Derivation of Passing–Bablok regression from Kendall’s tau

Abstract

Abstract It is shown how Passing’s and Bablok’s robust regression method may be derived from the condition that Kendall’s correlation coefficient tau shall vanish upon a scaling and rotation of the data. If the ratio of the standard deviations of the regressands is known, a similar procedure leads to a robust alternative to Deming regression, which is known as the circular median of the doubled slope angle in the field of directional statistics. The derivation of the regression estimates from Kendall’s correlation coefficient makes it possible to give analytical estimates of the variances of the slope, intercept, and of the bias at medical decision point, which have not been available to date. Furthermore, it is shown that using Knight’s algorithm for the calculation of Kendall’s tau makes it possible to calculate the Passing–Bablok estimator in quasi-linear time. This makes it possible to calculate this estimator rapidly even for very large data sets. Examples with data from clinical medicine are also provided.