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Session 7: Methodological Insights on Aspects of Non-Interventional Studies
Session Chair(s)
John Concato, MD, MPH, MS
Associate Director for Real-World Evidence Analytics, OMP, CDER, FDA, United States
This session will highlight specific methodological issues relevant to using real-world data to generate real-world evidence. The first presentation will discuss the history and evolving landscape of causal methods including propensity scores, g-computation, and methods based on machine learning. The second presentation will discuss the test-negative design derived from the case-control study design. The third presentation will discuss negative control outcome studies designed to assess comparability of potential treatments groups. The third presentation will discuss a structured framework for sensitivity analyses to assess unmeasured confounding. A panel discussion will follow.
Learning Objective : At the conclusion of this session, participants should be able to:- Summarize basic concepts, applications, and methods of causal methods
- Describe advantages and pitfalls of studies using negative control outcomes
- Identify major methodological challenges when planning or evaluating a test-negative study design
Speaker(s)
Delphine Saragoussi, MD, MSC
Head of Pharmacoepidemiology Vaccines, Sanofi, France
Strengths and Challenges of Test-Negative Designs to Assess Post-Marketing Vaccine Effectiveness
David Pritchard, PHD
Director, Data Management & Statistics, Target RWE, United States
Using Negative Outcome Control Studies to Assess Study Validity in the Real-World
Andrew Wilson, PHD, MS
Head of Innovative RWD Analytics, Parexel, United States
Traditions and Frontiers in Causal Methods - From the Rise and Fall of Propensity Scores to Causal AI for RWE
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