Background: Randomized clinical trials (RCTs) are designed to produce evidence in selected populations. Assessing their effects in the real-world is essential to change medical practice, however, key populations are historically underrepresented in the RCTs. We define an approach to simulate RCT-based effects in real-world settings using RCT digital twins reflecting the covariate patterns in an electronic health record (EHR). Methods: We developed a Generative Adversarial Network (GAN) model, TwinRCT-GAN, which generates a digital twin of an RCT (TwinRCT) conditioned on covariate distributions from an EHR cohort. We improved upon a traditional tabular conditional GAN, CTGAN, with a loss function adapted for data distributions and by conditioning on multiple discrete and continuous covariates simultaneously. We assessed the similarity between a Heart Failure with preserved Ejection Fraction (HFpEF) RCT (TOPCAT), a Yale HFpEF EHR cohort, and TwinRCT. We also evaluated cardiovascular event-free survival stratified by Spironolactone (treatment) use. Results: By applying TwinRCT-GAN to 3445 TOPCAT participants and conditioning on 3445 Yale EHR HFpEF patients, we generated TwinRCT datasets between 1141-3445 patients in size, depending on covariate conditioning and model parameters. TwinRCT randomly allocated spironolactone (S)/ placebo (P) arms like an RCT, was similar to RCT by a multi-dimensional distance metric, and balanced covariates (median absolute standardized mean difference (MASMD) 0.017, IQR 0.0034-0.030). The 5 EHR-conditioned covariates in TwinRCT were closer to the EHR compared with the RCT (MASMD 0.008 vs 0.63, IQR 0.005-0.018 vs 0.59-1.11). TwinRCT reproduced the overall effect size seen in TOPCAT (5-year cardiovascular composite outcome odds ratio (95\% confidence interval) of 0.89 (0.75-1.06) in RCT vs 0.85 (0.69-1.04) in TwinRCT). Conclusions: TwinRCT-GAN simulates RCT-derived effects in real-world patients by translating these effects to the covariate distributions of EHR patients. This key methodological advance may enable the direct translation of RCT-derived effects into real-world patient populations and may enable causal inference in real-world settings.

The authors are coinventors of a provisional patent related to the current work (63/606,203). EKO is a co-inventor of the U.S. Patent Applications 63/508,315 & 63/177,117, a cofounder of Evidence2Health (with RK), and has previously served as a consultant to Caristo Diagnostics Ltd (outside the present work). RK is an Associate Editor of JAMA. He receives support from the Doris Duke Charitable Foundation (under award, 2022060). He also receives research support, through Yale, from Bristol-Myers Squibb, Novo Nordisk, and BridgeBio. He is a coinventor of U.S. Provisional Patent Applications 63/177,117, 63/428,569, 63/346,610, 63/484,426, 63/508,315, and 63/606,203 and is a co-founder of Ensight-AI and Evidence2Health, health platforms to improve cardiovascular diagnosis and evidence-based cardiovascular care.

This study was funded by: K23HL153775 5T32HL155000-03 1F32HL170592-01

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IRB of Yale University waived ethical approval and granted a waiver of consent for this work since this was a retrospective study with minimal risks to subjects.

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