A Monte Carlo approach to the estimation & analysis of uncertainty in clinical laboratory measurement processes

Abstract

Clinical laboratory testing is a vital component of many stages of the medical decision making process, and therefore information about the quality of the measurement process is critical to the medical decision-making process. A statement of uncertainty of the result of a laboratory test provides this information. To obtain this information, the clinical laboratory measurement process is conceptualized as a self-contained system, the concept of process phases is introduced, and a broadly applicable algorithm describing the modeling and estimation of uncertainty of such processes is developed. The article discusses how performance specifications for individual components can be used to characterize their uncertainty, and uses Monte Carlo simulation to integrate these individual component uncertainties into a net system uncertainty. The proposed approach is illustrated by developing a mathematical model of the serum cholesterol assay analysis procedure. The uses of the model are to: 1) simulate, evaluate and optimize quality control policies without resorting to conducting controlled experiments, 2) obtain performance targets for the measurement process by using uncertainty estimates from the simulation, 3) estimate the contribution of each source of uncertainty to the net system uncertainty, and 4) study the effects of varying the parameters of the system on the net system uncertainty are illustrated with examples.