Randomized controlled clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of pharmaceutical drugs, but in many cases their costs, duration, limited generalizability, and ethical or technical feasibility have caused some to look for real-world studies as alternatives. However, real-world studies may be less convincing due to the lack of randomization and blinding. In this article, we discuss some key considerations in the design of real-world studies, which include experimental studies (e.g., hybrid or pragmatic clinical trials and non-randomized single-arm clinical trials with external controls) and non-experimental studies (e.g., cohort studies, cross-sectional studies, and case-control studies). Causal inference plays a critical role in the derivation of robust real-world evidence (RWE) from the analysis of real-world data (RWD). Therefore, we apply the hypothetical strategy, along with the concept of potential outcome, to lay out these key considerations, and we hope these considerations are helpful for the design, conduct, and analysis of real-world studies.
Keywords: Causal inference; Confounding bias; Real-world evidence; Real-world studies; Study design.
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