Model and Data Uncertainty in Model Averaging in Dose Response Assessment

• Activity Number: 72
• Type: Topic Contributed
• Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Hojin Moon*+ and Steven Kim and James Chen and Ralph Kodell
• Companies: California State University, Long Beach and University of California, Irvine and National Center for Toxicological Research, FDA and University of Arkansas for Medical Sciences
• Keywords: Bias-skewness correction ; confidence limit ; data uncertainty ; food safety ; Kullback information criterion

Abstract

Food-borne infection is caused by intake of foods or beverages contaminated with microbial pathogens. Dose-response modeling is used to estimate exposure levels of pathogens associated with specific risks of infection. When a single dose-response model is used and confidence limits on infectious doses are calculated, only data uncertainty is captured. We propose a method to estimate the lower confidence limit on an infectious dose by including model uncertainty and separating it from data uncertainty. The infectious dose is estimated by a weighted average of effective dose estimates from a set of dose-response models via a Kullback information criterion. The confidence interval for the infectious dose is constructed by the delta method with a bootstrap method. To evaluate the actual coverage probabilities of the lower confidence limit, a Monte Carlo simulation study is conducted under sublinear, linear, and superlinear dose-response shapes that can be commonly found in real data sets. Our method achieves coverage close to nominal in almost all cases, thus providing a useful and efficient tool for accurate calculation of lower confidence limits on infectious doses.