Using Real-World Cohorts To Assess The Generalizability And Relevance Of Randomized Clinical Trials (Rcts).

6540 Background: RCTs are the gold standard for understanding the efficacy of new treatments, however, patients (pts) in RCTs often differ from those treated in the real-world. Further, selecting a standard of care (SOC) arm is challenging as treatment options may evolve during the course of a RCT. Our objective was to assess the generalizability and relevance of RCTs supporting recent FDA approvals of anticancer therapies. Methods: RCTs were identified that supported FDA approvals of anticancer therapies (1/1/2016 - 4/30/2018). Relevant pts were selected from the Flatiron Health longitudinal, EHR-derived database, where available. Two metrics were calculated: 1) a trial’s pt generalizability score (% of real-world pts receiving treatment consistent with the control arm therapy for the relevant indication who actually met the trial's eligibility criteria) and 2) a trial’s SOC relevance score (% of real-world pts with the relevant indication and meeting the trial's eligibility criteria who actually received treatment consistent with the control arm therapy). All analyses excluded real-world pts treated after the relevant trial’s enrollment ended. Results: 14 RCTs across 5 cancer types (metastatic breast, advanced non-small cell lung cancer, metastatic renal cell carcinoma, multiple myeloma, and advanced urothelial) were included. There was wide variation in the SOC relevance and pt generalizability scores. The median pt generalizability score was 63% (range 35% - 88%), indicating that most real-world pts would have met the RCT eligibility criteria. The median SOC relevance score was 37% (range 15% - 74%), indicating that most RCT control arms did not reflect the way trial-eligible real-world pts in the US were actually treated. Conclusions: There is great variability across recent RCTs in terms of pt generalizability and relevance of SOC arms. Real-world data can be used to inform selection of control arms, predict impact of inclusion/exclusion criteria, and also assess the generalizability of the results of completed trials. Incorporating real-world data in planning and interpretation of prospective clinical trials could improve accrual and enhance relevance of RCT outcomes.