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- Title
Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse.
- Authors
Weiskopf, Nicole G; Dorr, David A; Jackson, Christie; Lehmann, Harold P; Thompson, Caroline A
- Abstract
Objectives Collider bias is a common threat to internal validity in clinical research but is rarely mentioned in informatics education or literature. Conditioning on a collider, which is a variable that is the shared causal descendant of an exposure and outcome, may result in spurious associations between the exposure and outcome. Our objective is to introduce readers to collider bias and its corollaries in the retrospective analysis of electronic health record (EHR) data. Target audience Collider bias is likely to arise in the reuse of EHR data, due to data-generating mechanisms and the nature of healthcare access and utilization in the United States. Therefore, this tutorial is aimed at informaticians and other EHR data consumers without a background in epidemiological methods or causal inference. Scope We focus specifically on problems that may arise from conditioning on forms of healthcare utilization, a common collider that is an implicit selection criterion when one reuses EHR data. Directed acyclic graphs (DAGs) are introduced as a tool for identifying potential sources of bias during study design and planning. References for additional resources on causal inference and DAG construction are provided.
- Subjects
UNITED States; DIRECTED acyclic graphs; CAUSAL inference; ELECTRONIC health records; HEALTH services accessibility; EDUCATIONAL literature
- Publication
Journal of the American Medical Informatics Association, 2023, Vol 30, Issue 5, p971
- ISSN
1067-5027
- Publication type
Article
- DOI
10.1093/jamia/ocad013