Over recent decades, epidemiologic methodology has evolved substantially to encompass a gamut of techniques for focalizing on the most unbiased effect estimate of a single exposure (e.g., a treatment effect). By contrast, less attention has been paid to the refinement of study designs and analytic techniques intended to encompass the underlying milieu of co-occurring clinical, environmental, and social determinants that interactively influence disease risk or outcomes. Single exposure approaches (i.e., the “potential outcomes framework”) have been criticized as being reductionist and have long been debated in the broader epidemiologic community, despite the utility of this approach in optimizing internal validity. Further, the gap between clinical trial efficacy and real-world (RW) effectiveness remains a concern for both regulatory entities and payers, though this gap may be partly attributable to varying prevalences of important effect modifiers between trial and RW patient populations.

This conceptual paper will expand upon the existing conceptual paradigms by applying them to RW research and delineating key considerations related to assessing whether a single exposure or causal architecture approach would be fit-for-purpose based on the overarching research question. Drawing upon examples from RW research types, such as use of external control groups, comparative effectiveness, and prediction of likely responders to treatment, the strengths and weaknesses of each approach will be demonstrated using case studies related to regulatory and/or payer decision-making. Methodologic recommendations will be provided.

Ultimately, both single exposure and causal architecture approaches to study design and analysis have a place in pharmacoepidemiology and real-world research. A careful examination of when each approach is fit-for-purpose can lead to the application of innovative strategies to the design and conduct of RW studies.