Net reclassification indices for evaluating risk prediction instruments: a critical review.
Net reclassification indices for evaluating risk prediction instruments: a critical review.
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DOI：
10.1097/ede.0000000000000018
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发表时间：
2014-01
期刊：
影响因子：
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通讯作者：
Pepe MS
中科院分区：
文献类型：
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作者：
Kerr KF;Wang Z;Janes H;McClelland RL;Psaty BM;Pepe MS
净重分类指数近来已成为衡量新生物标志物预测增量的流行统计方法。我们回顾了净重分类指数的各种类型及其正确的解释。我们评估了用这些指标量化预测增量的优缺点。对于预定义的风险类别，我们将净重分类指标与现有的预测增量度量相关联。我们还考虑了构建净重分类指标的可信区间的统计方法，并评估了基于这些指标的假设检验的优点。我们建议使用净重分类指数的调查人员应分别报告事件(病例)和非事件(对照)。当存在两个风险类别时，净重分类指数的分量与真阳性率和假阳性率的变化相同。我们主张使用真阳性率和假阳性率，并建议调查人员保留现有的描述性术语更有用。当存在三个或更多风险类别时，我们建议不要使用净重分类指数，因为它们不能充分考虑风险类别之间转移的临床重要差异。无类别净重分类指数是一种新的描述性工具，旨在避免预先定义的风险类别。然而，它面临着许多与其他测量方法相同的问题，如接收器工作特性曲线下的面积。此外，即使在独立的验证数据中，无类别指数也会夸大生物标记物的增量价值，从而误导调查人员。当研究人员想要检验一个没有预测增量的零假设时，回归模型中系数的成熟检验优于净重分类指数。如果调查人员想要使用净重分类指数，则应使用Bootstrap方法而不是已公布的方差公式来计算可信区间。预测增量的首选单数汇总是净收益的改善。
Net reclassification indices have recently become popular statistics for measuring the prediction increment of new biomarkers. We review the various types of net reclassification indices and their correct interpretations. We evaluate the advantages and disadvantages of quantifying the prediction increment with these indices. For pre-defined risk categories, we relate net reclassification indices to existing measures of the prediction increment. We also consider statistical methodology for constructing confidence intervals for net reclassification indices and evaluate the merits of hypothesis testing based on such indices. We recommend that investigators using net reclassification indices should report them separately for events (cases) and nonevents (controls). When there are two risk categories, the components of net reclassification indices are the same as the changes in the true-positive and false-positive rates. We advocate use of true- and false-positive rates and suggest it is more useful for investigators to retain the existing, descriptive terms. When there are three or more risk categories, we recommend against net reclassification indices because they do not adequately account for clinically important differences in shifts among risk categories. The category-free net reclassification index is a new descriptive device designed to avoid pre-defined risk categories. However, it suffers from many of the same problems as other measures such as the area under the receiver operating characteristic curve. In addition, the category-free index can mislead investigators by overstating the incremental value of a biomarker, even in independent validation data. When investigators want to test a null hypothesis of no prediction increment, the well-established tests for coefficients in the regression model are superior to the net reclassification index. If investigators want to use net reclassification indices, confidence intervals should be calculated using bootstrap methods rather than published variance formulas. The preferred single-number summary of the prediction increment is the improvement in net benefit.