Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis
整合随机试验、真实世界数据、人工智能与统计学：下一代证据综合AI 解读 ↓
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整合随机试验、真实世界数据、人工智能与统计学：下一代证据综合
- 研究问题
- 如何整合随机试验、真实世界数据和 AI/ML，使临床证据生成同时明确因果目标、控制偏差，并量化支持决策的不确定性？
- 方法与思路
- 这篇观点文章基于因果路线图提出六步统计路线，并围绕数据适用性、AI 的作用、统计职责、证据系统和人才培养展开讨论。
- 材料陈述的贡献
- 作者提出整合式证据框架，强调估计目标、数据适配、偏差控制和不确定性评估应共同约束证据生成与决策支持。
- 阅读关注点
- 应核对六步路线的具体内容、真实世界数据用于因果分析的判断标准，以及证据充分性如何被转化为可执行的评估程序。
- 阅读边界
- 所据为 arXiv 预印本，不能确认同行评审或期刊接收；摘要定位为观点文章，未提供新估计量、证明或整合框架的效果验证。
原文摘要
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 as an important source of real-world evidence (RWE). In parallel, artificial intelligence and machine learning (AI/ML) are increasingly used throughout the drug development process, providing scalability and flexibility but also presenting challenges in interpretability and statistical rigor. 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 under a statistical evidence framework that clarifies estimands, evaluates data fitness, controls bias, quantifies uncertainty, and determines when evidence is strong enough to support decisions. Building on the Causal Roadmap for high-quality real-world evidence, we present a six-step statistical roadmap for integrative evidence synthesis and organize the discussion around five core questions that statisticians must confront: when RWD is fit for causal use and when it is not; what AI genuinely contributes across the evidence lifecycle; what remains distinctly statistical and indispensable; how to build trustworthy end-to-end evidence systems in pharmaceutical, regulatory, and industry settings; and how statistical training should evolve.