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Biphasic, non-sigmoidal dose-response relationships are frequently observedin biochemistry and pharmacology, but they are not always analyzed withappropriate statistical methods. Here, we examine curve fitting methods for"hormetic" dose-response relationships where low and high doses of an effectorproduce opposite responses. We provide the full dataset used for modeling, andwe provide the code for analyzing the dataset in SAS using two establishedmathematical models of hormesis, the Brain-Cousens model and the Cedergreenmodel. We show how to obtain and interpret curve parameters such as the ED50that arise from modeling, and we discuss how curve parameters might change in apredictable manner when the conditions of the dose-response assay are altered.In addition to modeling the raw dataset that we provide, we also model thedataset after applying common normalization techniques, and we indicate howthis affects the parameters that are associated with the fit curves. TheBrain-Cousens and Cedergreen models that we used for curve fitting weresimilarly effective at capturing quantitative information about the biphasicdose-response relationships.

Modeling Biphasic, Non-Sigmoidal Dose-Response Relationships: Comparison of Brain-Cousens and Cedergreen Models for a Biochemical Dataset