Bayesian and ICHQ2 and Q14
- Listen time: 18m
Synopsis:
The ICH-Q2 and ICH-Q14 together describe the development and validation activities proposed during the life cycle of an analytical method to assess the quality of medicinal products. But what is it that defines the quality of a reportable value? The podcast explores how Bayesian statistics makes it possible to take an observation made during the validation of the analytical procedure and predict the uncertainty around any future result, and in so doing bridge the gaps that exist in the guidelines.
The ICH-Q2 and ICH-Q14 together describe the development and validation activities proposed during the life cycle of an analytical method to assess the quality of medicinal products. But what is it that defines the quality of a reportable value? The podcast explores how Bayesian statistics makes it possible to take an observation made during the validation of the analytical procedure and predict the uncertainty around any future result, and in so doing bridge the gaps that exist in the guidelines.
Bio:
Bruno Boulanger, Ph.D., is Global Head Statistics and Data Science at PharmaLex, where he draws on his many years of experience in several areas of pharmaceutical research and industry including discovery, toxicology, CMC and early clinical phases. Bruno has authored or co-authored more than 100 publications in applied statistics. He organizes and contributes to Non-Clinical Statistics in Europe and, in 2010, set up the First Applied Bayesian Biostatistics conference.
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