NDLI: Detecting Departure From Additivity Along a Fixed-Ratio Mixture Ray With a Piecewise Model for Dose and Interaction Thresholds

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Detecting Departure From Additivity Along a Fixed-Ratio Mixture Ray With a Piecewise Model for Dose and Interaction Thresholds

Content Provider	PubMed Central
Author	Yeatts, Sharon D. Gennings, Chris Wagner, Elizabeth D. Simmons, Jane Ellen Plewa, Michael J.
Copyright Year	2010
Abstract	For mixtures of many chemicals, a ray design based on a relevant, fixed mixing ratio is useful for detecting departure from additivity. Methods for detecting departure involve modeling the response as a function of total dose along the ray. For mixtures with many components, the interaction may be dose dependent. Therefore, we have developed the use of a three-segment model containing both a dose threshold and an interaction threshold. Prior to the dose threshold, the response is that of background; between the dose threshold and the interaction threshold, an additive relationship exists; the model allows for departure from additivity beyond the interaction threshold. With such a model, we can conduct a hypothesis test of additivity, as well as a test for a region of additivity. The methods are illustrated with cytotoxicity data that arise when Chinese hamster ovary cells are exposed to a mixture of nine haloacetic acids.
Related Links	http://dx.doi.org/10.1007/s13253-010-0030-x
Ending Page	522
Page Count	13
Starting Page	510
File Format	PDF
ISSN	10857117
Journal	Journal of agricultural, biological, and environmental statistics
Issue Number	4
Volume Number	15
Language	English
Publisher Date	2010-12-01
Access Restriction	Open
Subject Keyword	Agricultural and Biological Sciences (miscellaneous) Statistics, Probability and Uncertainty Statistics and Probability Applied Mathematics Agricultural and Biological Sciences(all) Environmental Science(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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