New roadmap integrates RCTs, RWD, and AI/ML for evidence synthesis

A new perspective paper proposes a six-step statistical roadmap for integrating randomized controlled trials (RCTs), real-world data (RWD), and artificial intelligence/machine learning (AI/ML) to improve evidence synthesis. The paper argues that the future of evidence generation lies in the principled combination of these methodologies, rather than their exclusive use. It addresses key questions regarding the causal use of RWD, the contributions of AI/ML, the indispensable role of statistics, and the evolution of statistical training for pharmaceutical and regulatory settings. AI

IMPACT Proposes a framework to enhance the reliability and efficiency of evidence generation in fields like drug development by integrating AI/ML with traditional statistical methods.

RANK_REASON The item is a research paper published on arXiv discussing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Causal Roadmap
• randomized controlled trial
• Real world data
• real-world evidence
• Shu Yang
• statistics

• paper
• other

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