Impact of assay stability on the false negative and false positive rates in quality control

•Risk-based methods for QC offer advantages over traditional approaches.•We developed a dynamic model for analysis of risk-based QC.•The dynamic model requires users to estimate input parameters.•The model is sensitive to assumptions regarding the input parameters. The dynamic Precision QC (PQC) model can be used to evaluate the performance of quality control (QC) monitoring systems. The model depends on inputs that describe the intrinsic shift behavior (i.e., stability) of an assay. The output of the model is a trade-off curve that shows the relationship between false negative (FN) and false positive (FP) risk events. The relationship between the inputs and outputs of this model has not yet been explored. We used Monte Carlo simulation to generate trade-off curves using the PQC. We varied the input parameters that determine assay stability (shift probability and shift size distribution) and studied the impact of these inputs on the output (i.e., the trade-off curve relating FN risk to FP risk). FN risk is sensitive to the shift probability and the width of the control limits. FN risk is sensitive to the shape of the shift size distribution when the standard deviation (SD) of the shift size distribution is relatively narrow (i.e., SD 2). Practical use of the PQC model may require the estimation of the shift probability and shift size distribution.