Quality control limits: Are we setting them too wide?

Background: Quality control charts (Levey Jennings Charts) are based on estimates of variation. There are two general approaches for estimating variation: those based on short-term variation and those based on long-term variation. We have observed that clinical laboratory science (CLS) tends to estimate variation using long-term variation but that most other fields use short-term variation. The objective of this study is to compare these two methods of estimating process variation, compare the accuracy of control limits generated by each method, and explore whether it would be useful for clinical laboratories to adopt methods used in other fields.
Methods: We conducted a literature review to compare recommendations for methods for estimation of variation in CLS with other fields. We searched textbooks for suggested methods and also searched the primary literature for references to methods associated with short-term and long-term variation. We provide theoretical results from statistics to show that, in theory, short-term estimates can differ from long-term estimates of variation. We used simulation studies to show that one can construct examples where short-term and long-term estimates of variation lead to significant differences in control limits. Finally, we show laboratory data comparing short-term and long-term estimates of variation.
Results: We found that practice in CLS differs from other fields. We found no references to methods based on short-term variation in CLS textbooks and only one reference in the primary literature. In contrast, standard quality control (QC) texts recommend methods based on short-term variation and the primary literature makes frequent reference to such methods. We found statistical papers that show that, in theory, estimates based on long-term variation can produce inflated estimates of process variation. We used simulation to show that such examples can be constructed. We examined 95 QC charts and found that in 93 cases, there were significant differences between short-term and long-term estimates of variation. The ratio of long-term to short-term variation was greater than 1.5 in 18% of cases.
Conclusion: Estimates of variation based on short-term and long-term variation can lead to significant differences in estimates. Estimates based on long-term variation are frequently larger than estimates based on short-term variation.
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