| Summary: | Methods for estimating the qPCR amplification efficiency <i>E</i> from data for single reactions are tested on six multireplicate datasets, with emphasis on their performance as a function of the range of cycles <i>n</i><sub>1</sub>–<i>n</i><sub>2</sub> included in the analysis. The two-parameter exponential growth (EG) model that has been relied upon almost exclusively does not allow for the decline of <i>E</i>(<i>n</i>) with increasing cycle number <i>n</i> through the growth region and accordingly gives low-biased estimates. Further, the standard procedure of “baselining”—separately estimating and subtracting a baseline before analysis—leads to reduced precision. The three-parameter logistic model (LRE) does allow for such decline and includes a parameter <i>E</i><sub>0</sub> that represents <i>E</i> through the baseline region. Several four-parameter extensions of this model that accommodate some asymmetry in the growth profiles but still retain the significance of <i>E</i><sub>0</sub> are tested against the LRE and EG models. The recursion method of Carr and Moore also describes a declining <i>E</i>(<i>n</i>) but tacitly assumes <i>E</i><sub>0</sub> = 2 in the baseline region. Two modifications that permit varying <i>E</i><sub>0</sub> are tested, as well as a recursion method that directly fits <i>E</i>(<i>n</i>) to a sigmoidal function. All but the last of these can give <i>E</i><sub>0</sub> estimates that agree fairly well with calibration-based estimates but perform best when the calculations are extended to only about one cycle below the first-derivative maximum (FDM). The LRE model performs as well as any of the four-parameter forms and is easier to use. Its proper implementation requires fitting to it plus a suitable baseline function, which typically requires four–six adjustable parameters in a nonlinear least-squares fit.
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