Dr Francq will discuss the need for analytical methods to deliver unbiased and precise results and talk in detail on confidence, prediction and tolerance intervals work in linear mixed models and the interpretation of statistical results.

This will be followed by Q&A.

Who is this event intended for? Statisticians and others working on assay qualification within the Pharmaceutical Industry.

What is the benefit of attending? Attendees will learn about robust assay qualification methodology.

14:00 BST | 15:00 CEST

In the pharmaceutical industry, all analytical methods must be shown to deliver unbiased and precise results. In an assay qualification or validation study, the trueness, accuracy and intermediate precision are usually assessed by comparing the measured concentrations to their nominal levels. Trueness is assessed by using confidence intervals of mean measured concentration, accuracy by prediction intervals for a future measured concentration, and the intermediate precision by the total variance.

ICH and USP guidelines alike request that all relevant sources of variability must be studied, e.g. the effect of different technicians, the day-to-day variability or the use of multiple reagent lots. Those different random effects must be modeled as crossed, nested or a combination of both.

Confidence, prediction and tolerance intervals in linear mixed models will be detailed with a focus on the interpretation of statistical results. Their relationships will be discussed together with the POOS (out-of-specification probability). Two real datasets from assay validation study during vaccine development are used to illustrate the statistical intervals and the POOS.