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Clinical trials with a hybrid control arm (a control arm constructed from acombination of randomized patients and real-world data on patients receivingusual care in standard clinical practice) have the potential to decrease thecost of randomized trials while increasing the proportion of trial patientsgiven access to novel therapeutics. However, due to stringent trial inclusioncriteria and differences in care and data quality between trials and communitypractice, trial patients may have systematically different outcomes compared totheir real-world counterparts. We propose a new method for analyses of trialswith a hybrid control arm that efficiently controls bias and type I error.Under our proposed approach, selected real-world patients are weighted by afunction of the "on-trial score," which reflects their similarity to trialpatients. In contrast to previously developed hybrid control designs thatassign the same weight to all real-world patients, our approach upweights ofreal-world patients who more closely resemble randomized control patients whiledissimilar patients are discounted. Estimates of the treatment effect areobtained via Cox proportional hazards models. We compare our approach toexisting approaches via simulations and apply these methods to a study usingelectronic health record data. Our proposed method is able to control type Ierror, minimize bias, and decrease variance when compared to using only trialdata in nearly all scenarios examined. Therefore, our new approach can be usedwhen conducting clinical trials by augmenting the standard-of-care arm withweighted patients from the EHR to increase power without inducing bias.

Combining Real-World and Randomized Control Trial Data Using Data-Adaptive Weighting via the On-Trial Score