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

ABSTRACT

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.

KEYWORDS:

• Electronic health records
• real world data
• survival
• survival reconstruction
• trial
• weighting

Acknowledgments

Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award number R21CA227613 and P30CA016520. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Disclosure statement

RAH reports grant funding from Pfizer, Merck, and Johnson & Johnson unrelated to the content of this manuscript. RM reports having served as a consultant to Roche, Seattle Genetics/Astellas, and Flatiron Health (for speaking at a scientific conference on real-world data), and receiving research funds from Merck, unrelated to the content of this manuscript.

Additional information

Funding

This work was supported by the National Institutes of Health [P30CA016520,R21CA227613].