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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Directed acyclic graphs for clinical research: a tutorial.

Sangmin Byeon1,2, Woojoo Lee1,2

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Summary
This summary is machine-generated.

Directed acyclic graphs (DAGs) clarify causal relationships and guide confounder selection in research. This guide introduces DAGs and DAGitty software for rigorous, transparent, and reproducible studies, especially in minimally invasive surgery.

Keywords:
Backdoor criterionCausal diagramsConfounder selectionDirected acyclic graphsd-Separation

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Area of Science:

  • Causal inference and research methodology.

Background:

  • Directed acyclic graphs (DAGs) are increasingly used in clinical and surgical research for visualizing causal structures.
  • Misuse of DAGs can occur due to a lack of understanding of fundamental principles.

Purpose of the Study:

  • To introduce basic DAG terminology, rules, and the DAGitty software.
  • To guide researchers in selecting variables for adjustment using the backdoor criterion.
  • To discuss potential biases and limitations, including issues with p-value-based confounder selection.

Main Methods:

  • Explanation of DAG concepts and the backdoor criterion for confounder identification.
  • Demonstration of the DAGitty software with practical examples.
  • Discussion of bias types and their mitigation.

Main Results:

  • DAGs aid in clarifying research questions and study designs.
  • DAGitty provides a user-friendly interface for creating and analyzing DAGs.
  • The backdoor criterion offers a principled approach to confounder selection.

Conclusions:

  • DAGs enhance the rigor, transparency, and reproducibility of research.
  • Proper application of DAGs is crucial for valid causal inference.
  • The study advocates for the use of DAGs in minimally invasive surgery research.