Structured use of the median in the analytical measurement process

In the pharmaceutical industry, the process of measuring a product's attributes can be very complicated and the potential for an analytical mistake can be quite high. Often, an unexpected result leads to an investigation to assess the possibility that a mistake was made in the laboratory. Traditionally, the data generated in these investigations has been used, along with various outlier tests, to attempt to negate the original data. Sometimes, historical estimates of the S.D. of the analytical method are not available for use in outlier testing and the power of the outlier tests to detect true mistakes without such historical estimates is often very low due to the small amount of data available. This leads to a great deal of inconsistency in the amount of data that is further generated and how the data is ultimately handled in making a decision. Recently, FDA demands for consistent and objective laboratory investigations have raised concerns about these practices. An alternative approach, involving a systematic investigation strategy and data handling via the structured use of the median, is proposed in this paper. The operating characteristics of the traditional and proposed approaches are compared to show their similarity and the advantages of the proposed approach. It is strongly believed by the authors that the structured use of the median will lead to more consistent investigations and data handling, which will benefit industry, the FDA and ultimately, the consumer, by allowing more accurate decisions to be made more efficiently.