A novel digital twin strategy to examine the implications of randomized clinical trials for real-world populations

Randomized clinical trials (RCTs) guide medical practice; however, their generalizability across populations varies. We developed a statistically informed Generative Adversarial Network model, RCT-Twin-GAN, that leverages relationships between covariates and outcomes to generate a digital twin of an RCT conditioned on covariate distributions from a second patient population. We reproduced the disparate treatment effects of RCTs with similar interventions: the Systolic Blood Pressure Intervention Trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Blood Pressure Trial. To demonstrate treatment effects of each RCT conditioned on the other RCT population, we evaluated the cardiovascular event-free survival of SPRINT-Twins conditioned on the ACCORD cohort and vice versa. The digital twins demonstrated balanced treatment arms (mean absolute standardized mean difference (MASMD)) of covariates 0.019 (SD 0.018), and the ACCORD-conditioned covariates of the SPRINT-Twins distributed more similarly to ACCORD than SPRINT (MASMD 0.0082 SD 0.016 vs. 0.46 SD 0.20). Notably, SPRINT-conditioned ACCORD-Twins reproduced the non-significant outcome seen in ACCORD (0.88 (0.73-1.06) vs. 0.87 (0.68-1.13)), while ACCORD-conditioned SPRINT-Twins reproduced the significant outcome seen in SPRINT (0.75 (0.64-0.89) vs. 0.79 (0.72-0.86)). Finally, we applied this approach to a real-world population in the electronic health record. RCT-Twin-GAN simulates the translation of RCT-derived treatment effects across patient populations.