APPROXIMATE CONFIDENCE-INTERVALS IN CALIBRATION USING THE BOOTSTRAP

The goal of calibration is to estimate a sample's concentration and th e error associated with that estimate when only its signal response is known. Simulations were run to test the accuracy and precision of var ious parametric confidence intervals to confidence intervals generated by the bias-corrected, nonparametric bootstrap approach. None of the methods studied reached their asymptotic coverage probability, althoug h the exact parametric confidence interval came the closest. The range s of exact parametric confidence intervals were significantly larger t han the other methods. Approximate parametric confidence intervals and bootstrap confidence intervals were both dependent on the number of r eplicates analyzed and on the coefficient of variation of the assay. W hen only a single replicate was available for analysis, the bootstrap method was dismal in containing the true sample concentration. As the number of replicates increases, the width of the bootstrap confidence interval converged to the exact parametric confidence interval, wherea s the approximate parametric confidence interval width increased and b egan to diverge from the exact parametric confidence interval. Bootstr ap confidence intervals produce maximal coverage with minimal range wh en 2-4 replicate samples are available for analysis.