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A new method for identification of outliers in immunogenicity assay cut point data.
J Immunol Methods. 2020 Jun 29 [Online ahead of print]JI

Abstract

The cut point is an important parameter for immunogenicity assay validation and critical to immunogenicity assessment in clinical trials. FDA (2019) recommends using a statistical approach to derive cut point, with an appropriate outlier removal procedure. In general, the industry follows the methods described in Shankar et al. (2008) and Zhang et al. (2013) among others to determine cut point. Outlier removal is a necessary step during the cut point determination exercise to reduce potential false negative classifications. However, the widely used statistical outlier removal method, namely, Tukey's box-plot method (1.5 times inter-quartile range, IQR), is often found to be overly conservative in the sense that it removes too many "outliers". Tukey's box-plot method can be used to flag potential outliers for further investigation, however, it is not a hypothesis testing based statistical method. Removing these suspected "outliers" will lead to lower cut point which might confound immunogenicity assessment due to the presence of many low false positives. Besides, the very nature of assay analytical variability has a non-negligible adverse impact on the reliability of ADA classification in terms of false positive and false negative, demanding as large as possible contribution from biological variability relative to analytical variability. A new outlier removal procedure, which takes into account the relative magnitude between biological variability and analytical variability within the sample population, is proposed and statistically justified. After sequential removal of analytical and biological outliers, a 5% false positive rate and 1% false positive rate in screening and confirmatory assays, respectively, are still targeted without increasing potential false negatives. Internal data shows that this practice has minimal impact on assay sensitivity and has the advantage of selecting true positive samples. It is shown that the new procedure is more appropriate for cut point determination.

Authors+Show Affiliations

AstraZeneca PLC. Electronic address: Jianchun.zhang@astrazeneca.com.AstraZeneca PLC.AstraZeneca PLC.AstraZeneca PLC.AstraZeneca PLC.AstraZeneca PLC.AstraZeneca PLC.AstraZeneca PLC.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32615125

Citation

Zhang, Jianchun, et al. "A New Method for Identification of Outliers in Immunogenicity Assay Cut Point Data." Journal of Immunological Methods, 2020, p. 112817.
Zhang J, Arends RH, Kubiak RJ, et al. A new method for identification of outliers in immunogenicity assay cut point data. J Immunol Methods. 2020.
Zhang, J., Arends, R. H., Kubiak, R. J., Roskos, L. K., Liang, M., Lee, N., Chen, C. C., & Yang, H. (2020). A new method for identification of outliers in immunogenicity assay cut point data. Journal of Immunological Methods, 112817. https://doi.org/10.1016/j.jim.2020.112817
Zhang J, et al. A New Method for Identification of Outliers in Immunogenicity Assay Cut Point Data. J Immunol Methods. 2020 Jun 29;112817. PubMed PMID: 32615125.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A new method for identification of outliers in immunogenicity assay cut point data. AU - Zhang,Jianchun, AU - Arends,Rosalin Hgp, AU - Kubiak,Robert J, AU - Roskos,Lorin K, AU - Liang,Meina, AU - Lee,Nancy, AU - Chen,Cecil Chi-Keung, AU - Yang,Harry, Y1 - 2020/06/29/ PY - 2019/08/11/received PY - 2020/06/17/revised PY - 2020/06/23/accepted PY - 2020/7/3/pubmed PY - 2020/7/3/medline PY - 2020/7/3/entrez KW - ADA KW - Analytical variability KW - Biological variability KW - Cut point KW - False negative KW - False positive KW - Immunogenicity KW - Outlier removal SP - 112817 EP - 112817 JF - Journal of immunological methods JO - J. Immunol. Methods N2 - The cut point is an important parameter for immunogenicity assay validation and critical to immunogenicity assessment in clinical trials. FDA (2019) recommends using a statistical approach to derive cut point, with an appropriate outlier removal procedure. In general, the industry follows the methods described in Shankar et al. (2008) and Zhang et al. (2013) among others to determine cut point. Outlier removal is a necessary step during the cut point determination exercise to reduce potential false negative classifications. However, the widely used statistical outlier removal method, namely, Tukey's box-plot method (1.5 times inter-quartile range, IQR), is often found to be overly conservative in the sense that it removes too many "outliers". Tukey's box-plot method can be used to flag potential outliers for further investigation, however, it is not a hypothesis testing based statistical method. Removing these suspected "outliers" will lead to lower cut point which might confound immunogenicity assessment due to the presence of many low false positives. Besides, the very nature of assay analytical variability has a non-negligible adverse impact on the reliability of ADA classification in terms of false positive and false negative, demanding as large as possible contribution from biological variability relative to analytical variability. A new outlier removal procedure, which takes into account the relative magnitude between biological variability and analytical variability within the sample population, is proposed and statistically justified. After sequential removal of analytical and biological outliers, a 5% false positive rate and 1% false positive rate in screening and confirmatory assays, respectively, are still targeted without increasing potential false negatives. Internal data shows that this practice has minimal impact on assay sensitivity and has the advantage of selecting true positive samples. It is shown that the new procedure is more appropriate for cut point determination. SN - 1872-7905 UR - https://www.unboundmedicine.com/medline/citation/32615125/A_new_method_for_identification_of_outliers_in_immunogenicity_assay_cut_point_data L2 - https://linkinghub.elsevier.com/retrieve/pii/S0022-1759(20)30101-0 DB - PRIME DP - Unbound Medicine ER -
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