Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized controlled trials with outcomes assessed using RWD to fully observational studies. Yet many RWE study proposals lack sufficient detail to evaluate adequacy, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to pre-specify analytic study designs; it addresses a wide range of guidance within a single framework. By requiring transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on pre-specified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers, with companion papers demonstrating application of the Causal Roadmap for specific use cases.

翻译：随着利用真实世界证据（RWE）支持临床政策和监管决策的需求日益增长，监管机构、学术界、专业学会及产业界已发布大量指南、建议及框架。从使用RWD评估结局的随机对照试验到完全观察性研究，各类研究均采用真实世界数据（RWD）生成RWE。然而，多数RWE研究方案缺乏评估充分性的详细说明，大量RWD分析存在假设不可靠、方法学缺陷或解读不当等问题。因果路线图作为一套明确、分项、迭代的流程，能够指导研究者预先指定分析性研究设计，在统一框架下整合各类指南要求。通过要求对因果假设进行透明评估，并基于预设标准促进设计与分析方案的客观比较，该路线图可帮助研究者评估特定研究的证据质量潜力，制定生成高质量RWE的研究方案，并与监管机构及其他利益相关方进行有效沟通。本文旨在推广并拓展因果路线图框架，供临床与转化研究人员使用，配套论文将展示该路线图在具体案例中的应用。