摘要
在流行病学研究中,选择偏倚会导致研究样本无法代表一般人群,使研究结果偏离真实值,无法推断真实的因果关联。本文通过构建有向无环图(directed acyclic graphs, DAGs),将复杂的因果关系可视化,提供识别选择偏倚的直观方法,并通过冲撞分层偏倚的图形结构来验证不同类型的选择偏倚。在实际研究中,可能同时存在多种偏倚,对冲撞变量进行不恰当的调整会新增冲撞分层偏倚,打开后门路径,引入混杂偏倚,甚至改变原有混杂偏倚的大小与方向。为了得到暴露到结局的无偏估计,研究者可以通过构建DAGs,帮助识别冲撞变量,防止冲撞偏倚的发生。
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.
作者
刘子言
吴小丽
解美秋
王志鹏
刘爱忠
LIU Zi-yan;WU Xiao-li;XIE Mei-qiu;WANG Zhi-peng;LIU Ai-zhong(Department of Epidemiology and Health statistics,School of Public Health,Central South University,Changsha 410008,China)
出处
《中华疾病控制杂志》
CAS
CSCD
北大核心
2019年第3期351-355,共5页
Chinese Journal of Disease Control & Prevention
关键词
病因研究
有向无环图
选择偏倚
冲撞分层偏倚
Etiology study
Directed acyclic graph
Selection bias
Collider-stratification bias
