T. SingtorojJ. TarningA. AnnerbergM. AshtonY. BergqvistN. J. WhiteN. LindegardhN. P.J. DayMahidol UniversityGoteborg University, Sahlgrenska AcademyHogskolan DalarnaNuffield Department of Clinical Medicine2018-08-202018-08-202006-04-11Journal of Pharmaceutical and Biomedical Analysis. Vol.41, No.1 (2006), 219-227073170852-s2.0-33645080172https://repository.li.mahidol.ac.th/handle/20.500.14594/23050The 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.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyChemistryPharmacology, Toxicology and PharmaceuticsArticleSCOPUS10.1016/j.jpba.2005.11.006