Validation of biomarker-based risk prediction models.
Validation of biomarker-based risk prediction models.
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DOI：
10.1158/1078-0432.ccr-07-4534
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发表时间：
2008-10-01
期刊：
影响因子：
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通讯作者：
Andridge RR
中科院分区：
文献类型：
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作者：
Taylor JM;Ankerst DP;Andridge RR
越来越多的预测模型被用来促进知情决策，这突出表明有必要仔细评估这些模型的有效性。特别是，涉及生物标记物的模型需要仔细验证，原因有两个：当复杂模型涉及大量生物标记物时存在过度拟合问题，以及用于测量生物标记物的分析方法的实验室间差异。在本文中，我们区分了内部统计验证和外部统计验证。内部验证，包括对现有数据的培训-测试分离或交叉验证，是模型建立过程的必要组成部分，可以提供对模型性能的有效评估。外部验证包括评估由来自不同机构的不同研究人员收集的一个或多个数据集的模型性能。外部验证是评估预测模型是否将推广到开发该模型时所依据的人群之外的人群所必需的更严格的程序。我们强调，外部数据集必须是真正外部的，即在模型开发中不起作用，理想情况下完全不可用于构建模型的研究人员。除了回顾不同类型的验证之外，我们还描述了不同类型和特征的预测模型和模型构建策略，以及在验证环境中评估其性能的适当措施。没有单一的衡量标准可以描述预测的不同组成部分，建议使用多种汇总衡量标准。
The increasing availability and use of predictive models to facilitate informed decision making highlights the need for careful assessment of the validity of these models. In particular, models involving biomarkers require careful validation for two reasons: issues with overfitting when complex models involve a large number of biomarkers, and inter-laboratory variation in assays used to measure biomarkers. In this paper we distinguish between internal and external statistical validation. Internal validation, involving training-testing splits of the available data or cross-validation, is a necessary component of the model building process and can provide valid assessments of model performance. External validation consists of assessing model performance on one or more datasets collected by different investigators from different institutions. External validation is a more rigorous procedure necessary for evaluating whether the predictive model will generalize to populations other than the one on which it was developed. We stress the need for an external dataset to be truly external, that is, to play no role in model development and ideally be completely unavailable to the researchers building the model. In addition to reviewing different types of validation, we describe different types and features of predictive models and strategies for model building, as well as measures appropriate for assessing their performance in the context of validation. No single measure can characterize the different components of the prediction, and the use of multiple summary measures is recommended.