TrialCalibre: A Fully Automated Causal Engine for RCT Benchmarking and Observational Trial Calibration
TLDR
TrialCalibre automates and scales the BenchExCal framework, using a multiagent system to improve the credibility and transparency of real-world evidence studies.
Key contributions
- Automates and scales the BenchExCal framework for real-world evidence (RWE) studies.
- Utilizes a multiagent system with specialized agents for protocol design, data synthesis, and calibration.
- Incorporates agent learning (RLHF) and knowledge blackboards for adaptive, auditable causal estimation.
- Enhances the credibility and transparency of observational trial calibration.
Why it matters
Real-world evidence studies are crucial for clinical decisions but suffer from biases and scalability issues. This paper introduces TrialCalibre, an automated multiagent system that addresses these challenges by making causal effect estimation more credible, transparent, and scalable. It significantly advances the practical application of robust RWE.
Original Abstract
Real-world evidence (RWE) studies that emulate target trials increasingly inform regulatory and clinical decisions, yet residual, hard-to-quantify biases still limit their credibility. The recently proposed BenchExCal framework addresses this challenge via a two-stage Benchmark, Expand, Calibrate process, which first compares an observational emulation against an existing randomized controlled trial (RCT), then uses observed divergence to calibrate a second emulation for a new indication causal effect estimation. While methodologically powerful, BenchExCal is resource intensive and difficult to scale. We introduce TrialCalibre, a conceptualized multiagent system designed to automate and scale the BenchExCal workflow. Our framework features specialized agents such as the Orchestrator, Protocol Design, Data Synthesis, Clinical Validation, and Quantitative Calibration Agents that coordi-nate the the overall process. TrialCalibre incorpo-rates agent learning (e.g., RLHF) and knowledge blackboards to support adaptive, auditable, and transparent causal effect estimation.
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