Analysis of uncertainty due to calibration in clinical laboratory measurement processes

The calibration of the instrument used in the analytical stage of a clinical laboratory testing process has a significant contribution to the uncertainty of the final measurement result. In this paper, we present the development of a mathematical model of the analytical stage of the serum cholesterol laboratory test that quantifies and integrates the uncertainties of the various components of the laboratory measurement system. Monte Carlo simulation is used to estimate the net uncertainty of associated with the measurement system. The model is further used to quantify the effect of uncertainty in calibration parameters on both the location and spread of the distribution of the final laboratory measurement result. In addition, the effect of the choice of calibrator concentration levels on the uncertainty of the measurement is quantified using Monte Carlo simulation. The simulation is also used to identify the optimal calibrator concentration levels that minimize uncertainty at medical decision points as well as across the patient concentration level range. The Monte Carlo methodology of optimal calibrator selection presented in this paper can be extended to linear calibration measurement systems in general.