Singtoroj T, Tarning J, Annerberg A, Ashton M, Bergqvist Y, White N J, Lindegardh N, Day N P J
Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
J Pharm Biomed Anal. 2006 Apr 11;41(1):219-27. doi: 10.1016/j.jpba.2005.11.006. Epub 2005 Dec 5.
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.
生物分析数据的质量高度依赖于为校准曲线使用适当的回归模型。传统上使用的是非加权线性回归，但它不一定是最佳模型。由于方差通常随浓度增加，生物分析测定通常通过使用数据转换和/或加权而受益。使用校准物范围为9至10000 ng/mL的数据集，将一种新方法与选择最佳回归模型的传统方法进行比较。新方法结合了每个校准水平的相对残差以及4天内独立质量控制样品的精密度和准确度，以选择并证明最佳回归模型。结果表明，不加权的对数-对数转换是拟合校准数据并确保该数据集具有良好可预测性的最简单模型。
