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Activity Number: 97
Type: Topic Contributed
Date/Time: Monday, July 30, 2007 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #310075
Title: Mixtures of Polya Trees for Flexible Spatial Frailty Survival Modeling
Author(s): Luping Zhao*+ and Timothy E. Hanson and Brad Carlin
Companies: The University of Minnesota and The University of Minnesota and The University of Minnesota
Address: 1008 27th Ave SE Apt F, Minneapolis, MN, 55414,
Keywords: Areal data ; Bayesian modeling ; Breast cancer ; Conditionally autoregressive (CAR) model ; Log pseudo marginal likelihood (LPML) ; Nonparametric modeling

Mixtures of Polya trees offer a very flexible, nonparametric approach for modeling time-to-event data. Many such settings also feature spatial association that requires further sophistication, either at a point (geostatistical) or areal (lattice) level. In this paper we combine these two aspects within three competing survival models, obtaining a data analytic approach that remains computationally feasible in a fully hierarchical Bayesian framework thanks to modern Markov chain Monte Carlo methods. We illustrate the usefulness of our proposed methods with an analysis of spatially oriented breast cancer survival data from the Surveillance, Epidemiology, and End Results program of the National Cancer Institute. Our results indicate appreciable advantages for our approach over previous, competing methods that impose unrealistic parametric assumptions, ignore spatial association, or both.

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