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The design and interpretation of clinical studies requires consideration of variables beyond the exposure or treatment of interest and patient outcomes, including decisions about which variables to capture and, of those, which to control for in statistical analyses to minimize bias in estimating treatment effects. Causal directed acyclic graphs (DAGs) are a useful tool for communicating researchers’ understanding of the potential interplay among variables and are commonly used for mediation analysis.1,2 Assumptions are presented visually in a causal DAG and, based on this visual representation, researchers can deduce which variables require control to minimize bias and which variables could introduce bias if controlled in the analysis.3- 5
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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.
Corresponding Author: Ari M. Lipsky, MD, PhD, Department of Emergency Medicine, HaEmek Medical Center, Yitshak Rabin Blvd 21, Afula 1834111, Israel (firstname.lastname@example.org).
Published Online: February 28, 2022. doi:10.1001/jama.2022.1816
Conflict of Interest Disclosures: None reported.
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