The multivariate coefficient of variation for comparing serum protein electrophoresis techniques in external quality assessment schemes

Zhang, Lixin; Albarède, Stéphanie; Dumont, Gilles; Campenhout, Christel Van; Libeer, Jean-Claude; Albert, Adelin

doi:10.1007/s00769-009-0627-3

Cited by 9 publications

(11 citation statements)

References 15 publications

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“…This distance, the so-called Mahalanobis distance, is obtained by robust estimates of multivariate centre and variability ( 50 ). Performance characterisation of analytical methods for profile data is also possible by means of a multivariate CV, which encompasses the variability estimates of the different parameters that the profile is made of ( 51 ). …”

Section: Combining Information From Different Parameters And/or Samplesmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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Reports act as an important feedback tool in External Quality Assessment (EQA). Their main role is to score laboratories for their performance in an EQA round. The most common scores that apply to quantitative data are Q- and Z-scores. To calculate these scores, EQA providers need to have an assigned value and standard deviation for the sample. Both assigned values and standard deviations can be derived chemically or statistically. When derived statistically, different anomalies against the normal distribution of the data have to be handled. Various procedures for evaluating laboratories are able to handle these anomalies. Formal tests and graphical representation techniques are discussed and suggestions are given to help choosing between the different evaluations techniques. In order to obtain reliable estimates for calculating performance scores, a satisfactory number of data is needed. There is no general agreement about the minimal number that is needed. A solution for very small numbers is proposed by changing the limits of evaluation. Apart from analyte- and sample-specific laboratory evaluation, supplementary information can be obtained by combining results for different analytes and samples. Various techniques are overviewed. It is shown that combining results leads to supplementary information, not only for quantitative, but also for qualitative and semi-quantitative analytes.

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Section: Combining Information From Different Parameters And/or Samplesmentioning

confidence: 99%

Demystifying EQA statistics and reports

Coucke

Soumali

2017

Biochem Med

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“…Finally, through a real dataset, we illustrate how this paper meets a real practical need, raised by Zhang et al (2010) in the context of EQA schemes, for non-arbitrary statistical testing procedures to compare several multivariate coefficients of variation.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, Zhang et al (2010), noting that EQA datasets often entail outliers or blunders, advocate the use of robust MCVs to compare serum protein electrophoresis techniques. A measure of electrophoretic profile consists in 5 fractions, summing up to 100%, corresponding to the proportion of albumin and α 1 , α 2 , β and gamma-globulins in the blood.…”

Section: Real-life Applicationmentioning

confidence: 99%

“…In practice, using the plug-in principle, µ and Σ can be replaced by any pair of location and covariance estimators. As in the univariate case, the use of robust estimators is preferable in the presence of outliers, as already mentioned in Zhang et al (2010) and Aerts et al (2015). The properties of these coefficients have so far been little studied and no testing procedure for comparing K p-variate MCVs, either based on classical estimators or on robust ones, has been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…However, until now, no statistical test was available in the literature in order to highlight significant differences between the techniques. The data set used herein, obtained from Zhang et al (2010), focuses on four techniques using distinct support medium, staining color or analytical principle. These techniques will be denoted simply as CH, EH, JW and GB (the full names are reported in Table 6).…”

Section: Real-life Applicationmentioning

confidence: 99%

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Robust asymptotic tests for the equality of multivariate coefficients of variation

Aerts

Haesbroeck

2016

TEST

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In order to easily compare several populations on the basis of more than one feature, multivariate coefficients of variation (MCV) may be used as they allow to summarize relative dispersion in a single index. However, up to date, no test of equality of one or more MCVs has been developed in the literature. In this paper, several classical and robust Wald-type tests are proposed and studied. The asymptotic distributions of the test statistics are derived under elliptical symmetry, and the asymptotic efficiency of the robust versions is compared to the classical tests. Robustness of the proposed procedures is examined through partial influence functions of the test statistic, as well as by means of power and level influence functions. A simulation study compares the performance of the classical and robust tests under uncontaminated and contaminated schemes, and the difference with the usual covariance homogeneity test is highlighted. As a by-product, these tests may also be considered in the univariate context where they yield procedures that are both robust and easyto-use. They provide an interesting alternative to the numerous parametric tests existing in the literature, which are, in most cases, unreliable in presence of outliers. The methods are illustrated on a real data set.

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A novel definition of the multivariate coefficient of variation

Albert¹,

Zhang²

2010

Biometrical J

Self Cite

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The coefficient of variation CV (%) is widely used to measure the relative variation of a random variable to its mean or to assess and compare the performance of analytical techniques/equipments. A review is made of the existing multivariate extensions of the univariate CV where, instead of a random variable, a random vector is considered, and a novel definition is proposed. The multivariate CV obtained only requires the calculation of the mean vector, the covariance matrix and simple quadratic forms. No matrix inversion is needed which makes the new approach equally attractive in high dimensional as in very small sample size problems. As an illustration, the method is applied to electrophoresis data from external quality assessment in laboratory medicine, to phenotypic characteristics of pocket gophers and to a microarray data set.

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The multivariate coefficient of variation for comparing serum protein electrophoresis techniques in external quality assessment schemes

Cited by 9 publications

References 15 publications

Demystifying EQA statistics and reports

Demystifying EQA statistics and reports

Robust asymptotic tests for the equality of multivariate coefficients of variation

A novel definition of the multivariate coefficient of variation

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