Event

PSI Pre-Clinical SIG Webinar: Assay Qualification by Linear Mixed Model: Confidence, Prediction & Tolerance Intervals

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Date: Tuesday 21st June 2022
Time: 14:00-15:00 BST | 15:00-16:00 CEST
Speaker: Bernard Francq

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.

Registration

You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = Free of charge
To register for the session, please click here.

Overview

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.

Speaker details

Speaker

Biography

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Bernard Francq

Bernard G Francq is Lead Statistician with GSK Biologicals, driving statistical innovation for CMC projects worldwide. He holds a PhD in Statistics (UCLouvain, 2013).

His work on errors-in-variables (EIV) regressions in method comparison studies has been awarded Best MSc Thesis Biostatistics (Quételet 2008, Belgium), Best Chemometrician Prize (Chimiométrie 2009, Paris) and Best Young Researcher (Agrostat 2012, Paris). His communication skills have been recognized with the Greenfield Challenge Award (ENBIS 2012, Ljubljana). Recent work on tolerance intervals in bridging studies was awarded Best GSK Statistical Paper (2020). He lectures at UCLouvain and regularly offers trainings to statisticians in the (bio)pharmaceutical industry.

 

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