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The multirules system for optimizing assay error detection originally developed by James Westgard and others is now widely used in clinical pathology laboratories (1). The power of various combinations of rules to detect changes in assay performance can be assessed by use of power function charts (2), some of which are freely available on the Westgard QC website (3). In this report I reassess the power of error detection of rules that require data from more than one quality-control (QC) run.Variables that can be adjusted in setting a QC protocol, and modeled in power function charts, include the choice of rules, the number of QC samples in a run (n), and the number of runs over which data are to be assessed (R). In the development of power function charts, other variables must also be clearly defined so that the charts represent the actual practice in the laboratory.Westgard’s rules can be divided into within-run rules, where the data for decision-making are available from within one QC run, and cross-run rules, where data from more than one QC run are required (R >1). Using approved terminology (4), examples of within-run rules for n = 2 include 13s and 22s, and examples of cross-run rules for n = 2 include 41s and 10x. Within-run QC rules are clearly preferable to cross-run rules because they will allow detection of changes in assay performance as soon as possible after the change. Cross-run rules are commonly added to within-run rules with the aim of gaining additional power of error detection to meet quality specifications.In this report I use a spreadsheet application to develop power function charts with the aim of assessing the power of error detection of combinations of rules when cross-run rules are included. Specifically, I …

Reevaluation of the Power of Error Detection of Westgard Multirules