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Points of Care in Using Statistics in Method Comparison Studies

Department of Pathology, and Laboratory Medicine, University of Wisconsin, Medical School, Room D4/237, 600 Highland Avenue, Madison, WI 53792, Fax 608-263-1568, E-mail jo.westgard@hosp.wisc.edu

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, Stöckl 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 . . . [Full Text of this Article]

Point 1: Use statistics to provide estimates of errors, not as indicators of acceptability.

Point 2: Recognize that the main purpose of the method comparison experiment is to obtain an estimate of systematic error or bias.

Point 3: Obtain estimates of systematic error at important medical decision concentrations.

Point 4: When there is a single medical decision concentration, make the estimate of systematic error near the mean of the data.

Point 5: When there are two or more medical decision concentrations, use the correlation coefficient, r, to assess whether the range of data is adequate for using ordinary regression analysis.

Point 6. When r is high, use the comparison plot along with ordinary linear regression statistics.

Point 7: When r is low, improve the data or change the statistical technique.

Point 8: When r is low and a difference plot is used, calculate t-test statistics to provide a quantitative estimate of SE.

Point 9: When in doubt about the validity of the statistical technique, see whether the choice of statistics changes the outcome or decision on acceptability.

Point 10: Plan the experiment carefully and collect the data appropriate for the statistical technique to be used.

References

The following articles in journals at HighWire Press have cited this article:

				J. O. Westgard Use and Interpretation of Common Statistical Tests in Method Comparison Studies Clin. Chem., March 1, 2008; 54(3): 612 - 612. [Full Text] [PDF]

				M. M. Flanders, R. Crist, S. Safapour, and G. M. Rodgers Evaluation and Performance Characteristics of the STA-R Coagulation Analyzer Clin. Chem., September 1, 2002; 48(9): 1622 - 1624. [Full Text] [PDF]

				R. F. Martin General Deming Regression for Estimating Systematic Bias and Its Confidence Interval in Method-Comparison Studies Clin. Chem., January 1, 2000; 46(1): 100 - 104. [Abstract] [Full Text] [PDF]