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esponse curve across all hardness levels vs. models with a separate dose-response curveby hardness level. The majority of these tables indicate that there is strong evidence thatthe dose-response curve varies by hardness. Next consider the model averaged estimatedLCxx/ICxx values for each hardness level. Even if there is strong support for an effect ofhardness, the effect of hardness may be small enough that a common LCxx/ICxx valueacross the hardness levels may be appropriate. This is not contradictory as the modelselection in Table 2 is comparing the models over the entire-dose response curve, whilethe estimates in Table 4 are for specific points on the dose-response curve.6. Discussion As outlined by Wheeler and Bailer (2005), model averaging provides a way toincorporate model uncertainty into the risk assessment process. Simply selecting thesingle “best” model may give a false sense of precision (i.e. single model reportedstandard errors typically underreport the true uncertainty in the BMD).Model averaging is not a panacea. Estimates of BMDs within the observed range of thedoses in a study will typically be very similar across a wide range of models as all of themodels must come “close” to the observed data. However, extrapolations that are faroutside the observed dose ranges of the data will typically be very sensitive to the choiceof models.It would be possible to extend the above modeling approach by incorporating both theeffects of hardness and sulphate upon the observed responses and deriving a single doseresponsecurve that incorporates both hardness and sulphate levels. The advantage of thismore complex approach is that a (model averaged) prediction equation for the BMD canbe established as a function of any hardness rather than relying on the observedhardnesses in the study. Unfortunately, in most cases, only a few levels of hardness werestudies and so the models for the effect of hardness must be very simple (e.g. linear) andextrapolations outside the observed ranges of hardness will be unwise.ReferencesAnderson, D. (2008) Model Based Inference in the Life Sciences: A Primer on Evidence.Springer: New York.Bailer AJ, Wheeler M, Dankovick D, Noble R, Bena J (2005) Incorporating uncertaintyand variability in the assessment of occupational hazards. Int J Risk Assess Manage5:344–357.Bailer, Noble, and Wheeler (2005) Model uncertainty and risk estimation for