A Generalized Pivotal Quantity Approach to Analytical Method Validation Based on Total Error

The primary purpose of method validation is to demonstrate that the method is fit for its intended use. Traditionally, an analytical method is deemed valid if its performance characteristics such as accuracy and precision are shown to meet prespecified acceptance criteria. However, these acceptance criteria are not directly related to the method's intended purpose, which is usually a gurantee that a high percentage of the test results of future samples will be close to their true values. Alternate "fit for purpose" acceptance criteria based on the concept of total error have been increasingly used. Such criteria allow for assessing method validity, taking into account the relationship between accuracy and precision. Although several statistical test methods have been proposed in literature to test the "fit for purpose" hypothesis, the majority of the methods are not designed to protect the risk of accepting unsuitable methods, thus having the potential to cause uncontrolled consumer's risk. In this paper, we propose a test method based on generalized pivotal quantity inference. Through simulation studies, the performance of the method is compared to five existing approaches. The results show that both the new method and the method based on β-content tolerance interval with a confidence level of 90%, hereafter referred to as the β-content (0.9) method, control Type I error and thus consumer's risk, while the other existing methods do not. It is further demonstrated that the generalized pivotal quantity method is less conservative than the β-content (0.9) method when the analytical methods are biased, whereas it is more conservative when the analytical methods are unbiased. Therefore, selection of either the generalized pivotal quantity or β-content (0.9) method for an analytical method validation depends on the accuracy of the analytical method. It is also shown that the generalized pivotal quantity method has better asymptotic properties than all of the current methods.

Analytical methods are often used to ensure safety, efficacy, and quality of medicinal products. According to government regulations and regulatory guidelines, these methods need to be validated through well-designed studies to minimize the risk of accepting unsuitable methods. This article describes a novel statistical test for analytical method validation, which provides better protection for the risk of accepting unsuitable analytical methods.