整合临床试验与真实世界数据，构建可信的下一代证据体系

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.