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

Diagnostic and Prognostic Research

Commentary

Gary S. Collins¹  Matthew Sperrin²  Glen P. Martin²  Richard D. Riley³ 

[1] Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK;Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK;Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK;

关键词: Clinical prediction model;    Validation;    Generalisability;   

DOI  :  10.1186/s41512-022-00136-8

 received in 2022-08-24, accepted in 2022-11-14,  发布年份 2022

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2022