Lesson 8 (Optional): Do you want to learn more about QC planning tools? This lesson provides a more in-depth discussion and a more quantitative treatment of power function graphs, critical-error graphs, OPSpecs charts, and quality-planning models. If you aren't satisfied with just learning how to use QC planning tools, but want to understand where they came from and why they work, this lesson provides the materials and references to papers in the scientific literature.

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Objectives: Learn the origin of power curves.  Be able to read a power curve and assess the probabilities of false  rejection for commonly used QC procedure.  Be able to calculate the critical-size errors that are medically important.  Be able to read a critical-error graph and assess the probability of error  detection for critical-size errors?  Be able to read an OPSpecs chart.  Be able to use an OPSpecs chart to select appropriate QC procedures.  Understand the evolution of the total error model to the analytical and  clinical quality planning models used in QC planning.  Be able to describe the mathematical basis of the OPSpecs chart.  Web materials: Lesson: Power function graphs  Lesson: Critical-error graphs  FAQs about power function and critical-error graphs  Lesson: OPSpecs charts  FAQs about OPSpecs charts   Lesson: Quality-planning models  Things to do: Study the materials.  Run QC Validator tutorial B, Multitest analyser albumin example, and tutorial D.  For the cholesterol method in your laboratory, enter the appropriate parameters and display power function graphs, critical-error graphs, and OPspecs charts.  Assessment: View the Answers What Pfr would you expect for a 12s rule with N=2, N=3, and N=4?  What Pfr would you expect for a 13s rule with N=2, N=3, and N=4?  What is the critical-SE when TEa is10%, bias is 0.0, and the method CV is 2.13%?  What are the Peds of detecting the critical-SE above by comonly used control rules with N's of 2?  Display the OPSpecs chart for the example above.  Demonstrate how to select an appropriate QC procedure from the OPSpecs chart.  Does the critical-error graph also support that selection?  Use the QC Validator program to select an appropriate QC procedure for a  cholesterol test having a TEa of 10%, a DInt of 20%, within-subject variation of 6.5%, method CV of 3.0%, and method bias of 0.0%.  Return to the top  View the Participant Q & A

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