Model averaging using fractional polynomials to estimate a safe level of exposure.
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, 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, 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.
Faes
Christel
C
Center for Statistics, Hasselt University, Diepenbeek, Belgium. christel.faes@uhasselt.be
Aerts
Marc
M
Geys
Helena
H
Molenberghs
Geert
G
D000818:Animals; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D004781:Environmental Exposure; D019855:Ethylene Glycol / Q000494:pharmacology; D005260:Female; D016014:Linear Models; D018811:Maternal Exposure; D051379:Mice; D015233:Models, Statistical; D008962:Models, Theoretical; D015999:Multivariate Analysis; D019055:No-Observed-Adverse-Effect Level; D011247:Pregnancy; D011336:Probability; D018570:Risk Assessment
D019855:Ethylene Glycol