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Session 9: Use of Real-World Data and Real-World Evidence in Safety
Session Chair(s)
James Buchanan, PHARMD
President, Covilance LLC, United States
This session will explore ways in which real-world data can be applied to questions that typically require the conduct of a randomized controlled clinical trial. In the absence of robust epidemiological data or appropriately sized control groups from clinical studies, it can be difficult to establish background rates for various adverse events of interest. However, real-world data can offer insight into these rates when used to create synthetic control groups. Causal inference techniques can be applied to observational data in an attempt to emulate a hypothetical pragmatic randomized trial. In addition, combining real-world evidence with clinical trial data can be a method to develop personalized benefit-risk assessments.
Learning Objective : - Identify ways in which real-world data can support safety surveillance programs
- Understand how causal inference models can evaluate safety questions for which no randomized controlled clinical data exist
- Describe the concept of a synthetic control group
- Discuss how real-world evidence and clinical trial data can be used to predict which patients stand to gain the most from therapy
Speaker(s)
Sara Lodi, PHD
Associate Professor, Biostatistics, Boston University School of Public Health, United States
An overview of target trial emulation and benchmarking
Tarek Hammad, MD, PHD, MS, MSC, FISPE
Vice President, Global Head of Medical Safety, Marketed Products and PDT, PSPV, Takeda Pharmaceuticals, United States
Use of Real-World Evidence in Personalized Benefit Risk Assessment
Israel Gutierrez, MD
Chief Medical Officer, TLR Therapeutics Inc, United States
RWD and Safety Signal Detection
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