10.1093/gao/9781884446054.013.7002292402 (Inactive DOIs, 2018)
Background Multiplexed assays, which simultaneously measure many analytes from a single sample, have become increasingly significant for laboratory diagnosis. Many multiplexed assays with application to clinical diagnosis consist of analyte measurements that can fail individually. In these cases, traditional statistical quantitative quality control (QC) measures cannot be used without creating an unacceptably high false rejection rate. Methods We developed stochastic simulation software (qcsim) to calculate and visualize the detection power of complex QC rule combinations, including traditional Westgard rules as well as statistical tests of multiple QC repeats, with arbitrary degrees of multiplexing and levels of control. We used this approach to evaluate novel QC models that maintain stringent control of the bias and imprecision of each analyte in a highly multiplexed assay ("panel"). For classifier-based assays that use an algorithm to generate a small number of diagnostic outcomes from a large number of analytes (a "pattern"), we use perturbation analysis to assess the effect of different classifiers with a single analytical platform. Results Multiple QC approaches are able to overcome the challenge of highly multiplexed assays, and we demonstrate successful strategies that control the false rejection rate using either high analytical performance (low imprecision) or multiple QC replicates. We also demonstrate that, for pattern-based assays, algorithmic details of the specific classifier determine both critical analytes and the required stringency of the QC design. Conclusions These results demonstrate multiple QC strategies that control highly multiplexed assays (1000-plex) at a level that is comparable to traditional QC schemes for single-analyte assays.