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Communication Strategies. Case Studies. Applied Knowledge.
Hear from leaders around the globe as they share insights about their experiences and lessons learned throughout their certification journey.
The evolving regulatory landscape presents both opportunities and challenges, centered around the use and analytics of real-world data (RWD). We’ll discuss, through 4 presenters, this evolving landscape, a unified approach to analysis (targeted learning), a worked example (using R), and finally challenges and future directions for real-world data.
January 9, 2023 – Apr 30, 2023: Early Bird $285 Member | $335 Nonmember
May 1, 2023 – June 1, 2023: Regular $335 Member | $395 Nonmember
Topics should interest a broad spectrum of people involved in regulatory, statistical, epidemiological, and innovative real-world data/evidence fields.
Regulatory affairs professionals involved in clinical trials, biologics and devices.
Dr. Mark van der Laan
Dr. Mark van der Laan is the Jiann-Ping Hsu/Karl E. Peace Professor in Biostatistics and Statistics at the University of California, Berkeley. His research interests include censored data, causal inference, genomics, observational studies, and adaptive designs. Mark has led the development of two general statistical approaches: Super Learning and Targeted Learning. Targeted Learning improves on typical current statistical practice by avoiding reliance on wrong model assumptions and its capability to target any question of interest. In 2005 Mark was awarded the Committee of Presidents of Statistical Societies (COPSS) Presidential Award in recognition of outstanding contributions to the statistics profession. He also received the 2004 Spiegelman Award and the 2005 van Dantzig Award. He co-founded the International Journal of Biostatistics and the Journal of Causal Inference. Mark has authored various books on Targeted Learning, Censored Data, and Multiple Testing, published over 400 publications, and mentored 60 Ph.D. students and 30 postdoctoral fellows.
Dr. Susan Gruber
Dr. Susan Gruber is the co-founder of TL Revolution and founder of Putnam Data Sciences, a statistical consulting and data analytics firm. She is a biostatistician and computer scientist who focuses on developing and applying data adaptive methodologies to improve the quality of real-world evidence generated by healthcare studies incorporating real-world data. Throughout her career, Dr. Gruber has prioritized the advancement of scientific activities at the FDA. She served as the Senior Director of the IMEDS Methods Research Program for the Reagan- Udall Foundation for the FDA. As Director of the Biostatistics Center in the Department of Population Medicine at Harvard Pilgrim Health Care Institute, Dr. Gruber began collaborating on FDA Sentinel Program projects to evaluate methodologies for causal effect estimation in a distributed data network. She is currently working to improve the usability and utility of TMLE-based software in support of regulatory decision-making.
Dr. Yuwei Zhang
Dr. Yuwei Zhang has 20 years of experience leveraging real-world data (RWD), and is well-versed in the healthcare ecosystem, including providers, payers, pharma, and advocacy groups. She has supported successful launches of drugs with expertise in data strategy and competitive intelligence. Her paper using RWD is one of the top five most-cited papers in the field. She has experience engaging with health authorities in the US and China. She served at pharmaceutical companies in various roles and used RWD to support Medical, Commercial, Clinical, and Regulatory. She also worked as a consultant for pharmaceutical companies in China.
Yuwei regularly gives talks at universities and has been invited to review manuscripts for top-tier medical journals, grant proposals, and books. At Parexel, she explores the frontiers of innovative RWD analytics and leverages data and digital innovation to deliver patient-centered care.
Dr. Andy Wilson
Dr. Andy Wilson is the Head of Innovative RWD Analytics at Parexel. Andy is an experienced Public Health and Epidemiology professional with 15 years of experience in Real World Data applications for late-phase clinical research and observational studies. He worked as a statistician and biostatistician for ten years before joining the Clinical Research Industry. Significant publication list and collaborations advancing innovation and adoption of new methods in reproducibility and transparency in research, causal inference, and machine learning. Andy teaches statistics and causal methods in public health at the University of Utah.
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