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A Novel Digital Twin Strategy to Examine the Implications of Randomized Control Trials for Real-World Populations
来源：https://mp.weixin.qq.com/s/IjewC4s6ykFP0Ep1Abf4PA
PDF：https://www.medrxiv.org/content/ ... 24304868v1.full.pdf
Abstract:
Randomized clinical trials (RCTs) are essential to guide medical practice; however, their
generalizability to a given population is often uncertain. We developed a statistically informed
Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that leverages relationships
between covariates and outcomes and generates a digital twin of an RCT (RCT-Twin)
conditioned on covariate distributions from a second patient population. We used RCT-TwinGAN to reproduce treatment effect outcomes of the Systolic Blood Pressure Intervention Trial
(SPRINT) and the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Blood
Pressure Trial, which tested the same intervention but had different treatment effect results. To
demonstrate treatment effect estimates of each RCT conditioned on the other RCT patient
population, we evaluated the cardiovascular event-free survival of SPRINT digital twins
conditioned on the ACCORD cohort and vice versa (SPRINT-conditioned ACCORD twins). The
conditioned digital twins were balanced by the intervention arm (mean absolute standardized
mean difference (MASMD) of covariates between treatment arms 0.019 (SD 0.018), and the
conditioned covariates of the SPRINT-Twin on ACCORD were more similar to ACCORD than a
sprint (MASMD 0.0082 SD 0.016 vs. 0.46 SD 0.20). Most importantly, across iterations,
SPRINT conditioned ACCORD-Twin datasets reproduced the overall non-significant effect size
seen in ACCORD (5-year cardiovascular outcome hazard ratio (95% confidence interval) of 0.88
(0.73-1.06) in ACCORD vs median 0.87 (0.68-1.13) in the SPRINT conditioned ACCORDTwin), while the ACCORD conditioned SPRINT-Twins reproduced the significant effect size
seen in SPRINT (0.75 (0.64-0.89) vs median 0.79 (0.72-0.86)) in ACCORD conditioned
SPRINT-Twin). Finally, we describe the translation of this approach to real-world populations by
conditioning the trials on an electronic health record population. Therefore, RCT-Twin-GAN
simulates the direct translation of RCT-derived treatment effects across various patient
populations with varying covariate distributions.
一种新的数字孪生策略，用于检验随机对照试验对真实世界人群的影响.pdf
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发表于 2024-6-16 15:16:10