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January 22, 2026European Journal of Anaesthesiology

Beyond observational data: understanding anaesthesia research better with causal diagrams

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Authors

PWPiet WyffelsGhent University HospitalADAlexander DecruyenaereGhent University HospitalPWPatrick WoutersGhent University Hospital

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Implication

This review demonstrates how causal diagrams enhance understanding of observational data in anesthesia, indicating new insights into research methodology.

Key Points

  • The aim is to explore how causal diagrams can improve the analysis and interpretation of observational data in anesthesia research.
  • Reviewed existing literature on causal inference and directed acyclic graphs (DAGs).
  • Provided examples of DAG applications in handling covariate adjustments and missing data.
  • Explained the titration paradox with advanced DAGs incorporating pharmacological insights.
  • Highlighted the advantages of using DAGs for drawing causal inferences from observational data.
  • Demonstrated how DAGs can clarify complex situations in randomized controlled trials.
  • Showed that DAGs help organize and communicate research results effectively.

Cite This Study

Wyffels et al. (2026) studied this question.

synapsesocial.com/papers/6971bdcf642b1836717e2789https://doi.org/10.1097/eja.0000000000002355
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