JSM 2005 - Toronto

Abstract #302392

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 48
Type: Invited
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: WNAR
Abstract - #302392
Title: Bayesian Spatial Boundary Analysis for Areal Health Outcome Data
Author(s): Bradley P. Carlin*+ and Haijun Ma
Companies: University of Minnesota and University of Minnesota
Address: MMC 303 Division of Biostatistics, Minneapolis, MN, 55455,
Keywords: hierarchical model ; spatial model ; MCMC ; boundary analysis
Abstract:

In the analysis of spatially referenced data, interest often focuses not on prediction of the spatially indexed variable itself, but on boundary analysis (i.e., the determination of boundaries on the map that separate areas of higher and lower values). Existing boundary analysis methods are sometimes generically referred to as ``wombling,'' after a foundational paper by Womble (1951). In this paper, we propose MCMC-driven hierarchical Bayesian methods for areal data (i.e., data that consist only of sums or averages over geopolitical regions). Our methods employ conditionally autoregressive (CAR) models in the usual way for capturing the similarity of rates in neighboring regions, as well as in a novel way to model the similarity of the likelihood that neighboring region-separating segments are part of the wombled boundary. For multivariate response data, multivariate CAR (MCAR) models also emerge as helpful. We illustrate our methods with an analysis of service areas of competing cancer hospice care systems measured at the ZIP code level in northeastern Minnesota.

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Revised March 2005