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Title: A new approach to evaluate regression models during validation of bioanalytical assays
Authors: T. Singtoroj
J. Tarning
A. Annerberg
M. Ashton
Y. Bergqvist
N. J. White
N. Lindegardh
N. P.J. Day
Mahidol University
Goteborg University, Sahlgrenska Academy
Hogskolan Dalarna
Nuffield Department of Clinical Medicine
Keywords: Biochemistry, Genetics and Molecular Biology;Chemistry;Pharmacology, Toxicology and Pharmaceutics
Issue Date: 11-Apr-2006
Citation: Journal of Pharmaceutical and Biomedical Analysis. Vol.41, No.1 (2006), 219-227
Abstract: The quality of bioanalytical data is highly dependent on using an appropriate regression model for calibration curves. Non-weighted linear regression has traditionally been used but is not necessarily the optimal model. Bioanalytical assays generally benefit from using either data transformation and/or weighting since variance normally increases with concentration. A data set with calibrators ranging from 9 to 10 000 ng/mL was used to compare a new approach with the traditional approach for selecting an optimal regression model. The new approach used a combination of relative residuals at each calibration level together with precision and accuracy of independent quality control samples over 4 days to select and justify the best regression model. The results showed that log-log transformation without weighting was the simplest model to fit the calibration data and ensure good predictability for this data set. © 2005 Elsevier B.V. All rights reserved.
ISSN: 07317085
Appears in Collections:Scopus 2006-2010

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