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Licensed Unlicensed Requires Authentication Published by De Gruyter May 16, 2022

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

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

Abstract

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).


Corresponding author: Tze Ping Loh, Department of Laboratory Medicine, National University Hospital, 5 Lower Kent Ridge Road, 119074 Singapore, Singapore, Phone: (+65) 67724345, Fax: (+65) 67771613, E-mail:

  1. Research funding: None declared.

  2. Author contributions: 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: Not acceptable.

  5. Ethical approval: Not acceptable.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/cclm-2022-0274).


Received: 2022-03-22
Accepted: 2022-04-29
Published Online: 2022-05-16
Published in Print: 2022-07-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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