EXPONENTIALLY ADJUSTED MOVING MEAN PROCEDURE FOR QUALITY-CONTROL - ANOPTIMIZED PATIENT SAMPLE CONTROL PROCEDURE

The idea of using patient samples as the basis for control procedures elicits a continuing fascination among laboratorians, particularly in the current environment of cost restriction. Average of normals (AON) procedures, although little used, have been carefully investigated at the theoretical level, The performance characteristics of Bull's algor ithm have not been thoroughly delineated, however, despite its widespr ead use. The authors have generalized Bull's algorithm to use variably sized batches of patient samples and a range of exponential factors, For any given hatch size, there is an optimal exponential factor to ma ximize the overall power of error detection, The optimized exponential ly adjusted moving mean (EAMM) procedure, a variant of AON and Bull's algorithm, outperforms both parent procedures. As with any AON procedu re, EAMM is most useful when the ratio of population variability to an alytical variability (standard deviation ratio, SDR) is low.