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Principles of Curve Fitting for Multiplex Sandwich Immunoassays, Rev B

gression (R2 = 0.9996), while the green bars indicate the range of concentrations showing 70130% recovery using logistic regression. The dynamic range using linear regression is narrower than that achieved using logistic regression. From a practical perspective, logistic regression is much more flexible with respect to the standard concentrations used in a multiplex setting. A range of 1.9532,000 pg/ml is appropriate for all analytes in a Bio-Plex cytokine assay panel, which facilitates analysis of the data compared to linear regression. It should be noted that although one may transform both the signal and the concentration to yield a linear plot, linear transformation of data is less accurate (Baud 1993). This inaccuracy is due to distortion of the experimental error and alteration of the relationship between x and y (Baud 1993).


Summary
Linear and logistic regressions are the two most commonly used curve-fitting models for sandwich immunoassays. Although linear regression may be useful when analyzing samples that fall within the linear portion of the response curve, logistic regression is the preferred regression type for multiplex immunoassays. The logistic regression yields the broadest range of concentrations at which unknown samples may be accurately predicted, and it allows the selection of a single set of standards that may be simultaneously applied to multiple analytes such as cytokines. The 5PL regression, with a fifth parameter to accommodate curve asymmetry, yields the best results in most cases. Assessment of the quality of curve-fitting routines is best achieved using two methods, standards recovery and spiked recovery. Both method
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