May 6, 2021
8:15 AM – 12 :15 PM Pacific Time

Real-World Data and Evidence in Drug Development and Regulatory Submission:

Statistical Considerations

Richard Baumgartner, Merck Research Laboratories

Jie CHEN, Overland Pharmaceutics, Inc. and CISD of Stanford University

Tze Leung LAI, Stanford University

  There has been a growing interest in using real-world data (RWD) and evidence (RWE) in drug development and regulatory science since the passage of the 21st Century Cures Act in December 2016. The US Food and Drug Administration released the Framework for FDA’s Real-World Evidence Program in December 2018 and subsequently issued in May 2019 a draft guidance for industry on Submitting Documents Using Real-World Data and Real-World Evidence to FDA for Drugs and Biologics. The recently issued RWE guidance by China National Medical Product Administration (NMPA) encourage sponsors to use RWD and RWE in clinical trial design and analysis and regulatory submission. This short course will discuss the evolving field of RWD and RWE with the focus on how RWD and RWE can be more efficiently used in the design and analysis of clinical trials, statistical and causal inference methodologies for generating RWE from RWD for regulatory submissions, and challenges and opportunities in using RWD and RWE in the regulatory setting.  

Outline of Topics
  1. Introduction
  2. Real-world data and clinical trial data
  3. Finding “fit-for-purpose” real-world data
  4. “Substantial evidence” to support regulatory decision
  5. Estimands in real-world data and evidence
  6. Control for confounding variables in the design and analysis of clinical trials
  7. External data to serve a control group for single-arm trials
  8. External data to augment concurrent control group in randomized trials
  9. Design and analysis of Pragmatic clinical trials
  10. Other considerations in the design and analysis of clinical trials using external data
  11. Causal inference frameworks for real-world evidence generation
  12. Regulatory considerations in using real-world data and evidence for decision-making
  13. Best practice in using real-world data and evidence
  14. Challenges and opportunities


Richard Baumgartner

Merck Research Laboratories

james Dr. Baumgartner is a Sr. Principal Scientist with Biometrics Research Department, Biostatistics and Research Decision Sciences (BARDS), Merck and Co. While at Merck, he has been supporting early clinical as well as preclinical studies with imaging component including functional Magnetic Resonance Imaging (fMRI), dynamic contrast-enhanced MRI (DCE-MRI) and Positron Emission Tomography (PET) imaging for neuroscience, inflammation and cardiovascular therapeutic areas. Currently he is also involved in several projects in Real World Data (RWD) space and he is works as a core member of the BARDS RWD working group. Previously he was Associate Research Officer with the Institute for Biodiagnostics, National Research Council Canada in Winnipeg, Canada, where he pioneered development of methods for exploratory analysis of fMRI. At the Institute for Biodiagnostics, he also worked on metabolomics applications to develop diagnostic biomarkers for prediction of pathogenic fungi and breast cancer.


Overland Pharmaceutics, Inc. and CISD of Stanford University

james Jie is Senior Vice President and head of Biometrics, Overland Pharmaceuticals and a visiting member of the Center for Innovative Study Design, Stanford University. Before joining Overland in 2020, Jie was a distinguished Scientist in biostatistics at Merck Research Laboratories (US). He also worked as a global group head and/or senior director in several multi-national biopharmaceutical companies including AstraZeneca, Merck Serono, and Novartis. Jie has over 25 years of experience in biopharmaceutical R&D and has been invited to give short courses at ASA Regulatory-Industry Statistics Workshops and EMA statistics symposium and deliver invited / keynote speeches at national or international conferences. He is a member of the editorial boards for the Contemporary Clinical Trials and the Journal of Biopharmaceutical Statistics and also a co-lead for one of the ASA RWE Scientific Working Group sub-teams. 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 statistics journals. He is a Fellow of the ASA.

Jie received a medical degree from Shanghai First College of Medicine (now Fudan University School of Public Health) and a Ph.D. in statistics from Temple University, Philadelphia.

Tze Leung LAI

Stanford University

james Tze Leung LAI is the Ray Lyman Wilbur Professor of Statistics and, by courtesy, of Biomedical Data Science and of the Institute of Computational and Mathematical Engineering (ICME) at Stanford University. He is also Co-director of the Center for Innovative Study Design (CISD) at the Stanford University School of Medicine. He has supervised 76 Ph.D. theses and seven postdoctoral trainees. He has published over 300 papers, many of which are in clinical trial design and analysis, population pharmacokinetics and pharmacodynamics, survival analysis, longitudinal data analysis, multiple endpoints, biomarkers, adaptive methods, sequential analysis and time series.