Combining Biomarker Calibration Data to Reduce Measurement Error.

Biomarker assay measurement often consists of a two-stage process where laboratory equipment yields a relative measure which is subsequently transformed to the unit of interest using a calibration curve. The calibration curve establishes the relation between the measured relative units and sample biomarker concentrations using stepped samples of known biomarker concentrations. Samples from epidemiologic studies are often measured in multiple batches or plates, each with independent calibration experiments. Collapsing calibration information across batches before statistical analysis has been shown to reduce measurement error and improves estimation. Additionally, collapsing in practice can also create an additional layer of quality control (QC) and optimization in a part of the laboratory measurement process that is often highly automated. Principled recalibration is demonstrated via. a three-step process of identifying batches where recalibration might be beneficial, forming a collapsed calibration curve and recalibrating identified batches, and using QC data to assess the appropriateness of recalibration. Here, we use inhibin B measured in biospecimens from the BioCycle study using 50 enzyme-linked immunosorbent assay (ELISA) batches (3875 samples) to motivate and display the benefits of collapsing calibration experiments, such as detecting and overcoming faulty calibration experiments, and thus improving assay coefficients of variation from reducing unwanted measurement error variability. Differences in the analysis of inhibin B by testosterone quartile are also demonstrated before and after recalibration. These simple and practical procedures are minor adjustments implemented by study personnel without altering laboratory protocols which could have positive estimation and cost-saving implications especially for population-based studies.