An Alternative Data Transformation Approach for ADA Cut Point Determination: Why Not Use a Weibull Transformation?

Jordan, Gregor; Staack, Roland F.

doi:10.1208/s12248-021-00625-6

Cited by 2 publications

(3 citation statements)

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“…Normal method . The normal method finds the Q quantile of a normal distribution with a mean of and a standard deviation of , where and denote the mean and standard deviation of , respectively [ 3 , 5 – 7 , 17 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…Lognormal method . The lognormal method is the normal method applied to the data after a natural log transformation [ 5 , 7 , 17 ]. This equates to fitting a lognormal distribution to the raw data and using the Q quantile of that lognormal distribution.…”

Section: Methodsmentioning

confidence: 99%

“…Empirical method . The empirical method uses the empirical quantile of as an estimator of the cutoff, avoiding parametric assumptions [ 5 – 7 , 17 , 18 ]. The empirical method is the only nonparametric method widely used in the literature and the only one considered herein.…”

Section: Methodsmentioning

confidence: 99%

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Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory

Pugh,

Fosdick,

Nehring

et al. 2024

BMC Med Res Methodol

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Background Rapidly developing tests for emerging diseases is critical for early disease monitoring. In the early stages of an epidemic, when low prevalences are expected, high specificity tests are desired to avoid numerous false positives. Selecting a cutoff to classify positive and negative test results that has the desired operating characteristics, such as specificity, is challenging for new tests because of limited validation data with known disease status. While there is ample statistical literature on estimating quantiles of a distribution, there is limited evidence on estimating extreme quantiles from limited validation data and the resulting test characteristics in the disease testing context. Methods We propose using extreme value theory to select a cutoff with predetermined specificity by fitting a Pareto distribution to the upper tail of the negative controls. We compared this method to five previously proposed cutoff selection methods in a data analysis and simulation study. We analyzed COVID-19 enzyme linked immunosorbent assay antibody test results from long-term care facilities and skilled nursing staff in Colorado between May and December of 2020. Results We found the extreme value approach had minimal bias when targeting a specificity of 0.995. Using the empirical quantile of the negative controls performed well when targeting a specificity of 0.95. The higher target specificity is preferred for overall test accuracy when prevalence is low, whereas the lower target specificity is preferred when prevalence is higher and resulted in less variable prevalence estimation. Discussion While commonly used, the normal based methods showed considerable bias compared to the empirical and extreme value theory-based methods. Conclusions When determining disease testing cutoffs from small training data samples, we recommend using the extreme value based-methods when targeting a high specificity and the empirical quantile when targeting a lower specificity.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory

Pugh,

Fosdick,

Nehring

et al. 2024

BMC Med Res Methodol

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2021 White Paper on Recent Issues in Bioanalysis: TAb/NAb, Viral Vector CDx, Shedding Assays; CRISPR/Cas9 & CAR-T Immunogenicity; PCR & Vaccine Assay Performance; ADA Assay Comparability & Cut Point Appropriateness ( Part 3 – Recommendations on Gene Therapy, Cell Therapy, Vaccine Assays; Immunogenicity of Biotherapeutics and Novel Modalities; Integrated Summary of Immunogenicity Harmonization)

Loo

Harris²,

Milton

et al. 2022

Bioanalysis

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The 15th edition of the Workshop on Recent Issues in Bioanalysis (15th WRIB) was held on 27 September to 1 October 2021. Even with a last-minute move from in-person to virtual, an overwhelmingly high number of nearly 900 professionals representing pharma and biotech companies, contract research organizations (CROs), and multiple regulatory agencies still eagerly convened to actively discuss the most current topics of interest in bioanalysis. The 15th WRIB included 3 Main Workshops and 7 Specialized Workshops that together spanned 1 week in order to allow exhaustive and thorough coverage of all major issues in bioanalysis, biomarkers, immunogenicity, gene therapy, cell therapy and vaccines. Moreover, in-depth workshops on biomarker assay development and validation (BAV) (focused on clarifying the confusion created by the increased use of the term “Context of Use – COU”); mass spectrometry of proteins (therapeutic, biomarker and transgene); state-of-the-art cytometry innovation and validation; and, critical reagent and positive control generation were the special features of the 15th edition. This 2021 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop, and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2021 edition of this comprehensive White Paper has been divided into three parts for editorial reasons. This publication (Part 3) covers the recommendations on TAb/NAb, Viral Vector CDx, Shedding Assays; CRISPR/Cas9 & CAR-T Immunogenicity; PCR & Vaccine Assay Performance; ADA Assay Comparability & Cut Point Appropriateness. Part 1A (Endogenous Compounds, Small Molecules, Complex Methods, Regulated Mass Spec of Large Molecules, Small Molecule, PoC), Part 1B (Regulatory Agencies' Inputs on Bioanalysis, Biomarkers, Immunogenicity, Gene & Cell Therapy and Vaccine) and Part 2 (ISR for Biomarkers, Liquid Biopsies, Spectral Cytometry, Inhalation/Oral & Multispecific Biotherapeutics, Accuracy/LLOQ for Flow Cytometry) are published in volume 14 of Bioanalysis, issues 9 and 10 (2022), respectively.

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An Alternative Data Transformation Approach for ADA Cut Point Determination: Why Not Use a Weibull Transformation?

Cited by 2 publications

References 14 publications

Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory

Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory

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