This chapter explains the theory and practice of immunoassay calibration curve fitting techniques. The nature and characteristics of the immunoassay dose-response curve are discussed and the general principles involved in finding a suitable response transformation, including the importance of the response-error relationship. Curve-fitting methods are explained, starting with early approaches that are seldom used now. Regression and curve-fit metrics are discussed in detail, with extensive coverage of 4-parameter and 5-parameter logistic methods, which are most commonly used. The importance of calibrator positioning, understanding error profiles and the implications of outliers are all discussed and explained. There is also a section on commercial systems involving factory-set master curves and adjusters run in the user laboratory.

John R. Dunn, Ph.D. is the Chief Technical Officer at Brendan Technologies, Inc.
Dr. Dunn has been a researcher in the fields of tumor cell metabolism, endocrinology, radiology and pharmacology. He has extensive research and laboratory management experience from several industry positions, including setting up and running a high-volume reference laboratory specializing in immunochemistry. He also was a consultant for assay development and statistics for several laboratories, and served as a technical advisor for several diagnostic reagent manufacturers. Dr. Dunn is founder and head of the development team which manufactures StatLIA, data analysis and management software for the biopharmaceutical and research communities. He conducts training classes and consultations at biopharmaceutical, diagnostic and reagent manufacturers in the U.S. and Europe.

This chapter also contains a section on master curves and adjusters by David Wild taken from the previous edition. This section has been revised for the fourth edition.

Calibration, regression, curve-fit, model, goodness-of-fit, standard, standard curve, calibration curve, calibrator, dose, response, logit, weighting, least-squares, linear regression, 4-parameter logistic, 5-parameter logistic, cubic spline, error profile, precision profile, outlier, adjuster.