Modelling geographically referenced survival data with a cure fraction.
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.
Cooner
Freda
F
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455-0392, USA.
Banerjee
Sudipto
S
McBean
A Marshall
AM
D001499:Bayes Theorem; D040362:Geographic Information Systems; D006801:Humans; D015233:Models, Statistical; D018426:SEER Program; D017062:Small-Area Analysis; D016019:Survival Analysis; D019452:Terminally Ill / Q000706:statistics & numerical data