A new framework to enhance the interpretation of external validation studies of clinical prediction models
A new framework to enhance the interpretation of external validation studies of clinical prediction models
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DOI：
10.1016/j.jclinepi.2014.06.018
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发表时间：
2015-03-01
影响因子：
7.2
通讯作者：
Moons, Karel G. M.
中科院分区：
文献类型：
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作者：
Debray, Thomas P. A.;Vergouwe, Yvonne;Moons, Karel G. M.
目的：人们普遍认为，诊断和预后预测模型的性能应在外部验证研究中进行评估，使用来自“不同但相关”样本的独立数据与开发样本的数据进行比较。我们开发了一个框架的方法步骤和统计方法，用于分析和增强解释的结果从外部验证研究的预测models.Study设计和设置：我们建议量化的发展和验证样本之间的相关性程度的规模范围从再现性到可移植性，通过评估其相应的情况下，组合的差异。随后，我们评估了模型在验证样本中的性能，并根据病例组合差异解释了性能。最后，我们可以调整模型的validation setting.Results：我们说明了这三个步骤的框架与预测模型诊断深静脉血栓形成使用三个验证样本与不同的情况下组合。虽然一个外部验证样本仅评估模型的再现性，但另外两个样本评估模型的可移植性。在所有验证样本的性能是足够的，该模型并不需要广泛的更新，以纠正误校准或拟合差的validationsettings.Conclusion：建议的框架增强了解释的结果在外部验证的预测模型。(C)2015年，作者。爱思唯尔公司出版
Objectives: It is widely acknowledged that the performance of diagnostic and prognostic prediction models should be assessed in external validation studies with independent data from "different but related" samples as compared with that of the development sample. We developed a framework of methodological steps and statistical methods for analyzing and enhancing the interpretation of results from external validation studies of prediction models.Study Design and Setting: We propose to quantify the degree of relatedness between development and validation samples on a scale ranging from reproducibility to transportability by evaluating their corresponding case-mix differences. We subsequently assess the models' performance in the validation sample and interpret the performance in view of the case-mix differences. Finally, we may adjust the model to the validation setting.Results: We illustrate this three-step framework with a prediction model for diagnosing deep venous thrombosis using three validation samples with varying case mix. While one external validation sample merely assessed the model's reproducibility, two other samples rather assessed model transportability. The performance in all validation samples was adequate, and the model did not require extensive updating to correct for miscalibration or poor fit to the validation settings.Conclusion: The proposed framework enhances the interpretation of findings at external validation of prediction models. (C) 2015 The Authors. Published by Elsevier Inc.