Abstract
A predictive value model has been developed to describe the usefulness of results from quality control tests or procedures. The model shows that the critical parameters are the probability for false rejection, probability for error detection, and prevalence or frequency of occurrence of analytical errors. When prevalence is low, control procedures should have a low probability for false rejection. When prevalence is high, control procedures should have a high probability for error detection. The predictive value model for a quality control (QC) test is analogous to the predictive value model for a diagnostic test, thus suggesting new strategies for optimizing the performance of QC tests.
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