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Journal article : Letter

Targeted validation: validating clinical prediction models in their intended population and setting

Abstract:
Clinical prediction models must be appropriately validated before they can be used. While validation studies are sometimes carefully designed to match an intended population/setting of the model, it is common for validation studies to take place with arbitrary datasets, chosen for convenience rather than relevance. We call estimating how well a model performs within the intended population/setting "targeted validation". Use of this term sharpens the focus on the intended use of a model, which may increase the applicability of developed models, avoid misleading conclusions, and reduce research waste. It also exposes that external validation may not be required when the intended population for the model matches the population used to develop the model; here, a robust internal validation may be sufficient, especially if the development dataset was large.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1186/s41512-022-00136-8

Authors


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Role:
Author
ORCID:
0000-0002-5351-9960
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Institution:
University of Oxford
Division:
MSD
Department:
NDORMS
Sub department:
Botnar Research Centre
Role:
Author
ORCID:
0000-0002-2772-2316


Publisher:
BioMed Central
Journal:
Diagnostic and Prognostic Research More from this journal
Volume:
6
Issue:
1
Article number:
24
Publication date:
2022-12-22
Acceptance date:
2022-11-14
DOI:
EISSN:
2397-7523
Pmid:
36550534


Language:
English
Keywords:
Subtype:
Letter
Pubs id:
1317279
Local pid:
pubs:1317279
Deposit date:
2024-01-26

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