Validating the Bland-Altman Method of Agreement

Bland-Altman’s analysis and plot are the most common methods used to assess the relative agreement between two analytical methods that measure the continuous variables measured on the same scale. Many agreement studies have shown that using the t-test or the Pearson’s product-moment correlation is flawed when measuring the agreement or detecting the bias. Bland-Altman proposed graphical techniques for analyzing method comparison and validation studies and many articles advocating their approach or variations of it have since appeared in many journals. The basic concept of Bland-Altman’s approach is the visualization of the difference of the measurements made by the two methods, then plotting the differences (diff) or the bias (Y-axis) versus the mean (mean) of the two readings (X-axis). In addition, additional reference lines such as the zero bias line and 95% upper (0 + 1.96 Sdiff) and 95% lower (0 1.96 Sdiff) are also overlaid on the same scatter plot. When there is no systematic bias, it is easy to verify from the plot if the differences are symmetrical around zero. If there is no relationship between the differences and the averages, the agreement between the two methods may be summarized using the means and standard deviations test methods. Bland-Altman’s plots have become an essential requirement in validity or method-comparison studies and Bland-Altman’s original paper has been cited over more than 11500 occasions—compelling evidence of its importance in medical research. However, some recent reports discussed the flaws of the Bland-Altman’s graphical methods and advocate the use of regression approach especially when the bias distribution shows heterogeneous bias distribution. Therefore, we are proposing a robust enhanced Bland-Altman’s analysis which combines a superior graphical display of bias distribution supplemented with a regression analysis between the diff and the standard measurement. The primary objective of this poster presentation is using advanced SAS statistical and graphical procedures to demonstrate our enhanced features of robust BlandAltman’s analysis and compare its performances with the original Bland-Altman’s method using six diverse types of simulated data sets.