Several authors have documented the poor performance of usual large-sample, individual calibration confidence intervals based on a single run of an immunoassay. Inaccuracy of these intervals may be attributed to the paucity of information on model parameters available in a single run. Methods for combining information from multiple runs to estimate assay response variance parameters and to refine characterization of the standard curve for the current run via empirical Bayes techniques have been proposed. We investigate formally the utility of these techniques for improving the quality of routine individual calibration inference.