Risk Analysis for Quality Control Part 2: Theoretical Foundations for Risk Analysis.

Abstract

Risk analysis can be used to determine control limits for quality control (QC). The Parvin model is the most commonly used method for risk analysis; however; the Parvin model rests on assumptions that have been shown to produce paradoxical results and to underestimate risk. There is a need for an improved framework for risk analysis. We developed a dynamic model (Markov Reward Model) to analyze the long-term behavior of an assay under the influence of a QC monitoring system. The model is flexible and accounts for different patterns of assay behavior (shift frequency, shift distribution) and the impact of error on patient outcomes. The model determines the distribution of undetected reported errors and the frequency of false-positive laboratory results as a function of QC settings. The model accounts for the competing risks (false detections, shifts in the mean) that cause an assay to move from an in-control state to an out-of-control state. The model provides a tradeoff curve that expresses the cost to prevent an unacceptable reported result in terms of laboratory cost (false-positive QC). The model can be used to optimize settings of a particular QC method or to compare the performance of different methods. We developed a method to evaluate that determines the cost to reduce the risk to patients (reported results with unacceptable errors) in terms of laboratory costs (false-positive QC).