Background: Real-world evidence (RWE) generation is pivotal to provide data on patients receiving treatment outside of controlled clinical trial settings. Regulatory approval of new treatments is often followed by post‑marketing evaluations to understand real‑world care, safety, or effectiveness of therapy. Non-interventional studies provide overall value demonstration to supplement clinical trial results. While design of the study protocol is critical in the success of the research effort, few guidelines consider the nuances for protocol design in a pre- and post-marketing real-world setting.

Discussion: RWE studies differ fundamentally from clinical trials; study type, design, and methods require adaptation to the research questions and objectives, as well as end users and expected applications of the study results. Studies require more flexible data collection procedures and collaboration between the protocol writer and operational team to allow the integration of realistic assumptions into the protocol during development. To ensure successful design and implementation of the study, there are several key factors and challenges to consider. Real-world study protocols must present clear and measurable objectives. They must delineate a strong scientific approach that is operationally feasible; include variables chosen to support outcomes of interest, including potential confounders; consider ethical requirements; avoid requirements that can impact routine clinical care; and allow for the anticipation of variability in study procedures. Early engagement of key stakeholders/reviewers is also critical. Protocols should be written by trained individuals with appropriate scientific background and product/disease knowledge to mitigate and address these issues.

Conclusion: Real-world study protocols are complex and must ensure the study design does not alter routine clinical care patterns. Successful study execution is dependent on a well-developed protocol based on clear objectives, designed by experts with knowledge of the real-world landscape and the numerous methodological, data collection and operational challenges associated with dynamic and highly variable real-world settings.