Bayesian cure rate models induced by frailty in survival analysis. (October 2017)

• Record Type: Journal Article
• Authors: de Souza, Daiane; Cancho, Vicente G; Rodrigues, Josemar; Balakrishnan, Narayanaswamy
• Other Names: Balakrishnan N. guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 26:Number 5(2017)
• Page Start: 2011
• Page End: 2028
• Publication Date: 2017-10
• Subjects: Bayesian inference -- frailty models -- proportional hazard models -- long-term survivors -- hyper-Poison distribution -- extended Conway–Maxwell distribution; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217708671
• Languages: English
• ISSNs: 0962-2802

Abstract

Frailty models provide a convenient way of modeling unobserved dependence and heterogeneity in survival data which, if not accounted for duly, would result incorrect inference. Gamma frailty models are commonly used for this purpose, but alternative continuous distributions are possible as well. However, with cure rate being present in survival data, these continuous distributions may not be appropriate since individuals with long-term survival times encompass zero frailty. So, we propose here a flexible probability distribution induced by a discrete frailty, and then present some special discrete probability distributions. We specifically focus on a special hyper-Poisson distribution and then develop the corresponding Bayesian simulation, influence diagnostics and an application to real dataset by means of intensive Markov chain Monte Carlo algorithm. These illustrate the usefulness of the proposed model as well as the inferential results developed here.