title: [Use of causal diagrams in Epidemiology: application to a situation with confounding].
abstract: Epidemiological research still rarely uses causal diagrams, despite growing recognition of their explanatory potential. One possible reason is that many research programs involve themes in which there is a certain degree of uncertainty as to mechanisms in the processes that generate the data. In this study, the relationship between occupational stress and obesity is used as an example of the application of causal diagrams to questions related to confounding. The article presents the selection stages for variables in statistical adjustment and the derivation of a causal diagram's statistical implications. The main advantage of causal diagrams is that they explicitly reveal the respective model's underlying hypotheses, allowing critical analysis of the implications and thereby facilitating identification of sources of bias and uncertainty in the epidemiological study's results.
authors: Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil.
authors: Taísa Rodrigues
authors: TR
authors: Cortes
authors: Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil.
authors: Eduardo
authors: E
authors: Faerstein
authors: Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil.|Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brasil.
authors: Claudio José
authors: CJ
authors: Struchiner
mesh_terms: D015982:Bias; D001938:Brazil; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D015233:Models, Statistical