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J Dent Res. 2016 Jul;95(8):853-9. doi: 10.1177/0022034516639920. Epub 2016 Mar 21.

Directed Acyclic Graphs for Oral Disease Research.

Author information

1
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA Department of Dental Ecology, School of Dentistry, University of North Carolina, Chapel Hill, NC, USA.
2
Department of Oral Diagnostic Sciences, University at Buffalo, Buffalo, NY, USA.
3
Department of Dental Ecology, School of Dentistry, University of North Carolina, Chapel Hill, NC, USA gary_slade@unc.edu.
4
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA.

Abstract

Directed acyclic graphs (DAGs) are nonparametric graphical tools used to depict causal relations in the epidemiologic assessment of exposure-outcome associations. Although their use in dental research was first advocated in 2002, DAGs have yet to be widely adopted in this field. DAGs help identify threats to causal inference such as confounders, bias due to subject selection, and inappropriate handling of missing data. DAGs can also inform the data analysis strategy based on relations among variables depicted on it. This article uses the example of a study of temporomandibular disorders (TMDs), investigating causal effects of facial injury on subsequent risk of TMD. We illustrate how DAGs can be used to identify 1) potential confounders, 2) mediators and the consequences of attempt to estimate direct causal effects, 3) colliders and the consequences of conditioning on colliders, and 4) variables that are simultaneously mediators and confounders and the consequences of adjustment for such variables. For example, one DAG shows that statistical adjustment for the pressure pain threshold would necessarily bias the causal relation between facial injury and TMD. Finally, we discuss the usefulness of DAGs during study design, subject selection, and choosing variables to be measured in a study.

KEYWORDS:

bias; causation; confounders; epidemiologic methods; mediators; observational study design

PMID:
27000052
PMCID:
PMC4935832
DOI:
10.1177/0022034516639920
[Indexed for MEDLINE]
Free PMC Article
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