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Principles of Curve Fitting for Multiplex Sandwich Immunoassays, Rev B

ting the spiked samples as controls and specifying the concentration of each sample in the Enter Controls Info dialog box of the protocol settings. The recovery values are displayed in the report table in the (Obs/Exp) * 100 column (Figure 5). The disadvantage of this method is that it is affected by variables other than curve fitting. Errors in sample preparation or assay preparation (pipetting, adding reagents) may affect overall recovery. In addition, it is difficult to accurately spike low levels of cytokines into samples due to the relative imprecision of pipets that deliver small volumes. All of these variables should be taken into consideration when analyzing spiked recovery data. For example, if standards recovery is accurate, but spiked recovery is poor, the error is most likely due to the spiked samples themselves. This method should be performed in addition to, and not in place of, backcalculation of standards when evaluating assay performance.

Linear vs. Logistic Regression
Linear and logistic regression methods have distinct advantages and disadvantages. Linear regression may be readily used when analyzing serum or plasma cytokine levels. The biological range of most cytokines in serum is within the linear range of the standard curve and thus linear regression is appropriate for analysis. Some samples may need to be diluted and reanalyzed if the result is above the range covered by the standard curve. Although linear regression requires fewer data points or standards (as few as 3) compared to logistic regression (4PL and 5PL require 6 data points), a more accurate fit is obtained by using at least 6 points for any of the regression types (Motulsky 1996). Linear regressi
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