EurA1c: the European HbA1c Trial to Investigate the Performance of HbA1c Assays in 2166 Laboratories across 17 Countries and 24 Manufacturers by Use of the IFCC Model for Quality Targets

Background: A major objective of the IFCC Committee on Education and Use of Biomarkers in Diabetes is to generate awareness and improvement of HbA1c assays through evaluation of the performance by countries and manufacturers. Methods: Fresh whole blood and lyophilized hemolysate specimens manufactured from the same pool were used by 17 external quality assessment organizers to evaluate analytical performance of 2166 laboratories. Results were evaluated per country, per manufacturer, and per manufacturer and country combined according to criteria of the IFCC model for quality targets. Results: At the country level with fresh whole blood specimens, 6 countries met the IFCC criterion, 2 did not, and 2 were borderline. With lyophilized hemolysates, 5 countries met the criterion, 2 did not, and 3 were borderline. At the manufacturer level using fresh whole blood specimens, 13 manufacturers met the criterion, 8 did not, and 3 were borderline. Using lyophilized hemolysates, 7 manufacturers met the criterion, 6 did not, and 3 were borderline. In both country and manufacturer groups, the major contribution to total error derived from between-laboratory variation. There were no substantial differences in performance between groups using fresh whole blood or lyophilized hemolysate samples. Conclusions: The state of the art is that 1 of 20 laboratories does not meet the IFCC criterion, but there are substantial differences between country and between manufacturer groups. Efforts to further improve quality should focus on reducing between-laboratory variation. With some limitations, fresh whole blood and well-defined lyophilized specimens are suitable for purpose

Principal Balances of Compositional Data for Regression and Classification using Partial Least Squares

High-dimensional compositional data are commonplace in the modern omics sciences amongst others. Analysis of compositional data requires a proper choice of orthonormal coordinate representation as their relative nature is not compatible with the direct use of standard statistical methods. Principal balances, a specific class of log-ratio coordinates, are well suited to this context since they are constructed in such a way that the first few coordinates capture most of the variability in the original data. Focusing on regression and classification problems in high dimensions, we propose a novel Partial Least Squares (PLS) based procedure to construct principal balances that maximize explained variability of the response variable and notably facilitates interpretability when compared to the ordinary PLS formulation. The proposed PLS principal balance approach can be understood as a generalized version of common logcontrast models, since multiple orthonormal (instead of one) logcontrasts are estimated simultaneously. We demonstrate the performance of the method using both simulated and real data sets

Principal Balances of Compositional Data for Regression and Classification using Partial Least Squares

High-dimensional compositional data are commonplace in the modern omics sciences amongst others. Analysis of compositional data requires a proper choice of orthonormal coordinate representation as their relative nature is not compatible with the direct use of standard statistical methods. Principal balances, a specific class of log-ratio coordinates, are well suited to this context since they are constructed in such a way that the first few coordinates capture most of the variability in the original data. Focusing on regression and classification problems in high dimensions, we propose a novel Partial Least Squares (PLS) based procedure to construct principal balances that maximize explained variability of the response variable and notably facilitates interpretability when compared to the ordinary PLS formulation. The proposed PLS principal balance approach can be understood as a generalized version of common logcontrast models, since multiple orthonormal (instead of one) logcontrasts are estimated simultaneously. We demonstrate the performance of the method using both simulated and real data sets