Tools, Technologies and Training for Healthcare Laboratories

CLIA Final Rule

Part VI: Method Validation - Statistical Sense, Sensitivity, and Significance

Final5 CLIA Rule. Part VI:
Method Validation - Statistical Sense, Sensitivity, and Significance

With new requirements for method validation, laboratories have to become smarter about their use of statistics and experiments. Dr. Westgard examines the best uses and the proper interpretations of statistics and experiments in method validation. (preview)

April 24, 2003

The use of statistics in method validation studies is still a major problem in laboratories today. Given the Final CLIA Rule's requirement for method validation studies on all non-waived methods introduced after April 24, 2003, analysts need guidance and training on the use and application of statistics in method validation studies.

State of the art

One barometer of current method validation practices is found in the current issue of MLO (April 2003), where the problem of using statistics in a comparison of methods study is discussed (see Answering your questions column and question about comparing two analyzers) [1]. What's the best way to analyze the data - correlation coefficient, regression statistics (slope, y-intercept, standard error about the line), t-test statistics (t-value, bias, SD of the differences), or the F-test? While it is not difficult to make statistical calculations today because of the availability of statistics packages on personal computers, people still have difficulties in making sense of the calculated statistics.

Thirty years ago we investigated the issue of proper use and interpretation of statistics in the method comparison experiment [2]. The findings are as relevant now as then because the statistical skills of laboratory managers and analysts have not improved.


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