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A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The\n                    <jats:italic>Causal Roadmap<\/jats:italic>\n                    is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the\n                    <jats:italic>Roadmap<\/jats:italic>\n                    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\n                    <jats:italic>Causal Roadmap<\/jats:italic>\n                    framework for use by clinical and translational researchers; three companion papers demonstrate applications of the\n                    <jats:italic>Causal Roadmap<\/jats:italic>\n                    for specific use cases.\n                  <\/jats:p>","DOI":"10.1017\/cts.2023.635","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T05:37:16Z","timestamp":1695361036000},"source":"Crossref","is-referenced-by-count":70,"title":["A causal roadmap for generating high-quality real-world evidence"],"prefix":"10.1017","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2967-0855","authenticated-orcid":false,"given":"Lauren 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