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Sonny Feisal Rinaldi
Anisa Agustia Ibadurrahmah
Surya Ridwanna
Harianto Harianto


Sigma Metrics, as a quality indicator, have been widely applied in clinical laboratories to assess the performance of analytical methods. Described in the document Clinical and Laboratory Standards Institute (CLSI) EP15- A3, the use of target values can be sourced from certified reference standards, survey materials from the Proficiency Testing (PT)/External Quality Assessment (EQA), materials used in inter-laboratory quality control programs and internal quality control materials with predetermined targets. This research aims to determine whether there is a difference in the sigma metrics between the bias derived from the manufacturer's target value and those from the peer group source in the External Quality Assurance Services (EQAS) program. The research methodology employed is descriptive comparative analysis, utilizing the results of material inspection data for 15 internal quality control parameters of Clinical Chemistry over a span of 2 years at the Pramita Laboratory in Bandung. The calculation of the sigma metrics commences with computing the coefficient of variation (CV), and the appropriate Total Error aalowable (Tea) sources for each parameter are determined beforehand using the TEa algorithm. The research findings indicate a difference between the sigma metrics derived from the manufacturer's target value and those from the EQAS-peer group target value, accounting for 33% or 10 parameters out of the total parameters with 2 levels of inspection are calculated on the sigma scale. However, in 67% or 20 parameters out of the total parameters, no such difference is observed. Bias associated with the target value from the manufacturer and the EQAS peer group shows no significant difference, suggesting that the laboratory can utilize pre-existing target values confidently.


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How to Cite
Rinaldi, S. F., Ibadurrahmah, A. A., Ridwanna, S. and Harianto, H. (2024) “Comparison of the sigma metrics using the total error allowable algorithm with variation of bias source”, Indonesian Journal of Medical Laboratory Science and Technology, 6(1), pp. 27–34. doi: 10.33086/ijmlst.v6i1.4930.
Sigma metrics, TEa algorithm, Variation of bias sources


Wesgard JO, Wesgard S. Learning guide series six sigma-based quality control. Abbott. 2022. Available from:


Goel SS, Saini R, Singh SB, Anggarwal O, Goel AK. Six sigma metrics and quality control in clinical laboratory. 2014;2(2):140-149. DOI:

Varela B, Pacheco G. Comprehensive evaluation of the internal and external quality control to redefine analytical quality goals. Biochem Med (Zagreb). 2018;28 (2 Special Issue). DOI:

Mrazek C, Lipp G, Keppel MH, et al. Errors within the total laboratory testing process, from test selection to medical decision-making – A review of causes, consequences, surveillance and solutions. Biochem Med (Zagreb). 2020 Jun 15; 30(2): 020502. DOI:

Teshome M, Worede A, Asmelash D. Total Clinical Chemistry Laboratory Errors and Evaluation of the Analytical Quality Control Using Sigma Metric for Routine Clinical Chemistry Test. Journal of Multidisciplinary Healthcare. 2021:14 125–136. DOI:

Taher J, Cosme J, Renley BA, Daghfal DJ, Yip PM. A novel sigma metric encompasses global multi-site performance of 18 assays on the abbott alinity system. Clinical Biochemistry. 2019;63:106–112. DOI:

Swetha N K, Kusuma K S, Sigma metric analysis of quality indicators across the testing process as an effective tool for the evaluation of laboratory performance. Medical Journal Armed Forces India. 2023: 150-155. DOI:

Allen TT. Quality Control and Six Sigma. In: Introduction to Engineering Statistics and Lean Six Sigma. Springer London. 2019:39-54. DOI:

Thiago Coutinho. Learn how the QA metrics helps in the optimization and quality of production processes. Think Lean Six Sigma. 2021. Available from:,resourcing%2C%20and%20release%2Dreadiness.

