TITLE: Estimand in Real-world Setting and Targeted Learning in Generating and Evaluating Real-world Evidence
INSTRUCTOR: Jie Chen, Overland Pharmaceuticals and Susan Gruber, Putnam Data Sciences


Constructing the right estimand—the target of estimation—which reflects the research question and the study objective, is one of the key components in formulating a clinical study. ICH E9(R1) describes statistical principles for constructing estimands in clinical trials with a focus on five attributes—population, treatment, end- points, intercurrent events, and population-level summary. However, defining estimands for clinical studies using real-world data (i.e., real-world evidence studies) requires additional considerations due to, for example, heterogeneity of study population, complexity of treatment regimes, different types and patterns of intercurrent events, and complexities in choosing study endpoints. In the first half of this tutorial, we will review the essential components of estimands and causal inference framework, discusses considerations in constructing estimands for real-world evidence (RWE) studies, highlights similarities and differences in traditional clinical trial and RWE study estimands, and provides a roadmap for choosing appropriate estimands for RWE studies.
Targeted Learning (TL) provides a template for efficient learning from data. TL can improve the quality of real-world evidence (RWE) generated from clinical studies using real-world data (RWD) and help assess the level of support for sound decision-making. The TL estimation roadmap is a step-by-step guide for causal effect estimation that produces a rich trove of information for assessing whether the RWD are adequate to address selected study questions, and whether study findings provide robust scientific evidence. In the second half of the tutorial, we will present the roadmap and use case studies to showcase its utility for developing a statistical analysis plan, generating RWE, and evaluating results from prior studies.

Instructors’ Biography:

Dr. Jie Chen is Chief Scientific Officer at Elixir Clinical Research and Senior Vice President and head of Biometrics at Overland Pharmaceuticals. Before joining Elixir and Overland, Jie was a distinguished Scientist in the Biostatistics and Research Decision Sciences at Merck Research Laboratories (US). He also worked as a senior global group head in several multi-national biopharmaceutical companies. Jie has over 27 years of experience in biopharmaceutical R&D and has been invited to give short courses at national or international statistical conferences. He is a co-chair for the phase III projects of the American Statistical Association (ASA) Real-World Evidence Scientific Working Group. Jie has co-authored a book on Medical Product Safety Evaluation: Biological Models and Statistical Methods (with Heyse and Lai) and published over 40 papers in peer-reviewed statistical journals. He is an elected Fellow of the ASA and a visiting member of the Center for Innovative Study Design, Stanford University.

Dr. Susan Gruber, PhD, MPH, MS, is the founder of Putnam Data Sciences, a statistical consulting and data analytics consulting firm. Her work focuses on the development and application of data-adaptive methodologies for improving the quality of evidence generated by observational and randomized health care studies. Prior to forming Putnam Data Sciences, Dr. Gruber was the Director of the Biostatistics Center in the Department of Population Medicine at Harvard Pilgrim Health Care and Harvard Medical School, and former Senior Director of the IMEDS Methods program at the Reagan Udall Foundation for the FDA.

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