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Directed Acyclic Graphs in Surgical Research

Published:August 29, 2022DOI:https://doi.org/10.1016/j.jss.2022.07.017

      Abstract

      Surgical research often utilizes multivariable regression to evaluate causal relationships between variables, but there is usually little explanation of the decision-making regarding which variables were controlled for. We propose that directed acyclic graphs (DAGs)—a formal logic tool that illustrates connections between variables—should be used to define and communicate variable relationships to readers and other audiences. While literature in epidemiology and other medical fields has recently started to incorporate DAGs more, they are still seldom seen in surgical publications. In this review, we describe the background and need for DAGs and argue for their use. Next, we explain how bias can be introduced without a thoughtful approach to control variable selection. Finally, we recommend that researchers communicate their choices and rationale when selecting control variables in published surgical research.

      Keywords

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