Application of GEV in Analysis of Survival Data

• Activity Number: 186
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Dooti Roy*+ and Dipak Dey and Vivekananda Roy
• Companies: University of Connecticut and University of Connecticut and Iowa State University
• Keywords: Bayesian Inference ; Generalized Extreme Value Distribution ; Survival Analysis ; SEER ; Metropolis Hastings ; Breast Cancer

Abstract

This paper introduces the generalized extreme value (GEV) distribution to analyze right-censored survival data for populations with a surviving fraction. We deal with both GEV minima and GEV maxima models to show that our proposed GEV model leads to extremely ?exible hazard functions. We show that our Bayesian model has several nice properties. For example, we prove that even when objective priors are used, the resulting posterior distribution could still be proper under some weak conditions. We further provide theoretical and numerical results showing that our GEV models offer a richer class of models than the widely used Weibull models. For simulation purpose, the cure fraction is kept around 10% to 15% to match real life scenarios. Finally, two real recent data sets,one of melanoma cancer data and another data set of breast cancer from SEER database are analyzed to illustrate the applicability of our proposed GEV models.