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Your Environment. Your Health.

FLEXIBLE BAYESIAN HIERARCHICAL MODELS FOR ESTIMATING INHALATION EXPOSURES

Export to Word (http://www.niehs.nih.gov//portfolio/index.cfm/portfolio/grantdetail/grant_number/R01ES030210/format/word)
Principal Investigator: Banerjee, Sudipto
Institute Receiving Award University Of California Los Angeles
Location Los Angeles, CA
Grant Number R01ES030210
Funding Organization National Institute of Environmental Health Sciences
Award Funding Period 15 Dec 2018 to 30 Nov 2022
DESCRIPTION (provided by applicant): Project Summary/Abstract We propose to develop innovative statistical tools for melding exposure models and observational data aris- ing from measurements of concentrations in controlled chamber conditions. As a first step, we will construct a rich dataset of exposure scenarios in laboratory exposure chambers and real workplace settings, contain- ing data on exposure determinants such as contaminant generation and ventilation rates and exposure mea- surements. We will develop a comprehensive and computationally feasible Bayesian statistical framework for melding the physical exposure models with experimental data from the workplace to effectively account for the sources of uncertainty and produce reliable statistical inference (estimation and predictions). We will employ a Bayesian framework to validate physical models from monitoring data. Our framework will also include formal statistical measures for validating models with observed field data. We do so by assessing how adequately the models capture features and patterns in the monitoring data, applying sensitivity analysis to the choice of priors, and choosing or selecting a model among a set of competing models. We will also develop and disseminate a user-friendly statistical software package that will enable researchers to implement the proposed methods for a wide variety of physical models to analyze their data in a seamless and convenient manner. Upon successful completion of the project, our developments will allow researchers and exposure managers to systematically evaluate retrospective exposure, to predict current and future exposure in the absence of the working process or operation, and to estimate exposure with only a small number of air samples with possibly high variability. With only a few monitoring data points, our Bayesian melding framework will provide more precise estimates of exposure than monitoring. With advances in computational methods and inexpensive software implementation, we purport to exalt formal modeling to an indispensable position in the exposure assessors' armory.
Science Code(s)/Area of Science(s) Primary: 81 - Statistics/Statistical Methods/Development
Secondary: 03 - Carcinogenesis/Cell Transformation
Publications See publications associated with this Grant.
Program Officer Bonnie Joubert
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