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Activity Number: 608
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #314963
Title: A Flexible Cure Rate Model for Spatially Correlated Survival Data Based on Generalized Extreme Value Distribution and Gaussian Process Priors
Author(s): Dan Li* and Xia Wang and Dipak K. Dey
Companies: University of Cincinnati and University of Cincinnati and University of Connecticut
Keywords: cure rate model ; Gaussian process ; generalized extreme value distribution ; Markov chain Monte Carlo (MCMC) ; spatial effect ; surviving modeling
Abstract:

Our work proposes a new survival model in a Bayesian context to analyze right-censored survival data for populations with a surviving fraction, assuming the log failure time follows a generalized extreme value (GEV) distribution. Many applications require a more flexible modeling of covariate information than simple linear or parametric form. Also, the spatial variation is sometimes unexplained by the covariates considered in the analysis. Therefore, nonlinear covariate effects and spatial effects are incorporated into the systematic component of our model. Gaussian processes (GPs) provide a natural framework for modeling potentially nonlinear relationship and have become powerful in nonlinear regression. Our model adopts a semi-parametric Bayesian approach by imposing a GP prior on the nonlinear structure of continuous covariate. With the consideration of computational complexity, the conditionally autoregressive distribution is placed on the region-specific frailties to handle spatial correlation. The flexibility and gains of our proposed model are illustrated through simulation studies and analysis of a dataset involving a colon cancer clinical trial from the state of Iowa.


Authors who are presenting talks have a * after their name.

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