A novel digital twin strategy to enable the translation of evidence from randomized control trials to new populations

Abstract Background Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. Between the SPRINT and ACCORD trials, only SPRINT found a significant reduction in major cardiovascular events (MACE) with intensive blood pressure control, but ACCORD did not, without clear evidence of whether this reflected differences in enrolled patients. Purpose To use a novel RCT digital twin-based approach to demonstrate the translation of an RCT to a different population, leveraging the differences in populations and treatment effects in SPRINT and ACCORD. Methods We developed a statistically informed Generative Adversarial Network (GAN) model that leverages relationships between covariates and outcomes using Directed Acyclic Graphs (DAGs). This model can generate a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from a second patient population while maintaining these relationships (Fig A). Using this, we generated the (i) RCT-Twin of SPRINT, which was conditioned on the 10 most disparate covariates drawn from ACCORD, and (ii) an RCT-Twin of ACCORD conditioned on SPRINT. To demonstrate treatment effect estimates of each RCT conditioned on the other RCT’s patient population, we evaluated the cardiovascular event-free survival of 10 iterations of these cross-trained RCT-Twins against the original trials. Results Using 9361 SPRINT participants and conditioning on 4733 ACCORD participants, we generated 10 ACCORD-conditioned SPRINT-Twin datasets. We confirmed that the RCT-Twins were consistently balanced between treatment and controls across all measured covariates (mean absolute standardized mean difference (MASMD) 0.019, SD 0.018), as expected for real RCTs. We also confirmed that covariates of the ACCORD-conditioned SPRINT-Twins were closer to ACCORD than SPRINT (MASMD 0.0082 vs 0.46, SD 0.016 vs 0.20), suggestive of successful conditioning. Most importantly, across iterations, ACCORD-conditioned SPRINT-Twin datasets reproduced the overall non-significant effect size seen in ACCORD (5-year MACE hazard ratio (95% confidence interval) of 0.88 (0.73-1.06) in ACCORD vs median 0.87 (0.68-1.13) in the ACCORD-conditioned SPRINT-Twins, Fig B), while the SPRINT-conditioned ACCORD-Twin datasets reproduced the significant effect size seen in SPRINT (0.75 (0.64-0.89) vs median 0.79 (0.72-0.86) in the SPRINT-conditioned ACCORD-Twins, Fig C). Conclusions Our novel conditioned digital twin approach simulates RCT-derived effects in different patient populations by translating these effects to the covariate distributions of the patients. This key methodological advance may enable the direct translation of RCT-derived effects into disparate patient populations and may enable causal inference in real-world settings.