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Clinical Chemistry 44: 116-123, 1998;
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(Clinical Chemistry. 1998;44:116-123.)
© 1998 American Association for Clinical Chemistry, Inc.

Automation and Analytical Techniques

Statistically accurate estimation of hormone concentrations and associated uncertainties: methodology, validation, and applications

Martin Straumea, Michael L. Johnson1, and Johannes D. Veldhuis

Department of Internal Medicine, Division of Endocrinology and Metabolism, National Science Foundation Center for Biological Timing, Gilmer Hall, University of Virginia, Charlottesville, VA 22903.
1 Department of Pharmacology, University of Virginia Health Sciences Center, Charlottesville, VA 22908.
a Author for correspondence. Fax 804-982-4505; e-mail ms3g{at}virginia.edu.

We describe a data reduction procedure to assign statistically accurate estimates of unknown hormone concentrations, with associated uncertainties, based on experimental uncertainties in sample replicates and the fitted calibration curve. Three mathematical calibration curve functions are considered. The one providing optimal statistical characterization of reference calibrators is chosen for unknown evaluation. Experimental error is addressed by assigning and propagating uncertainty estimates for each measured response (including zero-dose responses) by an empirically determined discrete uncertainty profile and by propagating calibration curve uncertainty. Discrete uncertainty profiles account for both response precision (replicability) and accuracy (deviation from predicted calibration curves) without relying on assumed theoretical response variance–assay response relations. The validity of assigning variable response weighting by this procedure was assessed by Monte Carlo simulations based on chemiluminescence growth hormone calibration curves. Much-improved accuracy and estimated precision are achieved for unknown hormone concentrations, particularly extremely low concentrations, by using this variable response weighting procedure.




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