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<p>Clinical trials generate essential evidence on treatment safety and efficacy, but slow timelines, high costs, and limited inclusivity constrain efficiency, generalisability, and clinical impact. Causal inference and digital twins offer complementary tools to define estimands, characterise treatment-effect heterogeneity, assess transportability, and simulate patient trajectories under alternative interventions. Integrated responsibly into trial design, recruitment, monitoring, and post-trial translation, they could support faster, fairer, and more informative clinical trials.</p>

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Causal inference and digital twins: a roadmap for the future of clinical trials

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

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  • Silas Ruhrberg Estévez,
  • Richard Peck,
  • Eoin McKinney,
  • Jim Weatherall,
  • Stuart Bailey,
  • Justine Rochon,
  • Chris Anagnostopoulos,
  • Pierre Marquet,
  • Anthony Wood,
  • Nicky Best,
  • Harry Amad,
  • Julianna Piskorz,
  • Krzysztof Kacprzyk,
  • Rafik Salama,
  • Christina Gunther,
  • Francesca Frau,
  • Antoine Pugeat,
  • Ramon Hernandez,
  • Mihaela van der Schaar

摘要

Clinical trials generate essential evidence on treatment safety and efficacy, but slow timelines, high costs, and limited inclusivity constrain efficiency, generalisability, and clinical impact. Causal inference and digital twins offer complementary tools to define estimands, characterise treatment-effect heterogeneity, assess transportability, and simulate patient trajectories under alternative interventions. Integrated responsibly into trial design, recruitment, monitoring, and post-trial translation, they could support faster, fairer, and more informative clinical trials.