Flexible Cure Rate Modeling Under Latent Activation Schemes

Abstract

With rapid improvements in medical treatment and health care, many datasets dealing with time to relapse or death now reveal a substantial portion of patients who are cured (i.e., who never experience the event). Extended survival models called cure rate models account for the probability of a subject being cured and can be broadly classified into the classical mixture models of Berkson and Gage (BG type) or the stochastic tumor models pioneered by Yakovlev and extended to a hierarchical framework by Chen, Ibrahim, and Sinha (YCIS type). Recent developments in Bayesian hierarchical cure models have evoked significant interest regarding relationships and preferences between these two classes of models. Our present work proposes a unifying class of cure rate models that facilitates flexible hierarchical model-building while including both existing cure model classes as special cases. This unifying class enables robust modeling by accounting for uncertainty in underlying mechanisms leading to cure. Issues such as regressing on the cure fraction and propriety of the associated posterior distributions under different modeling assumptions are also discussed. Finally, we offer a simulation study and also illustrate with two datasets (on melanoma and breast cancer) that reveal our framework's ability to distinguish among underlying mechanisms that lead to relapse and cure.

An Evidence of Link between Default and Loss of Bank Loans from the Modeling of Competing Risks

“…We suggest the reader to references in texts Maller and Zhou (1996) and Ibrahim et al (2005). The modeling of competing risks is widespread in literature in articles such as Cooner et al (2006), Cooner et al (2007), Xu et al (2011). Among a wide number of papers, this subject have been aware due important in articles as Chen et al (1999), Tsodikov et al (2003) and Tournoud and Ecochard (2007).…”

Regression models generated by gamma random variables with long-term survivors

“…The literature on the subject is significant and increasing. Books by Maller and Zhou (1996) and Ibrahim et al (2001), as well as the review paper by Chen et al (1999), Tsodikov et al (2003), and the article by Cooner et al (2007) represent key references. Alternatively, other works dealt with cure rate models.…”

Negative Binomial Kumaraswamy-G Cure Rate Regression Model

“…More recently, proposed a unified theory of long duration, considering different competitive causes. In this context, most long-term models make use of this proposal, among which are (Sy and Taylor 2000;Castro et al 2009;Cancho et al 2011;Gu et al 2011), besides (Ibrahim et al 2005;Cooner et al 2007;Ortega et al 2008Ortega et al , 2009Cancho et al 2009).…”