Model Uncertainty and Model Averaging in the Estimation of Infectious Doses for Microbial Pathogens

• Activity Number: 615
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Hojin Moon*+ and Steven Kim and James J. Chen and Ralph L Kodell
• Companies: California State University Long Beach and University of California at Irvine and FDA and University of Arkansas for Medical Sciences
• Keywords: bootstrap ; 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 or illness. 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, where data uncertainty is addressed by a bootstrap method. To evaluate the actual coverage probabilities of the lower confidence limit, a Mont