Publication: A new approach to evaluate regression models during validation of bioanalytical assays
Issued Date
2006-04-11
Resource Type
ISSN
07317085
Other identifier(s)
2-s2.0-33645080172
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Pharmaceutical and Biomedical Analysis. Vol.41, No.1 (2006), 219-227
Suggested Citation
T. Singtoroj, J. Tarning, A. Annerberg, M. Ashton, Y. Bergqvist, N. J. White, N. Lindegardh, N. P.J. Day A new approach to evaluate regression models during validation of bioanalytical assays. Journal of Pharmaceutical and Biomedical Analysis. Vol.41, No.1 (2006), 219-227. doi:10.1016/j.jpba.2005.11.006 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/23050
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Title
A new approach to evaluate regression models during validation of bioanalytical assays
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.
