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Bioequivalence approach for whole effluent toxicity testing
Author(s) -
Shukla Rakesh,
Wang Qin,
Fulk Florence,
Deng Chunqin,
Denton Debra
Publication year - 2000
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620190120
Subject(s) - bioequivalence , ceriodaphnia dubia , statistical power , statistics , statistical hypothesis testing , test (biology) , econometrics , mathematics , toxicity , biology , chemistry , pharmacology , acute toxicity , paleontology , bioavailability , organic chemistry
Increased use of whole effluent toxicity (WET) tests in the regulatory arena has brought increased concern over the statistical analysis of WET test data and the determination of toxicity. One concern is the issue of statistical power. A number of WET tests may pass the current hypothesis test approach because they lack statistical power to detect relevant toxic effects because of large within‐test variability. Additionally, a number of WET tests may fail the current approach because they possess excessive statistical power, as a result of small within‐test variability, and detect small differences that may not be biologically relevant. The strengths and limitations of both the traditional hypothesis test approach and the bioequivalence approach for use in the National Pollutant Discharge Elimination System program were evaluated. Data from 5,213 single‐concentration, short‐term chronic WET tests with Ceriodaphnia dubia provided the database for analysis. Comparison of results between the current approach and the bioequivalence approach indicates that the current approach to WET testing is generally sound but that adopting the proposed bioequivalence approach resolves concerns of statistical power. Specifically, within this data set, applying the bioequivalence approach resulted in failure for tests with relatively large test variability and a pass for tests with relatively small within‐test variability.

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