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

Abstract

Clinical trials with a hybrid control arm (a control arm constructed from a combination of randomized patients and real-world data on patients receiving usual care in standard clinical practice) have the potential to decrease the cost of randomized trials while increasing the proportion of trial patients given access to novel therapeutics. However, due to stringent trial inclusion criteria and differences in care and data quality between trials and community practice, trial patients may have systematically different outcomes compared to their real-world counterparts. We propose a new method for analyses of trials with a hybrid control arm that efficiently controls bias and type I error. Under our proposed approach, selected real-world patients are weighted by a function of the "on-trial score," which reflects their similarity to trial patients. In contrast to previously developed hybrid control designs that assign the same weight to all real-world patients, our approach upweights of real-world patients who more closely resemble randomized control patients while dissimilar patients are discounted. Estimates of the treatment effect are obtained via Cox proportional hazards models. We compare our approach to existing approaches via simulations and apply these methods to a study using electronic health record data. Our proposed method is able to control type I error, minimize bias, and decrease variance when compared to using only trial data in nearly all scenarios examined. Therefore, our new approach can be used when conducting clinical trials by augmenting the standard-of-care arm with weighted patients from the EHR to increase power without inducing bias.

Testing for Treatment Effect Twice Using Internal and External Controls in Clinical Trials

“…There are numerous opportunities in how the integration of RCTs and RWD can achieve fruitful results that using either RCT or RWD alone can not (Colnet et al, 2020;Degtiar and Rose, 2021;Shi et al, 2021). Among those, an important theme is on augmenting the RCT with RWD to increase efficiency (Yang et al, 2020a,b;Gagnon-Bartsch et al, 2021;Chen et al, 2021;Cheng and Cai, 2021;Li and Luedtke, 2021), and particularly, constructing an externally augmented control arm in the analysis of RCTs (Li et al, 2020;Harton et al, 2021;Gao et al, 2021;Liu et al, 2022).…”