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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)
Strengths and Challenges of Test-Negative Designs to Assess Post-Marketing Vaccine Effectiveness
Delphine Saragoussi, MD, MSc
Sanofi, France
Head of Pharmacoepidemiology Vaccines
Using Negative Outcome Control Studies to Assess Study Validity in the Real-World
David Pritchard, PhD
Target RWE, United States
Director, Data Management & Statistics
Traditions and Frontiers in Causal Methods - From the Rise and Fall of Propensity Scores to Causal AI for RWE
Andrew Wilson, PhD, MS
Parexel, United States
Head of Innovative RWD Analytics