Online Causal Inference Seminar

Free and open to the public.

Tuesday, February 7, 2023 [Link to join] (ID: 996 2837 2037, Password: 386638)

• Speaker: Lauren Dang (w/ Maya Peterson, UC Berkeley)
• Title: Integration of Observational and Randomized Controlled Trial Data: Approaches, Challenges, A Novel Estimator, and Application to the LEADER Cardiovascular Outcomes Trial
• Discussant: Robin Evans (University of Oxford)
• Abstract: Although the randomized controlled trial (RCT) is the gold standard for evidence generation, conducting an adequately powered RCT is not always feasible or desirable. A traditional RCT may be impracticable for very rare diseases, and excessive randomization to control may be considered unethical for severe diseases without effective treatments or for certain pediatric drug approvals. In such cases, we may wish to integrate data from a small RCT with real-world data (RWD) to increase power but at the risk of introducing bias. A growing number of “data fusion” methods seek to estimate the bias from incorporating RWD to determine whether to include RWD or how to weight the RWD in a combined analysis. This talk will use a roadmap for causal inference to explore the challenges of integrating observational and RCT data, including considerations for designing such a hybrid trial. We will discuss different approaches to data fusion, including a novel estimator that uses cross-validated targeted maximum likelihood estimation (CV-TMLE) to data-adaptively select and analyze the optimal experiment - RCT only (if no unbiased external data exists) or RCT with external data. Finally, we will discuss an example of distinguishing biased versus unbiased extra controls by region in an analysis of the effect of liraglutide on change in hemoglobin A1c from the LEADER trial.