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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8435

Title: Model averaging in microbial risk assessment using fractional polynomials
Authors: NAMATA, Harriet
FAES, Christel
Teunis, P.
Issue Date: 2008
Citation: RISK ANALYSIS, 28(4). p. 891-905
Abstract: The alleviation of food-borne diseases caused by microbial pathogen remains a great concern in order to ensure the well-being of the general public. The relation between the ingested dose of organisms and the associated infection risk can be studied using dose-response models. Traditionally, a model selected according to a goodness-of-fit criterion has been used for making inferences. In this article, we propose a modified set of fractional polynomials as competitive dose-response models in risk assessment. The article not only shows instances where it is not obvious to single out one best model but also illustrates that model averaging can best circumvent this dilemma. The set of candidate models is chosen based on biological plausibility and rationale and the risk at a dose common to all these models estimated using the selected models and by averaging over all models using Akaike's weights. In addition to including parameter estimation inaccuracy, like in the case of a single selected model, model averaging accounts for the uncertainty arising from other competitive models. This leads to a better and more honest estimation of standard errors and construction of confidence intervals for risk estimates. The approach is illustrated for risk estimation at low dose levels based on Salmonella typhi and Campylobacter jejuni data sets in humans. Simulation studies indicate that model averaging has reduced bias, better precision, and also attains coverage probabilities that are closer to the 95% nominal level compared to best-fitting models according to Akaike information criterion.
Notes: Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. Natl Inst Publ Hlth & Environm, Dept IMA, NL-3720 BA Bilthoven, Netherlands.
URI: http://hdl.handle.net/1942/8435
ISI #: 000258078200008
ISSN: 0272-4332
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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