Points of Care in Using Statistics in Method Comparison Studies

Abstract

As clinical chemists and laboratory scientists, we are often concerned when personnel who have little laboratory training begin to perform laboratory tests, such as in point-of-care applications. It may be easy to perform such tests today with modern analytical systems, but there still are things that could go wrong. We hope that some kind of quality system is used to check that everything is working okay with point-of-care analyses. Imagine how statisticians might feel about the powerful statistics programs that are now in our hands. It is so easy to key-in a set of data and calculate a wide variety of statistics—regardless what those statistics are or what they mean. There also is a need to check that things are done correctly in the statistical analyses we perform in our laboratories. In this issue of the Journal, Stockl et al. (1) provide an interesting discussion of linear regression techniques in method comparison studies, pointing out that the quality of the data may be more important than the quality of the regression technique (e.g., ordinary linear regression vs Deming regression vs Passing-Bablock regression). In this Journal, the standard method for analyzing the data from a method comparison experiment has been to prepare a “comparison plot” that shows the test method results on the y -axis and the comparative method results on the x -axis, and then to calculate regression statistics to determine the best line of fit for the data. Different regression techniques may be appropriate, depending on the characteristics of the data—particularly the analytical range that is covered relative to the test values that are critical for medical applications. Elsewhere in the literature (2), there is a movement to discourage the use of regression analysis altogether and replace it with a simple graphical presentation of method comparison data in the …