Passing–Bablok regression is a method from robust statistics for nonparametric regression analysis suitable for method comparison studies introduced by and Heinrich Passing in 1983. The procedure is adapted to fit linear errors-in-variables models. It is symmetrical and is robust in the presence of one or few outliers. The original algorithm is rather slow for larger data sets as its computational complexity is . However, fast quasilinear algorithms of complexity ln have been devised. .
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| - Passing–Bablok regression is a method from robust statistics for nonparametric regression analysis suitable for method comparison studies introduced by and Heinrich Passing in 1983. The procedure is adapted to fit linear errors-in-variables models. It is symmetrical and is robust in the presence of one or few outliers. The original algorithm is rather slow for larger data sets as its computational complexity is . However, fast quasilinear algorithms of complexity ln have been devised. . (en)
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| - Passing–Bablok regression is a method from robust statistics for nonparametric regression analysis suitable for method comparison studies introduced by and Heinrich Passing in 1983. The procedure is adapted to fit linear errors-in-variables models. It is symmetrical and is robust in the presence of one or few outliers. The Passing-Bablok procedure fits the parameters and of the linear equation using non-parametric methods. The coefficient is calculated by taking the shifted median of all slopes of the straight lines between any two points, disregarding lines for which the points are identical or . The median is shifted based on the number of slopes where to create an approximately consistent estimator. The estimator is therefore close in spirit to the Theil-Sen estimator. The parameter is calculated by . In 1986, Passing and Bablok extended their method introducing an equivariant extension for method transformation which also works when the slope is far from 1.It may be considered a robust version of reduced major axis regression. The slope estimator is the median of the absolute values of all pairwise slopes. The original algorithm is rather slow for larger data sets as its computational complexity is . However, fast quasilinear algorithms of complexity ln have been devised. . Passing and Bablok define a method for calculating a 95% confidence interval (CI) for both and in their original paper, which was later refined, though bootstrapping the parameters is the preferred method for in vitro diagnostics (IVD) when using patient samples. The Passing-Bablok procedure is valid only when a linear relationship exist between and , which can be assessed by a CUSUM test. Further assumptions include the error ratio to be proportional to the slope and the similarity of the error distributions of the and distributions.The results are interpreted as follows. If 0 is in the CI of , and 1 is in the CI of , the two methods are comparable within the investigated concentration range. If 0 is not in the CI of there is a systematic difference and if 1 is not in the CI of then there is a proportional difference between the two methods. However, the use of Passing–Bablok regression in method comparison studies has been criticized because it ignores random differences between methods. (en)
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