Quantitative Benefit-risk in the Current Regulatory Environment and the Implications for Clinical Statisticians
Track
:
Track 15: Statistical Science and Quantitative Thinking
Program Code:
320
Date:
Wednesday, June 26, 2013
Time:
8:00 AM to 9:30 AM
EST
Location:
157AB
CHAIR
:
Susan P. Duke, MS (SCHNON), Manager, Benefit/Risk Evaluation, Global Clinical Safety & Pharmacovigilance, GlaxoSmithKline, United States
Susan has been active in quantitative benefit risk evaluation since her move to GSK's pharmacovigilance department in 2011. She also has developed expertise in statistical graphic design, including membership on FDA/Industry/Academia Safety Graphics team, leading their General Principles subgroup.
SPEAKER
(S):
Conny Berlin, MS (SPKNON), Global Head, Quantitative Safety Function, Novartis Pharma AG, Switzerland
Conny Berlin leads the Quantitative Safety Function at Novartis Pharma AG. Her responsibilities include promoting a quantitative safety culture which enables evaluation, understanding and communication of safety data and benefit-risk to support decision-making at all stages of the drug life-cycle.
Jonathan D. Norton, MS (SPKNON), Mathematical Statistician, Division of Biometrics V, OB, OTS, CDER, FDA, United States
Dr. Jon Norton is a Mathematical Statistician with the oncology division in CDER. After working at the University of Arkansas medical school, he earned his PhD at Florida State. Since joining FDA in 2008 he has advocated the use of quantitative and graphical benefit-risk methods for drug review.
Scott Evans, PhD (SPKSUP), Senior Research Scientist, Harvard University School of Public Health, United States
Dr. Evans is a Senior Research Scientist at Harvard University where he teaches clinical trials. He is a Fellow of the American Statistical Association, is a member of an FDA Advisory Committee, and has served/chaired numerous Data Monitoring Committees and Scientific Advisory Committees.
Description
Benefit-risk in clinical development is increasingly important to statisticians, as interest from regulators in more formal methods increases. We will describe decision science and statistical approaches, and what more statisticians can do in this area.