Multiplicity-adjusted Inferences in Risk Assessment: Benchmark Analysis with Continuous Response Data |
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Authors: | Yuping Wu Walter W. Piegorsch R. Webster West Dengfang Tang Maureen O. Petkewich Wei Pan |
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Affiliation: | (1) Department of Statistics, University of South Carolina, Columbia, SC 29208, USA;(2) South Carolina Cancer Center, Columbia, SC 29203, USA;(3) Baltimore Research and Education Foundation, Perry Point, MD 21902, USA;(4) Center for Coastal Environmental Health and Biomolecular Research, U.S. National Ocean Service, Charleston, SC 29412, USA |
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Abstract: | We develop and study multiplicity adjustments for low-dose inferences in environmental risk assessment. Application is intended for risk analysis studies where human, animal, or ecological data are used to set safe levels of a hazardous environmental agent. A modern method for making inferences in this setting is known as benchmark analysis, where attention centers on the dose at which a fixed benchmark level of risk is achieved. Both upper confidence limits on the risk and lower confidence limits on the “benchmark dose” are of interest. In practice, a number of possible benchmark risks may be under study; if so, corrections must be applied to adjust the limits for multiplicity. In this note, we discuss approaches for doing so with continuous, nonquantal response data. |
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Keywords: | Benchmark dose Environmental risk analysis Nonquantal dose response Quantitative risk assessment Simultaneous inferences |
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