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The influence of control group reproduction on the statistical power of the Environmental Protection Agency’s medaka Extended One-Generation Reproduction Test (MEOGRT)
Citation:
Flynn, K., J. Swintek, AND R. Johnson. The influence of control group reproduction on the statistical power of the Environmental Protection Agency’s medaka Extended One-Generation Reproduction Test (MEOGRT). ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY. Elsevier Science Ltd, New York, NY, 136:8-13, (2016).
Impact/Purpose:
The manuscript illustrates how the reproductive performance of the control medaka that are used in a MEOGRT influence statistical power, and therefore the successful implementation of the protocol. Example scenarios, based upon medaka reproduction data collected at MED, are discussed that bolster the recommendation that facilities planning to implement the MEOGRT should have a culture of medaka with high fecundity, low variance, and low rates of non-spawning days. Finally, an R-based software tool that is available to the public is introduced that allows the user to calculate the power properties of a MEOGRT either from collected or simulated data.
Description:
Because of various Congressional mandates to protect the environment from endocrine disrupting chemicals (EDCs), the United States Environmental Protection Agency (USEPA) initiated the Endocrine Disruptor Screening Program. In the context of this framework, the Office of Research and Development within the USEPA developed the Medaka Extended One Generation Reproduction Test (MEOGRT) to characterize the endocrine action of a suspected EDC. One important endpoint of the MEOGRT is fecundity of breeding pairs of medaka. Power analyses were conducted to determine the number of replicates needed in proposed test designs and to determine the effects that varying reproductive parameters (e.g. mean fecundity, variance, and days with no egg production) will have on the statistical power of the test. A software tool, the MEOGRT Reproduction Power Analysis Tool, was developed to expedite these power analyses by both calculating estimates of the needed reproductive parameters (e.g. population mean and variance) and performing the power analysis under user specified scenarios.
