title: Test of significant toxicity: a statistical application for assessing whether an effluent or site water is truly toxic.
abstract: The U.S. Environmental Protection Agency (U.S. EPA) and state agencies implement the Clean Water Act, in part, by evaluating the toxicity of effluent and surface water samples. A common goal for both regulatory authorities and permittees is confidence in an individual test result (e.g., no-observed-effect concentration [NOEC], pass/fail, 25% effective concentration [EC25]), which is used to make regulatory decisions, such as reasonable potential determinations, permit compliance, and watershed assessments. This paper discusses an additional statistical approach (test of significant toxicity [TST]), based on bioequivalence hypothesis testing, or, more appropriately, test of noninferiority, which examines whether there is a nontoxic effect at a single concentration of concern compared with a control. Unlike the traditional hypothesis testing approach in whole effluent toxicity (WET) testing, TST is designed to incorporate explicitly both α and β error rates at levels of toxicity that are unacceptable and acceptable, given routine laboratory test performance for a given test method. Regulatory management decisions are used to identify unacceptable toxicity levels for acute and chronic tests, and the null hypothesis is constructed such that test power is associated with the ability to declare correctly a truly nontoxic sample as acceptable. This approach provides a positive incentive to generate high-quality WET data to make informed decisions regarding regulatory decisions. This paper illustrates how α and β error rates were established for specific test method designs and tests the TST approach using both simulation analyses and actual WET data. In general, those WET test endpoints having higher routine (e.g., 50th percentile) within-test control variation, on average, have higher method-specific α values (type I error rate), to maintain a desired type II error rate. This paper delineates the technical underpinnings of this approach and demonstrates the benefits to both regulatory authorities and permitted entities.
authors.affiliation: U.S. Environmental Protection Agency, Sacramento, California, USA. denton.debra@epa.gov
authors.forename: Debra L
authors.initials: DL
authors.lastname: Denton
authors.forename: Jerry
authors.initials: J
authors.lastname: Diamond
authors.forename: Lei
authors.initials: L
authors.lastname: Zheng
mesh_terms: D003627:Data Interpretation, Statistical; D018675:Toxicity Tests / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D014481:United States; D014484:United States Environmental Protection Agency; D014874:Water Pollutants, Chemical / Q000032:analysis / Q000633:toxicity*; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*
chemical_list: D014874:Water Pollutants, Chemical