proposed model and establish mathematical relationships with the mixture model for cure rates. Prior elicitation is discussed in detail, and classes of noninformative and informative prior distributions are proposed. Several theoretical properties of the proposed priors and resulting posteriors are derived, and comparisons are made to the standard mixture model. A real dataset from a melanoma clinical trial is discussed in detail.read more
Citations
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Journal ArticleDOI
Power prior distributions for regression models
Joseph G. Ibrahim,Ming-Hui Chen +1 more
TL;DR: In this paper, the authors propose a general class of prior distributions for arbitrary regression models, called power prior distributions, which are based on the idea of raising the likelihood function of the historical data to the power ao, where 0 < ao < 1.
Journal ArticleDOI
Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models.
TL;DR: The utility of the bounded cumulative hazard model in cure rate estimation is considered, which is an appealing alternative to the widely used two-component mixture model, and is particularly suitable for semiparametric and Bayesian methods of statistical inference.
Journal ArticleDOI
The power prior: theory and applications.
TL;DR: An A-to-Z exposition of the power prior and its applications to date is given, including its theoretical properties, variations in its formulation, statistical contexts for which it has been used, applications, and its advantages over other informative priors.
Journal ArticleDOI
Flexible Cure Rate Modeling Under Latent Activation Schemes
TL;DR: A unifying class of cure rate models is proposed that facilitates flexible hierarchical model building while including both existing cure model classes as special cases and enables robust modeling by accounting for uncertainty in underlying mechanisms leading to cure.
Journal ArticleDOI
Cure models as a useful statistical tool for analyzing survival
TL;DR: By using cure models, rather than the standard Cox proportional hazards model, it is shown that whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors is evaluated.
References
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Journal ArticleDOI
Interferon alfa-2b adjuvant therapy of high-risk resected cutaneous melanoma: the Eastern Cooperative Oncology Group Trial EST 1684.
John M. Kirkwood,Myla Hunt Strawderman,Marc S. Ernstoff,Thomas J. Smith,Ernest C. Borden,Ronald H. Blum +5 more
TL;DR: IFN alpha-2b is the first agent to show a significant benefit in relapse-free and overall survival of high-risk melanoma patients in a randomized controlled trial.
Journal ArticleDOI
Adaptive Rejection Sampling for Gibbs Sampling
W. R. Gilks,Pascal Wild +1 more
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.
Journal ArticleDOI
Monte Carlo Estimation of Bayesian Credible and HPD Intervals
Ming-Hui Chen,Qi-Man Shao +1 more
TL;DR: In this article, a Markov chain Monte Carlo (MCMC) sampling algorithm is used to estimate Bayesian credible and highest probability density (HPD) intervals for parameters of interest and provides a simple Monte Carlo approach to approximate these Bayesian intervals when a sample of the relevant parameters can be generated from their respective marginal posterior distribution using a sample from an importance sampling distribution.
Journal ArticleDOI
Survival Curve for Cancer Patients Following Treatment
Joseph Berkson,Robert P. Gage +1 more
TL;DR: A simple function, in terms of two physically meaningful parameters, has been evolved, which fits survivorship data very well and can be used to compare succinctly the mortality of two groups, different in respect of treatment, type of cancer, or other characteristics.