Dr. Kelly Van Lancker is a postdoctoral researcher in biostatistics at Ghent University, Belgium, where she also obtained her PhD. Previously, Kelly was a postdoctoral researcher at the Johns Hopkins Bloomberg School of Public Health. Her goal is to develop innovative designs and analytical techniques for drawing causal inferences in health sciences. A big part of her research focuses on more accurate and faster decision-making in randomized clinical trials by making optimal use of the available data. She thereby mainly focuses on covariate adjustment, data-adaptive methods, complex designs, estimands and especially a combination of these topics. Her recent research is primarily aimed at learning about the opportunities and challenges in running pragmatic trials within clinical practice, and developing better prediction tools for personalized medicine.

Ilya Lipkovich is a Sr. Research Advisor at Eli Lilly and Company. Ilya received his Ph.D. in Statistics from Virginia Tech in 2002 and has >20 years of statistical consulting experience in pharmaceutical industry. He is an ASA Fellow and published on subgroup identification in clinical data, analysis with missing data, and causal inference.

Martin Ho is an ASA Fellow, and a Senior Director of RWE Rare Disease, Evidence Generation Platform at Pfizer, leading all RWE activities for Sickle Cell Disease assets. Prior, he was the head of biostatistics at Google, LLC for 2 years and served the public at the U.S. FDA for 13 years, with the last 3 years as Associate Director of the Office of Biostatistics and Epidemiology at the Center for Biologics Evaluation and Research. Before joining the U.S. FDA, he worked as senior biostatisticians in contract research organizations for clinical studies. He co-led the RWE Scientific Working Group of the ASA Biopharmaceutical Section and the Working Group published 5 papers in landscape assessments as well as state of the science prospects of RWE for regulatory considerations. One of the papers introduces a casual inference roadmap for RWE design and analysis in regulatory context.

I am currently a statistician with Novartis based in Basel, Switzerland. I obtained my Bachelor’s degree in Statistics from the University of Michigan in my hometown of Ann Arbor and then a Ph.D. in Biostatistics from Harvard University in 2020. My doctoral dissertation focused on statistical methods for dealing with missing data when the “Missing at Random” assumption does not hold. My current work at Novartis focuses on leveraging causal reasoning in the pharmaceutical industry.

Stephen Ruberg, PhD spent 38 years in the pharmaceutical industry and is currently the founder and President of Analytix Thinking, a consulting company dedicated to advancing the use of statistics in the design of clinical trials, the conduct of analysis, and the interpretation of scientific data. His present interests are in estimands, subgroup identification, Bayesian statistics and digital medicine. He is a Fellow of the American Statistical Association, the International Statistics Institute and was a member of an advisory committee to the Secretary of Health and Human Services in the US federal government.

Yongming Qu is currently a Vice President at Eli Lilly and Company. He received his PhD in Statistics from Iowa State University in 2002. He has made significant contributions in all phases of clinical development at Lilly and has been active in research for using novel analytics and statistical methods in drug development. He is a Fellow of American Statistical Association.

Sean is a Principal Statistical Scientist at Roche, UK. Prior to this, he was a Research Associate at the MRC Biostatistics Unit, University of Cambridge, which is also where he obtained his PhD in biostatistics. At Roche, Sean works on establishing new endpoints in neuroscience, understanding the operating characteristics of treatments on clinical outcomes through characterizing drug concentration-response relationships and disentangling distinct pathways from treatment to outcomes via potential mediators, as well as on real world observational studies for making indirect treatment comparisons and estimating long-term treatment effects. He also supports the conduct of ongoing Phase 3 and post marketing requirement studies in multiple sclerosis as a study statistician, and collaborates with academics, external companies and multiple sclerosis registries in his projects. His research interests include longitudinal data, causal inference, missing data and composite endpoints.

Martin is a statistician with close to 15 years experience in the pharmaceutical industry and currently employed by Novo Nordisk A/S. He combines regular activities in clinical trials with an interest in statistical methodology, including the use of causal inference.