Statistical comparison of two laboratory methods, measuring the same objects, is usually performed by regression. However, the results achieved are often incorrect due to application of ordinary least squares linear regression, which should not be used for this purpose. A statistically correct decision whether two laboratory methods provide concordant or discordant results is reliable only when using regression techniques which respect the errors of both compared variables.Another correct possibility is to apply a robust regression where the errors of compared variables are not influencing the calculation of regression parametersand their standard deviations. A survey of appropriate regression techniques and their use for comparison of real-life clinical methods is given in this paper.
