Statistical Considerations in Setting Quality Specification Limits Using Quality Data

Abstract

According to ICH Q6A (Specifications: test procedures and acceptance criteria for new drug substances and new drug procedures: chemical substances, (1999) [5]) Guidance, a specification is defined as a list of tests, references to analytical procedures, and appropriate acceptance criteria, which are numerical limits, ranges, or other criteria for the tests described. They are usually proposed by the manufacturers, and subject to the regulatory approval for use. When the acceptance criteria in product specifications cannot be pre-defined based on prior knowledge, the conventional approach is to use data of clinical batches collected during the clinical development phases. This interval may be revised with the accumulated data collected from released batches after drug approval. Dong et al. (J Biopharm Stat 25:317–327, 2015 [1]) discussed the statistical properties of the commonly used intervals and made some recommendations. However, in reviewing the proposed intervals, it is often difficult for the regulatory scientists to understand the difference between the intervals, when some intervals require only pre-specified target proportion of the distribution, and others require confidence level, in addition. Therefore, we propose to use the same confidence level of 95%, and calibrate each interval to the true coverage, under the tolerance interval setting. It is easy to show that the predictive interval and reference interval has the variable true coverage, and increases with the sample size, while tolerance interval covers the fixed true coverage. Based on our study results, we propose somesome appropriate statistical methods, in setting product specifications, to better ensure the product quality for the regulation purpose.