[Webinar 1: 6th December | Webinar 2: 14th December]
This two-day series of webinars on causal inference in clinical trials will introduce the key ideas of the topic and aim to illustrate how these are of practical value not purely academic interest.
The event will be structured as two webinars in consecutive weeks*, each of 2.5 hours. In registering here, you are registering for both as the link to join via Zoom contains the details for both occurrences.
The first webinar will provide an introduction to causal inference ideas and methods and how these relate to the estimand framework in both the setting of RCTs or real world data. Graphical method for communicating causal networks such as with single world intervention graphs will also be outlined.
The second webinar is aimed at illustrating real practical applications in drug development with use in regulatory settings or scientific exchange. Three case studies will be presented covering examples of how such ideas can provide valuable understanding of the effects of treatments in the presence of intercurrent events such as discontinuation or treatment switching or where effects may be mediated by intermediate factors.
Who is this event intended for? Statisticians with an interest in understanding causal inference methods or in applying these to practical situations in drug development
What is the benefit of attending? To understand the potential of the practical application of causal inference methods in drug development and be able to apply these to real world problems or clinical trials.
Dates & Timings
Part 1 - Wednesday 6th December
Part 2 - Thursday 14th December
13:30-16:00 GMT | 14:30-17:00 CET (both days)
*Please note: This is a two-part webinar and delegates will be required to attend Part 1 in order to attend Part 2. Upon joining the Zoom Webinar, you will be prompted to 'register', by entering your name & email. This will then save both webinar occurrences to your calendar.
Dec 6, 2023
Dec 14, 2023
1:30 PM - 4:00 PM
Day 1: Introduction to the Use of Causal Inference in Drug Development
o Kelly van Lancker (Ghent University) - The Role of Causal Inference in Clinical Trials: An Introductiono Ilya Lipkovich (Eli Lilly) - Causal Inference and Estimands in Clinical Trialso Mar...
o Kelly van Lancker (Ghent University) - The Role of Causal Inference in Clinical Trials: An Introduction o Ilya Lipkovich (Eli Lilly) - Causal Inference and Estimands in Clinical Trials o Martin Ho (Pfizer) - A Causal Inference Roadmap for Generating RWE in Regulatory Context: An Introduction and Illustration o Alex Ocampo (Novartis) - Single-World Intervention Graphs for Defining, Identifying, and Communicating Estimands in Clinical Trials
o Stephen Ruberg (Analytix thinking) / Yongming Qu (Eli Lilly) - Estimating Treatment Effects in Patients Who Adhere to Treatmento Sean Yiu (Roche) - Comparative safety analysis of time-vary...
o Stephen Ruberg (Analytix thinking) / Yongming Qu (Eli Lilly) - Estimating Treatment Effects in Patients Who Adhere to Treatment o Sean Yiu (Roche) - Comparative safety analysis of time-varying exposures in post marketing observational studies o Martin Linder (Novo Nordisk) - Mediation analysis for a cardiovascular outcome trial o Panel Discussion with all speakers from both days
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.
Sr. Research Advisor, Real World Analytics at Eli Lilly
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.
Distinguished Statistical Scientist, ASA Fellow at Analytix Thinking, LLC
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.