title: Graphical presentation of confounding in directed acyclic graphs.
abstract: Since confounding obscures the real effect of the exposure, it is important to adequately address confounding for making valid causal inferences from observational data. Directed acyclic graphs (DAGs) are visual representations of causal assumptions that are increasingly used in modern epidemiology. They can help to identify the presence of confounding for the causal question at hand. This structured approach serves as a visual aid in the scientific discussion by making underlying relations explicit. This article explains the basic concepts of DAGs and provides examples in the field of nephrology with and without presence of confounding. Ultimately, these examples will show that DAGs can be preferable to the traditional methods to identify sources of confounding, especially in complex research questions.
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Marit M
authors.initials: MM
authors.lastname: Suttorp
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Bob
authors.initials: B
authors.lastname: Siegerink
authors.affiliation: ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
authors.forename: Kitty J
authors.initials: KJ
authors.lastname: Jager
authors.affiliation: CNR-IBIM Clinical Epidemiology and Pathophysiology of Renal Diseases and Hypertension, Reggio Calabria, Italy.
authors.forename: Carmine
authors.initials: C
authors.lastname: Zoccali
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Friedo W
authors.initials: FW
authors.lastname: Dekker
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans
keywords: DAGs; causal; confounding; directed acyclic graph; epidemiology