executive summary
Non-interventional studies are a type of study in which patients receive a marketed drug of interest during routine clinical practice and are not assigned to an intervention according to a protocol, aiming to reveal insights that may not be available in controlled trials. Non-interventional or observational study designs can play an important role in evaluating treatment effects (i.e., causality) beyond the traditional randomized controlled trial (RCT). In these study designs, outcomes of routine clinical care are observed in real-world populations as opposed to study participants in RCTs who are selected according to narrow inclusion/exclusion criteria. Real-world data (RWD), derived from sources such as electronic medical records, claims data, and registries, provide a less constrained environment that better reflects the complexity and diversity of clinical practice. Additionally, real-world studies typically have much larger sample sizes, facilitating subgroup analysis that is often not feasible in RCTs. Subgroups in this case represent the unit of analysis of a subset of participants within a given study population. This nuanced understanding can aid in medical decision making by capturing real-world outcomes, patient diversity, and the long-term impact of interventions observed as part of routine clinical care.
Real-world evidence (RWE) complements RCTs by going beyond traditional clinical trials and providing timely insights on efficacy in diverse populations. Regulatory initiatives such as the U.S. Food and Drug Administration’s (FDA) Real-World Evidence Advancing Program recognize the value of RWE, aiming to modernize evidence generation and incorporate the patient perspective. However, ensuring the reliability of RWE in causal inference requires clear design, fit-for-purpose RWD, communication, and rigorous statistical analysis. Promoting the causal inference capabilities of RWE is essential to advance evidence-based medicine. Regulators recognize that using RWD to determine or measure causality comes with certain limitations. Proposed approaches may include established concepts such as targeted trial emulation and other causal frameworks to address confounding and other types of bias, as well as schemas to describe the overall study design. Integrating the strengths of RWE with traditional research methods such as RCTs can provide a more comprehensive understanding of medical interventions and their real-world impacts.