Criteria for evaluating risk prediction of multiple outcomes.
Criteria for evaluating risk prediction of multiple outcomes.
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DOI：
10.1177/0962280220929039
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发表时间：
2020-12
影响因子：
2.3
通讯作者：
Dudbridge F
中科院分区：
文献类型：
--
作者：
Dudbridge F
风险预测模型已经在许多情况下开发，以根据单一结果（例如疾病风险）对个体进行分类。新兴的“组学”生物标志物提供了一系列特征，可以同时预测来自单个生物样本的多种结果，从而产生了令人想起探索性假设检验的多重性问题。在这里，我提出了一些基本标准的定义，用于评估多个结果的预测模型。我在多变量设置中定义校准，然后区分结果明智和个人明智的预测，并在后者之间的联合和面板明智的预测。我举了一些例子，比如筛查和早期发现，在这些例子中，不同的预测意义可能更合适。在每种情况下，我提出的敏感性，特异性，一致性，阳性和阴性预测值和相对效用的定义。我通过一个多变量概率模型的定义，表明多变量预测模型的准确性，可以总结其协方差与负债向量。我举例说明了生物标志物面板上的概念，用于早期检测八种癌症，以及六种常见疾病的多基因风险评分。
Risk prediction models have been developed in many contexts to classify individuals according to a single outcome, such as risk of a disease. Emerging “-omic” biomarkers provide panels of features that can simultaneously predict multiple outcomes from a single biological sample, creating issues of multiplicity reminiscent of exploratory hypothesis testing. Here I propose definitions of some basic criteria for evaluating prediction models of multiple outcomes. I define calibration in the multivariate setting and then distinguish between outcome-wise and individual-wise prediction, and within the latter between joint and panel-wise prediction. I give examples such as screening and early detection in which different senses of prediction may be more appropriate. In each case I propose definitions of sensitivity, specificity, concordance, positive and negative predictive value and relative utility. I link the definitions through a multivariate probit model, showing that the accuracy of a multivariate prediction model can be summarised by its covariance with a liability vector. I illustrate the concepts on a biomarker panel for early detection of eight cancers, and on polygenic risk scores for six common diseases.
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