Reducing bias through directed acyclic graphs
Reducing bias through directed acyclic graphs
复制标题
DOI：
10.1186/1471-2288-8-70
复制
发表时间：
2008-10-30
影响因子：
4
通讯作者：
Platt, Robert W.
中科院分区：
文献类型：
--
作者：
Shrier, Ian;Platt, Robert W.
背景资料：大多数生物医学研究的目的是确定一个无偏估计的影响，为暴露的结果，即作出因果推断的曝光。流行病学的最新发展表明，识别混杂因素和调整混杂因素的传统方法可能不够。讨论：调整“潜在混杂因素”的传统方法可能会引入条件关联和偏倚，而不是最小化它。尽管先前发表的文章已经讨论了因果有向无环图方法（DAG）在混杂方面的作用，许多临床问题需要复杂的DAG，因此研究者可能继续使用传统的实践，因为他们没有正确使用DAG方法所必需的工具。这篇文章的目的是展示一个简单的使用DAG的6步方法，并从概念的角度解释为什么该方法有效。总结：使用简单的6步DAG方法来讨论混杂和选择偏倚可能会降低所选统计模型中效应估计的偏倚程度。
Background: The objective of most biomedical research is to determine an unbiased estimate of effect for an exposure on an outcome, i.e. to make causal inferences about the exposure. Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.Discussion: The traditional methods of adjusting for "potential confounders" may introduce conditional associations and bias rather than minimize it. Although previous published articles have discussed the role of the causal directed acyclic graph approach ( DAGs) with respect to confounding, many clinical problems require complicated DAGs and therefore investigators may continue to use traditional practices because they do not have the tools necessary to properly use the DAG approach. The purpose of this manuscript is to demonstrate a simple 6-step approach to the use of DAGs, and also to explain why the method works from a conceptual point of view.Summary: Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.