Van Heerden M, The application of sigma metrics in the laboratory to assess quality control processes in South Africa. African Journal of Laboratory Medicine. 2022; 11(1): 1344. DOI:

Rifai N, Chiu RWK, Young I, Carl TW. Tietz Textbook of Clinical Chemistry and Moleculer Diagnostics, Ninth Edition. St. Louis, Missouri: Elsevier. 2023. Available from:

Hens K, Berth M, Armbruster D, Westgard S. Sigma metrics used to assess analytical quality of clinical chemistry assays: Importance of the allowable total error (TEa) target. Clin Chem Lab Med. 2014;52(7):973-80. DOI:

Antonelli G, Padoan A, Aita A, Sciacovelli L, Plebani M. Verification or validation, that is the question. J Lab Precis Med. 2017 Aug;2:58-58. DOI:

Geto Z, Getahun T, Lejisa T, Tolcha Y, Bikila D, Bashea C, Meles M, Habtu W, Ashebir G, Negasa B, Sileshi M, Daniel Y, Gashu A, Challa F. Evaluation of sigma metrics and westgard rule selection and implementation of internal quality control in clinical chemistry reference laboratory, ethiopian public health institute. Indian Journal of Clinical Biochemistry. 2022 Jul 1;37(3):285-93. DOI:

Ercan S. Comparison of sigma metrics computed by three bias estimation approaches for 33 chemistry and 26 immunoassay analytes. Adv Lab Med 2023; 4(3):236–245. DOI:

Kashyap A, Sampath S, Tripathi P, Sen A. Sigma metrics: A valuable tootl for evaluating the performance of internal quality control in laboratory. J Lab Physicians. 2021. DOI:

Badrick T. Biological variation: Understanding why it is so important? Vol. 23, Practical Laboratory Medicine. Elsevier B.V.; 2021;23:e00199. DOI:

Sandberg S, Fraser CG, Horvath AR, Jansen R, Jones G, Oosterhuis W, Petersen PH, Schimmel H, Sikaris K, Panteghini M. Defining analytical performance specifications: Consensus Statement from the 1st Strategic Conference of the European Federation of Clinical Chemistry and Laboratory Medicine. In: Clinical Chemistry and Laboratory Medicine. Walter de Gruyter Gmb. 2015;53(6):833-5. DOI:

Peng SQ, Zhang JF, Zhou WQ, Mao WL, Han Z. Practical application of Westgard Sigma rules with run size in analytical biochemistry processes in clinical settings. J Clin Lab Anal. 2021 Mar 1;35(3):e23665. DOI:

Dayana Mayfield. Product Validation: How to Validate Before You Develop. DevSquad. 2023. Available from:

Laudus N, Nijs L, Nauwelaers I, Dequeker EMC. The significance of external quality assessment schemes for molecular testing in clinical laboratories. Cancers. 2022;14(15):3686. DOI:

Sciacovelli L, Secchiero S, Padoan A, Plebani M. External quality assessment programs in the context of ISO 15189 accreditation. Clin Chem Lab Med. 2018;56(10):1644-1654. DOI:

Price Intelligently. What Is Product Value & How Can It Boost Market Share. Price Intelligently by Paddle. 2022. Available from:

Sachs AL, Becker-Peth M, Minner S, Thonemann UW. Empirical newsvendor biases: Are target service levels achieved effectively and efficiently?. Prod Oper Manag. 2022 Apr 1;31(4):1839-55. DOI:

Çevlik T, Haklar G. Six sigma evaluation of 17 biochemistry parameters using bias calculated from internal quality control and external quality assurance data. J Med Biochem. 2024;43(1):43–49. DOI:

Payne DA, Russomando G, Linder MW, Baluchova K, Ashavaid T, Steimer W, Ahmad-Nejad P. External quality assessment (EQA) and alternative assessment procedures (AAPs) in molecular diagnostics: findings of an international survey. Clin Chem Lab Med. 2021 Feb 1;59(2). DOI: