title: [Application of directed acyclic graphs in control of confounding].
abstract: Observational study is a method most commonly used in the etiology study of epidemiology, but confounders, always distort the true causality between exposure and outcome when local inferencing. In order to eliminate these confounding, the determining of variables which need to be adjusted become a key issue. Directed acyclic graph(DAG)could visualize complex causality, provide a simple and intuitive way to identify the confounding, and convert it into the finding of the minimal sufficient adjustment for the control of confounding. On the one hand, directed acyclic graph can choose less variables, which increase statistical efficiency of the analysis. On the other hand, it could help avoiding variables that is not measured or with missing values. In a word, the directed acyclic graph could facilitate the reveal of the real causality effectively.
authors.affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Central South University, Changsha 410008, China.
authors.forename: R
authors.initials: R
authors.lastname: Xiang
authors.forename: W J
authors.initials: WJ
authors.lastname: Dai
authors.forename: Y
authors.initials: Y
authors.lastname: Xiong
authors.forename: X
authors.initials: X
authors.lastname: Wu
authors.forename: Y F
authors.initials: YF
authors.lastname: Yang
authors.forename: L
authors.initials: L
authors.lastname: Wang
authors.forename: Z H
authors.initials: ZH
authors.lastname: Dai
authors.forename: J
authors.initials: J
authors.lastname: Li
authors.forename: A Z
authors.initials: AZ
authors.lastname: Liu
mesh_terms: D015982:Bias; D001699:Biometry; D015984:Causality; D003196:Computer Graphics; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D006801:Humans