Influences of Between- and Within-Run Components on the Mean Rule
Author Information
Author(s): Pierre Douville, George S. Cembrowski, Jerome F. Strauss
Primary Institution: Wm. Pepper Laboratory, Department of Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania
Hypothesis
The study investigates how the between-run and within-run components of variation affect the performance of the mean rule in quality control.
Conclusion
The optimal detection of systematic errors is challenging in the presence of significant between-run variation.
Supporting Evidence
- The study found that significant between-run variation can lead to increased false rejection rates.
- An alternative model was proposed that better reflects laboratory practices and improves error detection.
- The mean rule is a simple method that can be optimized for better performance in detecting systematic errors.
Takeaway
This study looks at how different types of errors in lab tests can make it harder to spot problems, and suggests a simple way to improve error detection.
Methodology
The study used computer simulations to analyze the performance of the mean rule under varying conditions of systematic and random errors.
Limitations
The study may not account for all real-world complexities in laboratory settings.
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