Assessment of average of normals (AON) procedure for outlier-free datasets including qualitative values below limit of detection (LoD): an application within tumor markers such as CA 15-3, CA 125, and CA 19-9.

Average of normals (AON) is a quality control procedure that is sensitive only to systematic errors that can occur in an analytical process in which patient test results are used. The aim of this study was to develop an alternative model in order to apply the AON quality control procedure to datasets that include qualitative values below limit of detection (LoD). The reported patient test results for tumor markers, such as CA 15-3, CA 125, and CA 19-9, analyzed by two instruments, were retrieved from the information system over a period of 5 months, using the calibrator and control materials with the same lot numbers. The median as a measure of central tendency and the median absolute deviation (MAD) as a measure of dispersion were used for the complementary model of AON quality control procedure. The ubias values, which were determined for the bias component of the measurement uncertainty, were partially linked to the percentages of the daily median values of the test results that fall within the control limits. The results for these tumor markers, in which lower limits of reference intervals are not medically important for clinical diagnosis and management, showed that the AON quality control procedure, using the MAD around the median, can be applied for datasets including qualitative values below LoD.