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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 10000 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.

Original publication

DOI

10.1016/j.jpba.2005.11.006

Type

Journal article

Journal

J Pharm Biomed Anal

Publication Date

11/04/2006

Volume

41

Pages

219 - 227

Keywords

Algorithms, Calibration, Chemistry Techniques, Analytical, Chemistry, Pharmaceutical, Chromatography, Liquid, Computer Simulation, Dose-Response Relationship, Drug, Models, Statistical, Quinolines, Regression Analysis, Reproducibility of Results, Technology, Pharmaceutical, Time Factors