Sperrin Matthew, Riley Richard D, Collins Gary S, Martin Glen P
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.
Diagn Progn Res. 2022 Dec 22;6(1):24. doi: 10.1186/s41512-022-00136-8.
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.
临床预测模型在使用前必须进行适当验证。虽然验证研究有时会经过精心设计，以匹配模型的预期人群/环境，但验证研究通常使用的是任意数据集，这些数据集是为了方便而选择的，而非出于相关性考虑。我们将估计模型在预期人群/环境中的表现称为“靶向验证”。使用这个术语能更明确地聚焦于模型的预期用途，这可能会提高已开发模型的适用性，避免得出误导性结论，并减少研究浪费。它还揭示出，当模型的预期人群与用于开发模型的人群相匹配时，可能不需要进行外部验证；在这种情况下，强大的内部验证可能就足够了，特别是如果开发数据集很大。
