OPTIMAL PROCEDURES FOR DETECTING ANALYTIC BIAS USING PATIENT SAMPLES

We recently described the performance characteristics of the exponenti ally adjusted moving mean (EAMM), a patient-data, moving block mean pr ocedure, which is a generalized algorithm that unifies Bull's algorith m and the classic average of normals (AON) procedure. Herein we descri be the trend EAMM (TEAMM), a continuous signal analog of the EAMM proc edure related to classic trend analysis. Using computer simulation, we have compared EAMM and TEAMM over a range of biases for various sampl e sizes (N or equivalent smoothing factor alpha) and exponential param eters (P) under conditions of equivalent false rejection (fixed on a p er patient sample basis). We found optimal pairs of N and P for each l evel of bias by determination of minimum mean patient samples to rejec tion. Overall optimal algorithms were determined through calculation o f undetected lost medical utility (ULMU), a novel function that quanti fies the medical damage due to analytic bias. The ULMU function was ca lculated based on lost test specificity in a normal population, We fou nd that optimized TEAMM was superior to optimized EAMM for all levels of analytic bias. If these observations hold true for non-Gaussian pop ulations, TEAMM procedures are the method of choice for detecting bias using patient samples or as an event gauge to trigger use of known-va lue control materials.