Setting performance goals and evaluating total analytical error for diagnostic assays.

Abstract

BACKGROUND: Total analytical error has been a useful metric both to assess laboratory assay quality and to set goals. It is often estimated by combining imprecision (SD) and average bias in the equation: total analytical error = bias + 1.65 x imprecision. This indirect estimation model (referred to as the simple combination model) leads to different estimates of total analytical error than that of a direct estimation method (referred to as the distribution-of-differences method) or of simulation. METHODS: A review of the literature was undertaken to reconcile the different estimation approaches. RESULTS: The simple combination model can underestimate total analytical error by neglecting random interference bias and by not properly treating other error sources such as linear drift and outliers. A simulation method to estimate total analytical error is outlined, based on the estimation and combination of total analytical error source distributions. Goals for each total analytical error source can be established by allocation of the total analytical error goal. Typically, the allocation is cost-based and uses the probability of combinations of error sources. The distribution-of-differences method, simple combination model, and simulation method to evaluate total analytical error are compared. Outlier results can profoundly influence quality, but their rates are seldom reported. CONCLUSIONS: Total analytical error should be estimated either directly by the distribution-of-differences method or by simulation. A systems engineering approach that uses allocation of the total analytical error goal into error source goals provides a cost-effective approach to meeting total analytical error. Because outliers can cause serious laboratory error, the inclusion of outlier rate estimates from large studies (e.g., those conducted by manufacturers) would be helpful in assessing assay quality.