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

Tae Dong Jeong, Soo Kyung Kim, Sollip Kim, Chi Yeon Lim, Jae Woo Chung

Research output: Contribution to journalArticlepeer-review

Abstract

Objectives: 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. Methods: 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. Results: 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%. Conclusions: 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.

Original languageEnglish
Pages (from-to)989-994
Number of pages6
JournalClinical Chemistry and Laboratory Medicine
Volume60
Issue number7
DOIs
StatePublished - 1 Jun 2022

Keywords

  • analytical measurement range
  • linearity
  • polynomial regression
  • verification
  • weighted least-squares regression

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