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Updated: Aug 15, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Risk Analysis for Quality Control Part 1: The Impact of Transition Assumptions in the Parvin Model
Robert L Schmidt1,2, Ryleigh A Moore3, Brandon S Walker2
1Department of Pathology, University of Utah, Salt Lake City, UT, USA.
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.
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