A total error approach for the validation of quantitative analytical methods.
OBJECTIVE
Typical acceptance criteria for analytical methods are not chosen with regard to the concept of method suitability and are commonly based on ad-hoc rules. Such approaches yield unknown and uncontrolled risks of accepting unsuitable analytical methods and rejecting suitable analytical methods. This paper proposes a formal statistical framework for the validation of analytical methods, which incorporates the use of total error and controls the risks of incorrect decision-making.


METHODS
A total error approach for method validation based on the use of two-sided beta-content tolerance intervals is proposed. The performance of the proposed approach is compared to the performance of current ad-hoc approaches via simulation techniques.


RESULTS
The current ad-hoc approaches for method validation fail to control the risk of incorrectly accepting unsuitable analytical methods. The proposed total error approach controls the risk of incorrectly accepting unsuitable analytical methods and provides adequate power to accept truly suitable methods.


CONCLUSIONS
Current ad-hoc approaches to method validation are inconsistent with ensuring method suitability. A total error approach based on the use of two-sided beta-content tolerance intervals was developed. The total error approach offers a formal statistical framework for assessing analytical method performance. The approach is consistent with the concept of method suitability and controls the risk of incorrectly accepting unsuitable analytical methods.
Hoffman
David
D
Preclinical and Research Biostatistics, Sanofi-aventis, Bridgewater, New Jersey, USA.
Kringle
Robert
R
D002623:Chemistry Techniques, Analytical / Q000592:standards*; D008962:Models, Theoretical