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A new stochastic kriging method for modeling multi-source exposure-response data in toxicology studies.

Authors
Wang-K; Chen-X; Yang-F; Porter-DW; Wu-N
Source
ACS Sustain Chem Eng 2014 Jul; 2(7):1581-1591
NIOSHTIC No.
20044540
Abstract
One of the most fundamental steps in risk assessment is to quantify the exposure-response relationship for the material/chemical of interest. This work develops a new statistical method, referred to as SKQ (stochastic kriging with qualitative factors), to synergistically model exposure-response data, which often arise from multiple sources (e.g., laboratories, animal providers, and shapes of nanomaterials) in toxicology studies. Compared to the existing methods, SKQ has several distinct features. First, SKQ integrates data across multiple sources and allows for the derivation of more accurate information from limited data. Second, SKQ is highly flexible and able to model practically any continuous response surfaces (e.g., dose-time-response surface). Third, SKQ is able to accommodate variance heterogeneity across experimental conditions and to provide valid statistical inference (i.e., quantify uncertainties of the model estimates). Through empirical studies, we have demonstrated SKQ's ability to efficiently model exposure-response surfaces by pooling information across multiple data sources. SKQ fits into the mosaic of efficient decision-making methods for assessing the risk of a tremendously large variety of nanomaterials and helps to alleviate safety concerns regarding the enormous amount of new nanomaterials.
Keywords
Toxicology; Risk-factors; Statistical-analysis; Analytical-processes; Dose-response; Models; Nanotechnology; Safety-measures; Safety-practices; Author Keywords: Exposure-response; Multi-source data; Stochastic kriging; Gaussian process; Nanotoxicology
Contact
Feng Yang, Department of Industrial and Management System Engineering, West Virginia University, Morgantown, West Virginia 26506
CODEN
ASCECG
Publication Date
20140707
Document Type
Journal Article
Email Address
feng.yang@mail.wvu.edu
Fiscal Year
2014
NTIS Accession No.
NTIS Price
Identifying No.
M062014
Issue of Publication
7
ISSN
2168-0485
NIOSH Division
HELD
Priority Area
Manufacturing
Source Name
ACS Sustainable Chemistry and Engineering
State
WV; VA
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