Application of directed acyclic graphs in identifying and controlling confounding bias
10.3760/cma.j.cn112338-20190729-00559
- VernacularTitle:有向无环图在混杂因素识别与控制中的应用及实例分析
- Author:
Huixin LIU
1
;
Haibo WANG
;
Ning WANG
Author Information
1. 北京大学人民医院 100044
- Keywords:
Causal inference;
Confounder;
Directed acyclic graphs
- From:
Chinese Journal of Epidemiology
2020;41(4):585-588
- CountryChina
- Language:Chinese
-
Abstract:
Observational study has been viewed as the most convenient method in designing etiological studies. However, the presence of confounders always challenge the researchers in study design, since unadjusted confounders may lead to biased results. The traditional definition of a confounder is not intuitional in application and sometimes leading to inappropriate adjustment of nonexistent "confounders" which might induce new bias to merge. The use of directed acyclic graphs (DAGs) may identify confounders easier and more intuitional, as well as avoiding superfluous adjustment. It can also contribute to the identification of adjustment methods, and be useful in causal inference of observational studies.
