Dash, K., Goodacre, S. orcid.org/0000-0003-0803-8444 and Sutton, L. (2022) Composite outcomes in clinical prediction modeling: are we trying to predict apples and oranges? Annals of Emergency Medicine, 80 (1). pp. 12-19. ISSN 0196-0644
Abstract
Composite outcomes are widely used in clinical research. Existing literature has considered the pros and cons of composite outcomes in clinical trials, but their extensive use in clinical prediction has received much less attention. Clinical prediction assists decision-making by directing patients with higher risks of adverse outcomes toward interventions that provide the greatest benefits to those at the greatest risk. In this article, we summarize our existing understanding of the advantages and disadvantages of composite outcomes, consider how these relate to clinical prediction, and highlight the problem of key predictors having markedly different associations with individual components of the composite outcome. We suggest that a “composite outcome fallacy” may occur when a clinical prediction model is based on strong associations between key predictors and one component of a composite outcome (such as mortality) and used to direct patients toward intervention when these predictors actually have an inverse association with a more relevant component of the composite outcome (such as the use of a lifesaving intervention). We propose that clinical prediction scores using composite outcomes should report their accuracy for key components of the composite outcome and examine for inconsistencies among predictor variables.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 by the American College of Emergency Physicians. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield) > ScHARR - Sheffield Centre for Health and Related Research The University of Sheffield > Sheffield Teaching Hospitals |
Funding Information: | Funder Grant number HEALTH DATA RESEARCH UK LIMITED 2021.0099 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Apr 2022 14:59 |
Last Modified: | 24 Nov 2022 15:57 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.annemergmed.2022.01.046 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185408 |