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Abstract:
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Drug discovery scientists routinely develop and use in-vitro assays; for example, to identify "hits," or to quantify the efficacious concentrations of compounds in a lead series. New-and-improved assays are constantly developed to replace the old ones as the new assays may be cheaper, faster, or easier to use. An old assay cannot be replaced until the new one is determined to be equivalent to it. It is useful to know for which concentrations the assays are equivalent. Orthogonal regression (a.k.a., total least squares) is a standard statistical tool used to compare two assays across a range of concentrations. The talk will extend this approach to cases where (1) there are enough replicate measurements to identify the full set of model parameters and (2) the variability of the two assays may differ.
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