Emulating real-world GLP-1 efficacy in type 2 diabetes through causal learning and virtual patients.

ABSTRACT: Randomized controlled trials (RCTs) remain the benchmark for assessing treatment effects but are limited to phenotypically narrow populations by design. We introduce a novel generative artificial intelligence (AI) driven emulation method that infers effect size through virtual clinical trials, which can emulate the RCT process and potentially extrapolate into wider populations. We validate the virtual trials by comparing the predicted impact of glucagon-like peptide-1 (GLP-1) agonists on HbA1c in type-2 diabetes (T2DM) with its true efficacy established in the LEAD-5 trial. Our emulation model learns treatment effects from real-world evidence data by a combined generative AI and causal learning approach. Training data comprised pre- and post-treatment outcomes for 5,476 people with T2DM. W

SUBMITTER: MacLellan CR 

PROVIDER: S-EPMC12279107 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature