JSM 2005 Online Program

Abstract #304460

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	259
Type:	Topic Contributed
Date/Time:	Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #304460
Title:	Modeling Spatially Correlated Data for Individuals with Multiple Cancers
Author(s):	Ulysses Diva*+ and Sudipto Banerjee and Dipak Dey
Companies:	University of Connecticut and University of Minnesota and University of Connecticut
Address:	215 Glenbrook Rd. U-4120, Storrs, CT, 06269-4120, United States
Keywords:	Bayesian Heirarchical Models ; Frailty Models ; MCMC ; Mixture of Beta Functions ; Spatial Association ; Survival Modeling
Abstract:	One issue of interest to epidemiologists is the nature of spatial variation in survival data. The huge volume of data available from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute also calls for novel approaches in modeling cancer survival, paying particular attention to models that account for spatial clustering and variation. Modeling survival data of patients with multiple cancers poses unique challenges in terms of capturing the spatial correlation of the different cancers. This paper develops Bayesian hierarchical survival models for capturing spatial patterns within the framework of proportional hazards. Spatial variation is introduced in the form of county-cancer level frailties. The baseline hazard function is modeled semiparametrically using mixtures of beta distributions. We illustrate with data from the SEER database, perform model checking and comparison among competing models, and discuss implementation issues. A final best model is presented with special attention given to the spatial structure.

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