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Directed acyclic graphs in perioperative observational research-A systematic review and critique against best practice recommendations.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Watson, Matthew Lamont  ORCID logo  https://orcid.org/0000-0003-3807-8455
Hickman, Sebastian HM 
Dreesbeimdiek, Kaya Marlen 
Kohler, Katharina 

Abstract

The Directed Acyclic Graph (DAG) is a graph representing causal pathways for informing the conduct of an observational study. The use of DAGs allows transparent communication of a causal model between researchers and can prevent over-adjustment biases when conducting causal inference, permitting greater confidence and transparency in reported causal estimates. In the era of 'big data' and increasing number of observational studies, the role of the DAG is becoming more important. Recent best-practice guidance for constructing a DAG with reference to the literature has been published in the 'Evidence synthesis for constructing DAGs' (ESC-DAG) protocol. We aimed to assess adherence to these principles for DAGs constructed within perioperative literature. Following registration on the International Prospective Register of Systematic Reviews (PROSPERO) and with adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting framework for systematic reviews, we searched the Excerpta Medica dataBASE (Embase), the Medical Literature Analysis and Retrieval System Online (MEDLINE) and Cochrane databases for perioperative observational research incorporating a DAG. Nineteen studies were included in the final synthesis. No studies demonstrated any evidence of following the mapping stage of the protocol. Fifteen (79%) fulfilled over half of the translation and integration one stages of the protocol. Adherence with one stage did not guarantee fulfilment of the other. Two studies (11%) undertook the integration two stage. Unmeasured variables were handled inconsistently between studies. Only three (16%) studies included unmeasured variables within their DAG and acknowledged their implication within the main text. Overall, DAGs that were constructed for use in perioperative observational literature did not consistently adhere to best practice, potentially limiting the benefits of subsequent causal inference. Further work should focus on exploring reasons for this deviation and increasing methodological transparency around DAG construction.

Description

Keywords

Bias, Causality, Models, Theoretical, Communication, Data Interpretation, Statistical, Observational Studies as Topic

Journal Title

PLoS One

Conference Name

Journal ISSN

1932-6203
1932-6203

Volume Title

Publisher

Public Library of Science (PLoS)
Sponsorship
Wellcome Trust (204017/Z/16/Z)
Engineering and Physical Sciences Research Council (EP/S022961/1)