BOOTSTRAP-ADJUSTED CALIBRATION CONFIDENCE-INTERVALS FOR IMMUNOASSAY

In immunoassay, a nonlinear heteroscedastic regression model is used t o characterize assay concentration-response, and the model fitted to d ata from standard samples is used to calibrate unknown test samples. U sual large-sample methods to construct individual confidence intervals for calibrated concentrations have been observed in empirical studies to be seriously inaccurate in terms of achieving the nominal level of coverage. We show theoretically that this inaccuracy is due largely t o estimation of parameters characterizing assay response variance. By exploiting the theory, we propose a bootstrap procedure to adjust the usual intervals to achieve a higher degree of accuracy. We provide bot h theoretical results and simulation evidence to show that the propose d method attains the nominal level. A practical advantage of the proce dure is that it may be implemented reliably using far fewer bootstrap samples than are needed in other resampling schemes.