Integrating real world data and clinical trial results using survival data reconstruction and marginal moment-balancing weights

Outcomes in electronic health records (EHR)-derived cohorts can be compared to similarly treated clinical trial cohorts to estimate the efficacy-effectiveness gap, the discrepancy in performance of an intervention in a trial compared to the real world. However, because clinical trial data may only be available in the form of published summary statistics and Kaplan-Meier curves, survival data reconstruction methods are needed to recreate individual-level survival data. Additionally, marginal moment-balancing weights can adjust for differences in the distributions of patient characteristics between the trial and EHR cohorts. We evaluated bias in hazard ratio (HR) estimates by comparing trial and EHR cohorts using survival data reconstruction and marginal moment-balancing weights through simulations and analysis of real-world data. This approach produced nearly unbiased HR estimates. In an analysis of overall survival for patients with metastatic urothelial carcinoma treated with gemcitabine-carboplatin captured in the nationwide Flatiron Health EHR-derived de-identified database and patients enrolled in a trial of the same therapy, survival was similar in the EHR and trial cohorts after using weights 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.