Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 9, 2022

Comparison between polynomial regression and weighted least squares regression analysis for verification of analytical measurement range

  • Tae-Dong Jeong ORCID logo , Soo-Kyung Kim , Sollip Kim ORCID logo , Chi-Yeon Lim and Jae-Woo Chung ORCID logo EMAIL logo



Recently, the linearity evaluation protocol by the Clinical & Laboratory Standards Institute (CLSI) has been revised from EP6-A to EP6-ED2, with the statistical method of interpreting linearity evaluation data being changed from polynomial regression to weighted least squares linear regression (WLS). We analyzed and compared the analytical measurement range (AMR) verification results according to the present and prior linearity evaluation guidelines.


The verification of AMR of clinical chemistry tests was performed using five samples with two replicates in three different laboratories. After analyzing the same evaluation data in each laboratory by the polynomial regression analysis and WLS methods, results were compared to determine whether linearity was verified across the five sample concentrations. In addition, whether the 90% confidence interval of deviation from linearity by WLS was included in the allowable deviation from linearity (ADL) was compared.


A linearity of 42.3–56.8% of the chemistry items was verified by polynomial regression analysis in three laboratories. For analysis of the same data by WLS, a linearity of 63.5–78.3% of the test items was verified where the deviation from linearity of all five samples was within the ADL criteria, and the cases where the 90% confidence interval of all deviation from linearity overlapped the ADL was 78.8–91.3%.


Interpreting AMR verification data by the WLS method according to the newly revised CLSI document EP6-ED2 could reduce laboratory workload, enabling efficient laboratory practice.

Corresponding author: Jae-Woo Chung, MD, PhD, Department of Laboratory Medicine, Dongguk University Ilsan Hospital, Goyang, Republic of Korea, Phone: 82 31 961 7894, Fax: 82 31 961 7902, E-mail:

  1. Research funding: None declared.

  2. Author contributions: TDJ designed the study, analyzed the data, and wrote the draft; JWC conceived the study, analyzed the data, and finalized the draft; SKK and SK discussed the data and reviewed the manuscript; CYL contributed to statistical interpretation. All authors read and approved the final manuscript. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: This study involves no more than minimal risk to the subjects. The local IRB allow to the informed consent.

  5. Ethical approval: This study was approved by the Institutional Review Board of Ewha Womans University Seoul Hospital (approval No: SEUMC 2021-09-019).


1. Killeen, AA, Long, T, Souers, R, Styer, P, Ventura, CB, Klee, GG. Verifying performance characteristics of quantitative analytical systems: calibration verification, linearity, and analytical measurement range. Arch Pathol Lab Med 2014;138:1173–81. in Google Scholar PubMed

2. CLSI. Evaluation of the linearity of quantitative measurement procedures: a statistical approach; approved guideline. CLSI document EP06-A. Wayne, PA: Clinical and Laboratory Standards Institute; 2003.Search in Google Scholar

3. CLSI. Evaluation of linearity of quantitative measurement procedures, 2nd ed. Wayne, PA: CLSI Guideline EP06. Clinical and Laboratory Standards Institutie; 2020.Search in Google Scholar

4. Kroll, MH, Emancipator, K. A theoretical evaluation of linearity. Clin Chem 1993;39:405–13. in Google Scholar

5. Emancipator, K, Kroll, MH. A quantitative measure of nonlinearity. Clin Chem 1993;39:766–72. in Google Scholar

6. Jhang, JS, Chang, CC, Fink, DJ, Kroll, MH. Evaluation of linearity in the clinical laboratory. Arch Pathol Lab Med 2004;128:44–8. in Google Scholar PubMed

7. Webster, C, Coskun, A, Gonzales-Lao, E, Diaz-Garzon, J, Jonker, N, Minchinela, J, et al.. The EFLM biological variation database. Available from [Accessed 1 Nov 2021].Search in Google Scholar

8. Desirable biological variation database specifications. Available from [Accessed 1 Nov 2021].Search in Google Scholar

9. Westgard, JO. Useful measures and models for analytical quality management in medical laboratories. Clin Chem Lab Med 2016;54:223–33. in Google Scholar PubMed

Supplementary Material

The online version of this article offers supplementary material (

Received: 2022-01-06
Accepted: 2022-04-22
Published Online: 2022-05-09
Published in Print: 2022-06-27

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 25.2.2024 from
Scroll to top button