Risk Analysis for Quality Control Part 1: The Impact of Transition Assumptions in the Parvin Model. (4th January 2023) - Schmidt, Robert L; Moore, Ryleigh A; Walker, Brandon S; Rudolf, Joseph W
Abstract: Background: Setting quality control (QC) limits involves balancing the risk of false-positive results and false-negative results. Recent approaches to QC have focused on the assessment of false-negative results. The Parvin model is the most-used model for risk analysis. The Parvin model assumes that the system makes a transition from an in-control to an out-of-control (OOC) state but makes no further transitions after moving to the OOC state. The implications of this assumption are unclear. Methods: We used simulation experiments to compare the performance of QC systems based on no OOC transitions allowed (NOOCTA) vs systems where OOC transitions were allowed (OOCTA). Results: The NOOCTA assumption leads to paradoxical tradeoff curves between false-positive results and false-negative results. Predictions of a false-negative result based on NOOCTA were about 10 times lower than models based on OOCTA. Conclusions: The most common models for QC risk analysis underestimate false-negative results. There is a need to develop better risk-based methods for QC analysis.