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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/4026

Title: Model averaging using fractional polynomials to estimate a safe level of exposure
Authors: Faes, Christel
Aerts, Marc
Geys, Helena
Molenberghs, Geert
Issue Date: 2007
Citation: RISK ANALYSIS, 27(1). p. 111-123
Abstract: Quantitative risk assessment involves the determination of a safe level of exposure. Recent techniques use the estimated dose-response curve to estimate such a safe dose level. Although such methods have attractive features, a low-dose extrapolation is highly dependent on the model choice. Fractional polynomials,((1)) basically being a set of (generalized) linear models, are a nice extension of classical polynomials, providing the necessary flexibility to estimate the dose-response curve. Typically, one selects the best-fitting model in this set of polynomials and proceeds as if no model selection were carried out. We show that model averaging using a set of fractional polynomials reduces bias and has better precision in estimating a safe level of exposure (say, the benchmark dose), as compared to an estimator from the selected best model. To estimate a lower limit of this benchmark dose, an approximation of the variance of the model-averaged estimator, as proposed by Burnham and Anderson,((2)) can be used. However, this is a conservative method, often resulting in unrealistically low safe doses. Therefore, a bootstrap-based method to more accurately estimate the variance of the model averaged parameter is proposed.
Notes: Hasselt Univ, Ctr Stat, Diepenbeek, Belgium. Johnson & Johnson, PRD Biometr & Clin Informat, Beerse, Belgium.FAES, C, Hasselt Univ, Ctr Stat, Agoralaan 1, Diepenbeek, Belgium.christel.faes@uhasselt.be
URI: http://hdl.handle.net/1942/4026
DOI: 10.1111/j.1539-6924.2006.00863.x
ISI #: 000244798100012
ISSN: 0272-4332
Category: A1
Type: Journal Contribution
Validation: ecoom, 2008
Appears in Collections: Research publications

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