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.

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Aims: To assess cohort retention in the OPPERA project and to compare the degree of overlap between pairs of chronic overlapping pain conditions (COPCs) using a cross-sectional analysis of data from 655 adults who completed followup in the OPPERA study. Methods: Subjects were classified for the absence or presence of each of the five COPCs. The extent of overlap beyond chance was quantified using odds ratios, which were calculated using binary logistic regression models. Results: While overlap was the norm, its magnitude varied according to COPC: 51% of people with headache had one or more overlapping COPCs, and this proportion increased to 90% for people with fibromyalgia. The degree of overlap between pairs of COPCs also varied considerably, with odds ratios being greatest for associations between musculoskeletal conditions (fibromyalgia, temporo mandibular disorders, and low back pain) and less pronounced for overlap involving headache or IBS. Furthermore, univariate associations between some pairs of COPCs were nullified after adjusting for other COPCs. Conclusion: There was greater overlap between fibromyalgia and either temporomandibular disorders or low back pain than between other pairs of COPCs. While musculoskeletal conditions exhibited some features that could be explained by a single functional syndrome, headache and irritable bowel syndrome did not.