Application of directed acyclic graphs in identification and control of selection bias in causal inference
10.16462/j.cnki.zhjbkz.2019.03.022
- Author:
Zi-yan LIU
1
;
Xiao-li WU
;
Mei-qiu XIE
;
Zhi-peng WANG
;
Ai-zhong LIU
Author Information
1. Department of Epidemiology and Health statistics, School of Public Health, Central South University, Changsha 410008,China
- Publication Type:Research Article
- Keywords:
Etiology study;
Directed acyclic graph;
Selection bias;
Collider-stratification bias
- From:
Chinese Journal of Disease Control & Prevention
2019;23(3):351-355
- CountryChina
- Language:Chinese
-
Abstract:
In the etiology study of epidemiology, selection bias will lead to the fact that the research sample cannot represent the general population, the association between exposure and outcome among those selected for analysis differs from the association among those eligible, and the true causal association cannot be inferred. Directed acyclic graphs (DAGs) could visualize complex causality, introduce the Collider-stratification bias using simple graphics language, provide a simple and intuitive way to identify Selection bias, different types of selection bias are verified by the graphic structure of the Collider-stratification bias. In practical studies, there may be multiple biases at the same time, improper adjustment of the collider will lead to Collider-stratification bias, open a backdoor path, even change the size and direction of the confounding bias. In order to obtain an unbiased estimate of the exposure to the outcome, it is necessary to identify the collider and avoid the adjustment to prevent the occurrence of Collider-stratification bias by using DAGs.
