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Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

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Volume 52, Issue 7


A statistical basis for harmonization of thyroid stimulating hormone immunoassays using a robust factor analysis model

Dietmar Stöckl / Katleen Van Uytfanghe
  • Laboratory for Analytical Chemistry, Faculty of Pharmaceutical Sciences, Ghent University, Gent, Belgium
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Stefan Van Aelst
  • Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Linda M. Thienpont
  • Corresponding author
  • Laboratory for Analytical Chemistry, Faculty of Pharmaceutical Sciences, Ghent University, Gent, Belgium
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2014-02-22 | DOI: https://doi.org/10.1515/cclm-2013-1038


Background: Between-method equivalence ideally is achieved by calibration against an SI-traceable reference measurement procedure. For measurement of thyroid stimulating hormone (TSH), it is unlikely to accomplish this goal in mid-term. Therefore, we investigated a statistical alternative based on a factor analysis (FA) model.

Methods: The FA model was applied to TSH results for 94 samples generated by 14 immunoassays (concentration range: 0.0005–78 mIU/L). The dataset did not fulfill the assumption of a homogeneous sample from an elliptically symmetric distribution, and, therefore, required standardization prior to application of the FA model. As outliers and missing values also occurred, the key quantities of the FA model had to be estimated with a method that can handle these complications. We selected a robust alternating regressions (RAR) method, which replaces in the minimization criterion of the fitting process the squared differences between results xij and model fit x^ij by a weighted absolute difference. The weights are adaptively determined in successive regressions, which down weighs the outliers. The weights for missing values are set to zero.

Results: The quality of the estimated targets was reflected by their central position in the distributions, and description of the relationship between results and targets by a simple two-parameter regression equation with high correlation coefficients and low SDs of the percentage-residuals. Mathematical recalibration eliminated the method differences and improved the between-method CV from 11% to 6%.

Conclusions: RAR applied to a multimethod comparison dataset hampered by outliers and missing values, is fit to the purpose of harmonization.

This article offers supplementary material which is provided at the end of the article.

Keywords: factor analysis model; harmonization; principal component analysis; robust alternating regressions


  • 1.

    Miller WG, Myers GL, Gantzer ML, Kahn SE, Schönbrunner ER, Thienpont LM, et al. Roadmap for harmonization of clinical laboratory measurement procedures. Clin Chem 2011;57:1108–17.Web of ScienceCrossrefGoogle Scholar

  • 2.

    Joint Committee for Traceability in Laboratory Medicine (JCTLM) Database of higher-order reference materials, measurement methods/procedures and services. Available from: http://www.bipm.org/jctlm/. Accessed 31 July, 2013.

  • 3.

    Thienpont LM, Van Houcke SK. Traceability to a common standard for protein measurements by immunoassay for in-vitro diagnostic purposes. Clin Chim Acta 2010;411:2058–61.PubMedWeb of ScienceGoogle Scholar

  • 4.

    Lawton WH, Sylvestre EA, Young-Ferraro BJ. Statistical comparison of multiple analytic procedures: application to clinical chemistry. Technometrics 1979;21:397–409.CrossrefGoogle Scholar

  • 5.

    Carey RN, Wold S, Westgard JO. Principal component analysis: an alternative to “referee” methods in method comparison studies. Anal Chem 1975;47:1824–9.CrossrefPubMedGoogle Scholar

  • 6.

    Rymer JC, Sabatier R, Daver A, Bourleaud J, Assicot M, Bremond J, et al. A new approach for clinical biological assay comparison and standardization: application of principal component analysis to a multicenter study of twenty-one carcinoembryonic antigen immunoassay kits. Clin Chem 1999;45:869–81.PubMedGoogle Scholar

  • 7.

    Van Houcke SK, Van Aelst S, Van Uytfanghe K, Thienpont LM. Harmonization of immunoassays to the all-procedure trimmed mean – proof of concept by use of data from the insulin standardization project. Clin Chem Lab Med 2013;51:e103–5.Web of ScienceGoogle Scholar

  • 8.

    Miller WG, Thienpont LM, Van Uytfanghe K, Clark PM, Lindstedt P, Nilsson G, et al. Toward standardization of insulin immunoassays. Clin Chem 2009;55:1011–8.CrossrefPubMedWeb of ScienceGoogle Scholar

  • 9.

    Thienpont LM, Van Uytfanghe K, Beastall G, Faix JD, Ieiri T, Miller WG, et al. Report of the IFCC Working Group for Standardization of Thyroid Function Tests, Part 1: Thyroid-stimulating hormone. Clin Chem 2010;56:902–11.Web of ScienceGoogle Scholar

  • 10.

    Wold H. Nonlinear estimation by iterative least squares procedures. In: David FN, editor. Research papers in statistics: Festschrift for Jerzy Neyman. London: Wiley, 1966;411–44.Google Scholar

  • 11.

    Gabriel KR, Zamir S. Lower rank approximation of matrices by least squares with any choice of weights. Technometrics 1979;21:489–98.CrossrefGoogle Scholar

  • 12.

    Croux C, Filzmoser P, Pison G, Rousseeuw PJ. Fitting multiplicative models by robust alternating regressions. Statist Comput 2003;13:23–36.CrossrefGoogle Scholar

  • 13.

    Hubert M, Rousseeuw PJ, Van Aelst S. High-breakdown robust multivariate methods. Stat Sci 2008;23:92–119.CrossrefWeb of ScienceGoogle Scholar

  • 14.

    Westgard QC. Desirable specifications for total error, imprecision, and bias derived from biologic variation. Available from: http://www.westgard.com/biodatabase1.htm. Accessed 31 July, 2013.

  • 15.

    R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org/. Accessed 31 July, 2013.

  • 16.

    Chu H, Chen S, Louis TA. Random effects models in a meta-analysis of the accuracy of two diagnostic tests without a gold standard. J Am Stat Assoc 2009;104:512–23.CrossrefGoogle Scholar

  • 17.

    Joo DJ, Jung I, Kim MS, Huh KH, Kim H, Choi JS, et al. Comparison of the affinity column-mediated immunoassay and microparticle enzyme immunoassay methods as a tacrolimus concentration assay in the early period after liver transplantation. Transplan Proc 2010;42:4137–40.CrossrefGoogle Scholar

  • 18.

    Rose CE, Romero-Steiner S, Burton RL, Carlone GM, Goldblatt D, Nahm MH, et al. Multilaboratory comparison of Streptococcus pneumoniae opsonophagocytic killing assays and their level of agreement for the determination of functional antibody activity in human reference sera. Clin Vaccine Immunol 2011;18:135–42.PubMedCrossrefWeb of ScienceGoogle Scholar

About the article

Corresponding author: Linda M. Thienpont, Laboratory for Analytical Chemistry, Faculty of Pharmaceutical Sciences, Ghent University, Harelbekestraat 72, 9000 Gent, Belgium, Phone: +32 9 2648104, Fax: +32 9 2648198, E-mail:

Received: 2013-12-02

Accepted: 2014-01-27

Published Online: 2014-02-22

Published in Print: 2014-07-01

Citation Information: Clinical Chemistry and Laboratory Medicine (CCLM), Volume 52, Issue 7, Pages 965–972, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/cclm-2013-1038.

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