A Novel Digital Twin Strategy to Examine the Implications of Randomized Clinical Trials for Real-World Populations.

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-Twin-GAN 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 found different treatment effects. To demonstrate treatment effect estimates of each RCT conditioned on the other RCT's pati

SUBMITTER: Thangaraj PM 

PROVIDER: S-EPMC10996766 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature