Causal Transfer Learning Using Negative Control Outcomes: Safe Improvement of the Power of RCT using RWD

Music City Center 

Randomized controlled trials (RCTs) are considered the gold standard for evaluating the relative effectiveness of interventions. However, evidence from RCTs alone could be limited due to lack of generalizability, small/moderate sample size, and limited follow up time. Recently, several methods have been developed to integrate RCTs with real-world data (RWD). Some of existing data integration methods have not account for the potential biases introduced by observational data, due to lack of randomization, unmeasured confounding, missingness, and selection biases. In this talk, we present a novel strategy to leverage a rich set of negative control outcomes to safely calibrate the estimate that combines the evidence from RCT and RWD, while mitigate the impacts of the biases. This approach offers a promising solution for enhancing the validity and reproducibility of evidence generated by integrating RWD with RCTs.