Retired, formerly at: Nuclear Medicine Department, Christchurch Hospital, Christchurch, New Zealand.
Ann Clin Biochem. 2019 Mar;56(2):198-203. doi: 10.1177/0004563218806560. Epub 2018 Oct 29.
Bland-Altman analysis is a popular and widely used method for assessing the level of agreement between two analytical methods. An important assumption is that paired method differences exhibit approximately constant (homogeneous) scatter when plotted against pair means. This allows estimation of limits of agreement which retain validity across the entire range of mean values. In practice, pair differences often increase systematically with the mean and Bland and Altman used log transformed data to achieve approximately homogeneous scatter. Unfortunately, a logarithmic transformation fails when data are located near the detection limit of an assay (a region that is often of considerable clinical importance).
Simulated thyrotropin data are used to illustrate how a variance function, estimated from pair differences, can be used to transform problematic data into a form suitable for traditional Bland-Altman analysis. Simulated and real data sets are used in a supplementary file to illustrate and offer practical solutions to potential problems.
Following transformation by variance function, Bland-Altman results can be readily interpreted by back-transformation either to the original measurement scale or as percentage values. Limits of agreement are no longer horizontal straight lines, but their shapes simply reflect error characteristics which are (or should be) thoroughly familiar to laboratory analysts.
The method is completely general and in principle requires only the estimation of a variance function that reliably describes the relationship between the variances of pair differences and their mean values. A computer program is available which performs the necessary calculations.
Bland-Altman 分析是一种常用于评估两种分析方法之间一致性水平的方法。一个重要的假设是,当将成对方法差异相对于均值进行绘制时,其散点应呈现近似恒定(均匀)的状态。这允许估计一致性界限,这些界限在整个均值范围内保持有效。在实际应用中,成对差异通常随着均值的增加而呈现系统性增加,而 Bland 和 Altman 使用对数转换数据来实现近似均匀的散点。然而,当数据位于测定下限(通常是具有重要临床意义的区域)附近时,对数转换会失败。
使用模拟促甲状腺激素数据来说明如何使用从成对差异估计的方差函数来转换存在问题的数据,以使其适合传统的 Bland-Altman 分析。模拟和真实数据集在补充文件中使用,以说明并提供潜在问题的实际解决方案。
经过方差函数转换后,通过反向转换,可以直接解释 Bland-Altman 结果,既可以转换回原始测量尺度,也可以转换为百分比值。一致性界限不再是水平的直线,而是简单地反映了误差特征,这些特征(或应该)是实验室分析师非常熟悉的。
该方法是完全通用的,原则上仅需要估计一个方差函数,该方差函数能够可靠地描述成对差异的方差与其均值之间的关系。提供了一个可以执行必要计算的计算机程序。