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|Title:||A new approach to evaluate regression models during validation of bioanalytical assays|
N. J. White
N. P.J. Day
Goteborg University, Sahlgrenska Academy
Nuffield Department of Clinical Medicine
|Keywords:||Biochemistry, Genetics and Molecular Biology;Chemistry;Pharmacology, Toxicology and Pharmaceutics|
|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.|
|Appears in Collections:||Scopus 2006-2010|
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