Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Chamblee, GA 30341, USA.
Clin Chim Acta. 2010 Feb;411(3-4):270-9. doi: 10.1016/j.cca.2009.11.021. Epub 2009 Nov 27.
To minimize the effect of heteroscedasticity in chemical and other data, weighted regression analysis is the preferred regression technique. In this work a regression weight that maximizes accuracy and precision was sought.
Using real and simulated data from a serum cotinine assay, performance of 3 weighting schemes, namely, 1/X, 1/X(2), and 1/s(2)(Y) to calibrate chemical data was evaluated. Two performance measures were used to evaluate the accuracy and precision of each scheme to estimate concentrations in unknown specimens.
The weight, 1/X-particularly for low concentrations-was not acceptable. The performance of both, 1/X(2) and 1/s(2)(Y) was close, 1/X(2) being slightly better in many cases. Overall, however, when the variance of instrument signal increased beyond certain limits, none of the weighting schemes performed acceptably.
Because of its simplicity and ease of use, 1/X(2) is recommended for general application. If, however, instrument signal variance is too high to be managed by statistical techniques, the only solution is to control such variance through laboratory-based solutions.
为了将化学数据和其他数据中的异方差效应降到最低，加权回归分析是首选的回归技术。在这项工作中，寻求了一种能够最大化准确性和精密度的回归权重。
使用来自血清可替宁检测的真实和模拟数据，评估了 3 种加权方案，即 1/X、1/X(2)和 1/s(2)(Y)，以校准化学数据。使用两种性能指标来评估每种方案在估计未知样本浓度时的准确性和精密度。
权重 1/X-特别是对于低浓度-是不可接受的。1/X(2)和 1/s(2)(Y)的性能非常接近，在许多情况下 1/X(2)略好一些。然而，总体而言，当仪器信号的方差超过一定限制时，没有任何加权方案表现得令人满意。
由于其简单易用，1/X(2)推荐用于一般应用。但是，如果仪器信号方差太高，无法通过统计技术进行处理，那么唯一的解决方案是通过基于实验室的解决方案来控制这种方差。
