We developed a computer model to study the use of patients' specimens to assess compliance of cholesterol measurement performance with the 1992 goals of the Laboratory Standardization Panel of the National Cholesterol Education Program. The model uses Monte Carlo techniques to simulate cholesterol measurements that are subject to both systematic and random error. Split- sample measurements by a clinical laboratory and by a reference laboratory are compared by using linear regression to estimate clinical laboratory bias and imprecision; subsequently, according to specified decision limits, the performance of the clinical laboratory is classified as acceptable or deficient. We have quantified the influence of the bias and imprecision of the clinical laboratory, the imprecision of the reference laboratory, the number of split samples compared, and the decision limits on the accuracy of the classification of clinical laboratory performance. Unless the decision limits are carefully selected and a sufficient number of split samples are used, clinical laboratory performance will be frequently misclassified.
- Monte Carlo simulation
- variation, source of