Modelling geographically referenced survival data with a cure fraction

Abstract

The emergence of geographical information systems and related softwares nowadays enables medical databases to incorporate the geographical information on patients, allowing studies in spatial associations. Public health administrators and researchers are often interested in detecting variation in survival patterns by region or county in order to understand the possible factors that contribute towards such spatial discrepancies. These issues have led statisticians to develop survival models that account for spatial clustering and variation. Additionally, with rapid developments in medical and health sciences, researchers increasingly encounter data sets where a substantial portion of patients are cured. Models accounting for cure in the population assist in the prognosis of potentially terminal diseases. This article proposes a Bayesian modelling framework that models spatial associations for areally referenced survival data using a general class of cure models proposed by Cooner et al. The special models we outline are alternatives to the traditional proportional hazards models and can be fitted using standard Bayesian software such as WinBUGS.

A general long-term aging model with different underlying activation mechanisms: Modeling, Bayesian estimation, and case influence diagnostics

“…If the event of interest occurs (e.g., cancer relapse), then the random variable Y takes the value of the R th order statistics Z (R) . In other words, as in [Cooner et al, 2006] and [Cooner et al, 2007], R out of M causes are required to produce the event of interest. The resistance factor can be a fixed constant, a function of M or a random variable specified through a conditional distribution on M.…”

“…Another approach, by [Cooner et al, 2006] and [Cooner et al, 2007], forms an arranged stochastic sequence of latent causes, which induce the occurrence of the event of interest via an underlying activation mechanisms that lead to the event of interest.…”

Spatial Data Analysis

“…The reader should see Ibrahim et al (26) for a methodological introduction, whereas Othus et al (34) offer a more recent review and practical introduction. Cooner et al (14) build on their previously proposed flexible framework [13; also see Hurtado Rúa & Dey (25)] to introduce spatial frailties in cure models for geographically referenced data. Banerjee & Carlin (4) propose a spatial extension of earlier work by Chen et al (12), which assumes that some latent biological process is generating the observed data.…”

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).…”