JSM 2005 - Toronto

Abstract #303320

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 236
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract - #303320
Title: A Bayesian Pathways Analysis of Personal Exposure to Arsenic
Author(s): Xiaoyi Dong*+
Companies: The Ohio State University
Address: Rm404 Cockins Hall, Columbus, OH, 43210, United States
Keywords: Hierarchical Modeling ; Latent Variable ; Environmental Health ; Pollution
Abstract:

In environmental health studies, statistical models are used to link levels of toxic substances in environmental media to personal exposure. Biomarker data (e.g., levels in the blood or urine) often are used as a surrogate for personal exposure. In order to learn about personal exposure using biomarker data, a structural equations modeling approach known as a pathways analysis often is used. Given the hierarchical nature of the pathways modeling framework, we propose a Bayesian pathways model that explicitly relates environmental media measurements to a latent personal exposure variable, and then models the relationship between personal exposure and the biomarker data. Our approach is more general than traditional pathways models due to the flexibility of Bayesian modeling. An illustrative analysis using our Bayesian pathways model is given for arsenic; we examine arsenic data from the U.S. Environmental Protection Agency's (EPA) Region 5 collected as part of the National Human Exposure Assessment Survey (NHEXAS).

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Revised March 2005