Quantile Benchmark Dose Estimation for Continuous Endpoints

• Activity Number: 156
• Type: Topic Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Matthew Wheeler*+ and A. John Bailer and Kan Shao
• Companies: NIOSH and Miami University and U.S. Environmental Protection Agency
• Keywords: monotonic dose-response ; heterogeneous variance ; quantitative risk assessment ; smoothing splines

Abstract

Quantitative risk assessment (QRA) characterizes the risk of an adverse health outcome for an organism exposed to a chemical, environmental, physical or other hazard. Historically QRAs define risk based upon the increased probability of adverse response due to exposure when compared to the background probability of response. For a specified risk level, these probability statements are inverted to find an exposure (or dose) for minimizing risk often called the benchmark dose (BMD). For continuous endpoints BMD analyses have employed strong assumptions on the form of the response distribution which may not be met for most toxicology data sets. We propose a reformulation of the BMD for use in QRA for continuous response that is based upon milder assumptions using quantile regression and monotone smoothing splines. This method takes into account the uncertainty in the response distribution as well as uncertainty in the dose-response relationship. We apply this method to multiple data sets and show through a simulation study that the approach is often superior to current practice when the response distribution is not known and/or the dose-response is unknown.