How to use directed acyclic graphs: guide for clinical researchers

Abstract

Directed acyclic graphs are commonly used to illustrate and assess the hypothesised causal mechanisms in health and social research. These graphs can illuminate investigators’ assumptions and help clearly describe each possible explanation for associations observed in data given researchers’ assumptions, ranging from causal effects to confounding and selection bias, and thereby help identify variables that can be used to reduce or overcome bias. This article explains how to construct, interpret, and present directed acyclic graphs as part of clinical research studies and how they can help communicate a study’s strengths or limitations. Causal directed acyclic graphs (DAGs) are a type of graph that illustrates an assumed causal structure between variables of interest. These graphs can illustrate assumed links between possible causes (eg, a behaviour or a medical intervention; referred to in this article as exposure) to possible consequences (eg, presence or absence of disease; referred to in this article as outcome).1 While causal graphs have long been used,2 DAGs have a relatively short history in epidemiological research3 but have become widespread as a way to think about the causal structure underlying an exposure-outcome association.45 DAGs can be useful for many purposes, such as helping to identify confounders,67 evaluating potential selection bias,89 and understanding the roles that measurement error1011 and missing data12 might have in effect estimation. Recent papers have highlighted how DAGs can improve epidemiological131415 and clinical studies.16171819 However, they can also aid in understanding descriptive studies (eg, estimating the incidence of disease) and prediction studies (eg, modelling a patient’s risk of disease). These graphs can also help communicate the assumptions necessary to interpret results to collaborators, researchers, reviewers, readers, and editors. Despite their potential utility, wide variation in the use …