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Activity Number:
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411
- Innovative Modeling of Risks in the SAMSI GDRR Program
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
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Sponsor:
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Section on Risk Analysis
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Abstract #312649
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Title:
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Modeling Risks from Environmental Mixtures
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Author(s):
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Sanjib Basu* and Jiyeong Jang
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Companies:
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University of Illinois At Chicago and University of Illinois at Chicago
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Keywords:
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Collinearity;
Environmental exposure;
Variable selection;
Feature Screening
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Abstract:
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Understanding health risks from environment mixtures is of growing scientific interest and presents unique statistical methodological challenges. Environmental mixtures include a large number of pollutants which potentially interact and presents health risk. Individuals are exposed simultaneously to a multitude of the pollutants in the environmental mixture. Each pollutant may have weak individual effect that contributes to the overall heath effect of the mixture. Further, the pollutant measurements are often extremely correlated at levels that are generally not seen in other areas of science. To address these challenges, we develop a method that can effectively identify a set of influential mixture components and make improved predictions of the health risk. The performance of this method is evaluated through an extensive set of simulation studies and real-world data applications
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Authors who are presenting talks have a * after their name.
