Kimanani E K, Lavigne J
Department of Biometrics and Pharmacokinetics R & D, Phoenix International Life Sciences, Montreal, Quebec, Canada.
J Pharm Biomed Anal. 1998 Feb;16(6):1107-15. doi: 10.1016/s0731-7085(97)00063-0.
The objective of this study was to investigate the variability of optimal power models in contrast to common regression models within and between analytical methods, as well as the frequency of outlier rejection. This was done by fitting the power model to calibration curve data using the minimum sum of squared residuals as a curve selection criterion. The jackknife percent deviation was used for detecting outliers. The data were obtained from 2087 analytical batches for 91 projects using various analytical techniques. The most frequent regression model varied between analytical techniques while the median and interquartile range of the optimal powers were stable. Outlier rejection is highest in GC and LCMS in which the Wagner (Quadratic, log-log) is the most frequent model. These results suggest that the greatest source of variability in the ideal transformation may not be the analytical technique but other within-lab sources. Outlying values may be due to these other sources of variability as suggested by the outlier rejection profile.
本研究的目的是调查与普通回归模型相比，最佳幂模型在分析方法内部和之间的变异性，以及异常值剔除的频率。这是通过使用残差平方和最小作为曲线选择标准，将幂模型拟合到校准曲线数据来完成的。留一法百分比偏差用于检测异常值。数据来自使用各种分析技术的91个项目的2087个分析批次。最常用的回归模型因分析技术而异，而最佳幂的中位数和四分位间距是稳定的。气相色谱法（GC）和液相色谱 - 质谱联用仪（LCMS）中的异常值剔除率最高，其中瓦格纳（二次，对数 - 对数）是最常用的模型。这些结果表明，理想转换中最大的变异性来源可能不是分析技术，而是实验室内部的其他来源。如异常值剔除情况所示，异常值可能归因于这些其他变异性来源。
