Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis

TL;DR

This paper advocates for a principled integration of RCTs, RWD, AI/ML, and statistics to enhance evidence synthesis in clinical research, emphasizing transparency, causal inference, and future directions for robust, policy-relevant outcomes.

Contribution

It proposes a causal roadmap and integrative framework combining traditional statistics with AI/ML to improve evidence generation and translation in clinical science.

Findings

Highlighting the importance of causal inference and transparency.

Proposing methods for transporting RCT results to broader populations.

Outlining future research directions in privacy, uncertainty, and small-sample methods.

Abstract

Randomized controlled trials (RCTs) have been the cornerstone of clinical evidence; however, their cost, duration, and restrictive eligibility criteria limit power and external validity. Studies using real-world data (RWD), historically considered less reliable for establishing causality, are now recognized to be important for generating real-world evidence (RWE). In parallel, artificial intelligence and machine learning (AI/ML) are being increasingly used throughout the drug development process, providing scalability and flexibility but also presenting challenges in interpretability and rigor that traditional statistics do not face. This Perspective argues that the future of evidence generation will not depend on RCTs versus RWD, or statistics versus AI/ML, but on their principled integration. To this end, a causal roadmap is needed to clarify inferential goals, make assumptions explicit, and ensure transparency about tradeoffs. We highlight key objectives of integrative evidence synthesis, including transporting RCT results to broader populations, embedding AI-assisted analyses within RCTs, designing hybrid controlled trials, and extending short-term RCTs with long-term RWD. We also outline future directions in privacy-preserving analytics, uncertainty quantification, and small-sample methods. By uniting statistical rigor with AI/ML innovation, integrative approaches can produce robust, transparent, and policy-relevant evidence, making them a key component of modern regulatory science.