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Reducing bias through directed acyclic graphs.

Shrier I, Platt RW - BMC Med Res Methodol (2008)

Bottom Line: Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Clinical Epidemiology and Community Studies, SMBD-Jewish General Hospital, McGill University, Montreal, Canada. ian.shrier@mcgill.ca

ABSTRACT

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.

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Related in: MedlinePlus

a-b. In Step 3 (3a), all arrows emanating from X are deleted. In Step 4 (3b), one joins all parents of a common child. We have used dashed lines here for clarity.
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Figure 3: a-b. In Step 3 (3a), all arrows emanating from X are deleted. In Step 4 (3b), one joins all parents of a common child. We have used dashed lines here for clarity.

Mentions: Step 2 is essential because after completing the step, all variables left are either conditioned on, or have one of their descendants conditioned on. The importance of this result will become clear in Step 4.


Reducing bias through directed acyclic graphs.

Shrier I, Platt RW - BMC Med Res Methodol (2008)

a-b. In Step 3 (3a), all arrows emanating from X are deleted. In Step 4 (3b), one joins all parents of a common child. We have used dashed lines here for clarity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2601045&req=5

Figure 3: a-b. In Step 3 (3a), all arrows emanating from X are deleted. In Step 4 (3b), one joins all parents of a common child. We have used dashed lines here for clarity.
Mentions: Step 2 is essential because after completing the step, all variables left are either conditioned on, or have one of their descendants conditioned on. The importance of this result will become clear in Step 4.

Bottom Line: Recent developments in epidemiology have shown that traditional methods of identifying confounding and adjusting for confounding may be inadequate.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Centre for Clinical Epidemiology and Community Studies, SMBD-Jewish General Hospital, McGill University, Montreal, Canada. ian.shrier@mcgill.ca

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus