  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.
