Comparison of six regression-based lot-to-lot verification approaches

Norman Wen Xuan Koh, Corey Markus, Tze Ping Loh, Chun Yee Lim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Objectives: Detection of between-lot reagent bias is clinically important and can be assessed by application of regression-based statistics on several paired measurements obtained from the existing and new candidate lot. Here, the bias detection capability of six regression-based lot-to-lot reagent verification assessments, including an extension of the Bland-Altman with regression approach are compared. Methods: Least squares and Deming regression (in both weighted and unweighted forms), confidence ellipses and Bland-Altman with regression (BA-R) approaches were investigated. The numerical simulation included permutations of the following parameters: differing result range ratios (upper:lower measurement limits), levels of significance (alpha), constant and proportional biases, analytical coefficients of variation (CV), and numbers of replicates and sample sizes. The sample concentrations simulated were drawn from a uniformly distributed concentration range. Results: At a low range ratio (1:10, CV 3%), the BA-R performed the best, albeit with a higher false rejection rate and closely followed by weighted regression approaches. At larger range ratios (1:1,000, CV 3%), the BA-R performed poorly and weighted regression approaches performed the best. At higher assay imprecision (CV 10%), all six approaches performed poorly with bias detection rates <50%. A lower alpha reduced the false rejection rate, while greater sample numbers and replicates improved bias detection. Conclusions: When performing reagent lot verification, laboratories need to finely balance the false rejection rate (selecting an appropriate alpha) with the power of bias detection (appropriate statistical approach to match assay performance characteristics) and operational considerations (number of clinical samples and replicates, not having alternate reagent lot).

Original languageEnglish
Pages (from-to)1175-1185
Number of pages11
JournalClinical Chemistry and Laboratory Medicine
Issue number8
Early online date16 May 2022
Publication statusPublished - 1 Jul 2022
Externally publishedYes


  • between-reagent lot
  • bias
  • drift
  • reagent lot
  • shift


Dive into the research topics of 'Comparison of six regression-based lot-to-lot verification approaches'. Together they form a unique fingerprint.

Cite this