Integrating Real-World Data and Clinical Trial Results Using Survival Data Reconstruction and Marginal Moment-Balancing Weights

• Activity Number: 476 - Beyond Propensity Scores: Novel Techniques in Utilizing Real-World Data in Clinical Trials
• Type: Topic Contributed
• Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
• Sponsor: ENAR
• Author(s): Kylie Getz* and Ronac Mamtani and Rebecca Hubbard
• Companies: Rutgers School of Public Health and University of Pennsylvania and University of Pennsylvania
• Keywords: electronic health records; real world data; survival; survival reconstruction; weighting; clinical trial

Abstract

The efficacy-effectiveness gap describes the discrepancy between performance of an intervention in a trial and the real world. To estimate this gap using clinical trial and real-world data (RWD), two challenges must be addressed: obtaining individual-level trial data from published results, often presented as KM curves and summary statistics; and addressing confounding by differences in patient characteristics between trial and real-world populations. Using simulation studies and an analysis of RWD for metastatic urothelial cancer (mUC), we evaluated bias in hazard ratio (HR) estimates comparing trial and RWD cohorts using survival data reconstruction and marginal moment-balancing weights to address these challenges. Our pipeline for estimating the efficacy-effectiveness gap produced nearly unbiased HR estimates and demonstrated that overall survival was similar for trial and RWD mUC cohorts after weighting to balance age, sex, and performance status (HR = 0.93, 95% confidence interval (0.74, 1.18)). Overall, we conclude that this approach is feasible for comparison of trial and EHR cohorts and facilitates evaluation of outcome differences between trial and real-world populations.