Abstract
Purpose of Review
Epidemiologists frequently must handle competing events, which prevent the event of interest from occurring. We review considerations for handling competing events when interpreting results causally.
Recent Findings
When interpreting statistical associations as causal effects, we recommend following a causal inference “roadmap” as one would in an analysis without competing events. There are, however, special considerations to be made for competing events when choosing the causal estimand that best answers the question of interest, selecting the statistical estimand (e.g., the cause-specific or subdistribution) that will target that causal estimand, and assessing whether causal identification conditions (e.g., conditional exchangeability, positivity, and consistency) have been sufficiently met.
Summary
When doing causal inference in the competing events setting, it is critical to first ascertain the relevant question and the causal estimand that best answers it, with the choice often being between estimands that do and do not eliminate competing events.
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Funding
This work was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development grant R01 HD093602 and National Institutes of Health grant K01 AA028193.
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Rudolph, J.E., Lesko, C.R. & Naimi, A.I. Causal Inference in the Face of Competing Events. Curr Epidemiol Rep 7, 125–131 (2020). https://doi.org/10.1007/s40471-020-00240-7
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DOI: https://doi.org/10.1007/s40471-020-00240-7