Ask the Expert Webinar Series

Improving Manufacturing Product Quality With Bayesian Computation in SAS® Viya®


On-Demand • Cost: Complimentary

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About the webinar

Biopharmaceutical manufacturing is no easy task, and the requirements to ensure product safety and quality are crucial parts of delivering safe and effective therapies to patients. But how does it happen? That’s where advanced analytics comes in.

Join this session to learn how global cell therapy leader Kite, a Gilead company, is applying advanced statistical methods in SAS Viya to improve manufacturing quality, safety and timelines. You’ll learn more about long-tail distributions and why this type of data is prevalent in the CAR T-cell therapy industry, how the organization calculates action limits via Bayesian tolerance intervals, and how Kite’s computations in SAS Viya were developed and implemented to improve operations and outcomes.

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About the experts


José G. Ramírez

Chief Statistician, Global MSAT, Kite (a Gilead company)

José is the Chief Statistician within the Global MSAT group at Kite, a Gilead company. He provides statistical leadership in the use, promotion and adoption of best statistical approaches, including Bayesian methods. In his many years of experience in the semiconductor, electronics and biotech industries, he has worked closely with engineers and scientists to help them make sense of data and, through collaborative education, help promote statistical thinking.

José received a licentiate degree in mathematics from Universidad Simón Bolívar in Caracas, Venezuela, and both an MS in applied statistics and a PhD in statistics from the University of Wisconsin-Madison, where he was one of the founding members of the Center for Quality and Productivity Improvement. He has won both the SAS Users Group International (SUGI) best contributed statistics paper and the SAS User Feedback Award, and has written two books for SAS Press.


Fang Chen, Ph.D.

Director, Advanced Analytics R&D, SAS

Fang Chen is a Director of Advanced Statistical Methods at SAS and a Fellow of the American Statistical Association. He manages the development of statistical software for SAS/STAT®, SAS/QC® and analytical components that drive SAS Visual Statistics software. Also among his responsibilities are the development of Bayesian analysis software and the MCMC procedure. He has a PhD in statistics from Carnegie Mellon University.

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