Use of Coefficient of Variation in Assessing Variability of Quantitative Assays

By G. Reed, F. Lynn, B. Meade. Clinical and Vaccine Immunology, 2002

752 citations recorded by the source corpus.

ABSTRACT We have derived the mathematical relationship between the coefficient of variation associated with repeated measurements from quantitative assays and the expected fraction of pairs of those measurements that differ by at least some given factor, i.e., the expected frequency of disparate results that are due to assay variability rather than true differences. Knowledge of this frequency helps determine what magnitudes of differences can be expected by chance alone when the particular coefficient of variation is in effect. This frequency is an operational index of variability in the sense that it indicates the probability of observing a particular disparity between two measurements under the assumption that they measure the same quantity. Thus the frequency or probability becomes the basis for assessing if an assay is sufficiently precise. This assessment also provides a standard for determining if two assay results for the same subject, separated by an intervention such as vaccination or infection, differ by more than expected from the variation of the assay, thus indicating an intervention effect. Data from an international collaborative study are used to illustrate the application of this proposed interpretation of the coefficient of variation, and they also provide support for the assumptions used in the mathematical derivation.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4a453ca6568a854a778ccc1c5a410217404524ec

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Citations in the corpus

Monitoring the Coefficient of Variation Using Control Charts with Run Rules (2013, neutral): “[27].”
Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart (2017, background): “Monitoring the CV is also important in chemical and biological assay quality control to validate results (Reed et al. (2002), Seyler et al. (2005)).”
Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training (2021, methodology): “We normalize each technique’s bias values before Levene’s test to eliminate the impact of the average value [5, 28, 77, 91].”
One-Sided Shewhart-type Charts for Monitoring the Coefficient of Variation in Short Production Runs (2015, neutral): “[22]).”
Monitoring the coefficient of variation using a variable sample size control chart (2015, background): “It is also adopted in chemical and biological assay quality control to validate results, see [15].”
Monitoring the Coefficient of Variation Using EWMA Charts (2011, background): “It is also adopted in chemical- and biologicalassay quality control to validate results; see Reed et al. (2002).”