  Biphasic, non-sigmoidal dose-response relationships are frequently observed
in biochemistry and pharmacology, but they are not always analyzed with
appropriate statistical methods. Here, we examine curve fitting methods for
"hormetic" dose-response relationships where low and high doses of an effector
produce opposite responses. We provide the full dataset used for modeling, and
we provide the code for analyzing the dataset in SAS using two established
mathematical models of hormesis, the Brain-Cousens model and the Cedergreen
model. We show how to obtain and interpret curve parameters such as the ED50
that arise from modeling, and we discuss how curve parameters might change in a
predictable manner when the conditions of the dose-response assay are altered.
In addition to modeling the raw dataset that we provide, we also model the
dataset after applying common normalization techniques, and we indicate how
this affects the parameters that are associated with the fit curves. The
Brain-Cousens and Cedergreen models that we used for curve fitting were
similarly effective at capturing quantitative information about the biphasic
dose-response relationships.
