SurroPilot: An LLM-Assisted Platform for Heterogeneous Surrogate Endpoint Evaluation in Clinical Trials

Abstract

Surrogate endpoints are widely used in clinical trials to accelerate treatment evaluation, yet their validity may vary substantially across patient subgroups. Although recent advances in heterogeneous causal mediation analysis enable subgroup-specific surrogate evaluation, applying these methods requires substantial expertise in causal inference, statistical programming, and clinical trial methodology, limiting their accessibility to many biomedical researchers. We present SurroPilot, a large language model (LLM)-assisted platform for heterogeneous surrogate endpoint evaluation in clinical trials. Through natural-language interaction, SurroPilot supports the complete analytical workflow, including dataset understanding, data preprocessing, mediator and covariate selection, heterogeneous causal mediation analysis, subgroup interpretation, and automated report generation. To improve the reliability of AI-assisted statistical computing, the platform incorporates a shared context programmerinspector framework for iterative R code correction and automated validation of LLM-generated variable selections. Rather than replacing statistical methodology, SurroPilot integrates LLM with a validated heterogeneous mediation framework, allowing the LLM to assist with analytical reasoning while statistical inference is performed using established causal inference methods. Using the ACTG175 Phase III HIV clinical trial, we demonstrate that SurroPilot provides an end-to-end, reproducible workflow for heterogeneous surrogate endpoint evaluation and substantially lowers the technical barriers to applying advanced causal mediation methods in clinical trial research.