Brenda Crowe is an Associate Vice President in Statistics at Eli Lilly and Company. She has more than 25 years of pharmaceutical industry experience. She obtained a PhD in Statistics from the University of Toronto. She has worked extensively with both clinical trials and observational studies. She has participated in or led several expert teams. As examples, she was a co-chair of the cross-industry Safety Planning, Evaluation and Reporting team, and served as the lead editor of the Council for International Organizations of Medical Sciences report on meta-analysis of safety data. She is a fellow of the American Statistical Association.

Heinz Schmidli works as an Executive Director in the Statistical Methodology group at Novartis, Basel, Switzerland. Since he joined the group in 2007, he contributes to the development of innovative approaches for the design and analysis of clinical trials, with a focus on Bayesian methods. In 2012 he received the Paul-Martini-Prize of the GMDS (jointly with Tim Friede), and in 2020 the Novartis distinguished scientist award.

Björn Bornkamp works in the Statistical Methodology Group at Novartis in Basel, where he provides consulting to statisticians and clinical teams on topics related to dose-finding, subgroup analyses, Bayesian statistics as well as estimands and causal inference.

Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He has supported the methodological development in various areas of drug development, including dose finding, estimands, multiple testing, and adaptive designs. He was a member of the ICH E9(R1) Expert Working Group on 'Estimands and sensitivity analysis in clinical trials' and currently serves on the ICH E20 Expert Working Group on 'Adaptive clinical trials'.

Dong Xi works as an Associate Director in the Statistical Methodology group at Novartis Pharmaceuticals. He provides consulting services to clinical teams and develops innovative statistical methodologies in multiple comparisons, adaptive design, dose finding, estimands and causal inference.