Graphical assessment of incremental value of novel markers in prediction models: From statistical to decision analytical perspectives
Graphical assessment of incremental value of novel markers in prediction models: From statistical to decision analytical perspectives
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预测模型中新型标记增量价值的图形评估：从统计到决策分析的角度
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Pencina
中科院分区：
文献类型：
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作者：
E. Steyerberg;M. Vedder;M. Leening;D. Postmus;R. D'Agostino;B. Van calster;M. Pencina
新的标志物可以改善诊断和预后结果的预测。我们的目的是审查图形显示和汇总措施的选择，以评估标记物的预测价值超过标准，现成的预测。我们使用先前发表的来自心脏病研究的3264名参与者的数据来说明各种方法，其中183名参与者患有冠心病（10年风险5.6%）。我们考虑了将HDL胆固醇添加到预测模型中的增量值的性能度量。初始评估可能考虑统计学显著性（HR = 0.65，95%置信区间0.53至0.80;似然比p < 0.001），以及各种汇总测量的预测风险分布（密度或箱形图）。在预测性和受试者工作特征曲线中考虑了一系列决策阈值，其中通过添加HDL，曲线下面积（AUC）从0.762增加到0.774。我们可以进一步关注在重新分类图中有和没有事件的参与者的重新分类，并将持续净重新分类改善（NRI）作为汇总测量。当我们专注于一个特定的决策阈值时，敏感性和特异性的变化是核心。我们提出了一个净重新分类风险图，这使我们能够专注于重新分类的人数和他们的事件发生率。概括性指标包括二元AUC、两类NRI和决策分析变量，如净效益（NB）。各种图表和汇总测量可用于评估标志物的增量预测值。净重新分类风险的简单图表提供了对决策影响的重要见解。
New markers may improve prediction of diagnostic and prognostic outcomes. We aimed to review options for graphical display and summary measures to assess the predictive value of markers over standard, readily available predictors. We illustrated various approaches using previously published data on 3264 participants from the Framingham Heart Study, where 183 developed coronary heart disease (10‐year risk 5.6%). We considered performance measures for the incremental value of adding HDL cholesterol to a prediction model. An initial assessment may consider statistical significance (HR = 0.65, 95% confidence interval 0.53 to 0.80; likelihood ratio p < 0.001), and distributions of predicted risks (densities or box plots) with various summary measures. A range of decision thresholds is considered in predictiveness and receiver operating characteristic curves, where the area under the curve (AUC) increased from 0.762 to 0.774 by adding HDL. We can furthermore focus on reclassification of participants with and without an event in a reclassification graph, with the continuous net reclassification improvement (NRI) as a summary measure. When we focus on one particular decision threshold, the changes in sensitivity and specificity are central. We propose a net reclassification risk graph, which allows us to focus on the number of reclassified persons and their event rates. Summary measures include the binary AUC, the two‐category NRI, and decision analytic variants such as the net benefit (NB). Various graphs and summary measures can be used to assess the incremental predictive value of a marker. Important insights for impact on decision making are provided by a simple graph for the net reclassification risk.
DOI：
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发表时间：
2008
期刊：
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影响因子：
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作者：
A. Vickers;A. Cronin;E. Elkin;M. Gonen
通讯作者：
A. Vickers;A. Cronin;E. Elkin;M. Gonen
影响因子：
5
作者：
Pepe, Margaret S.;Feng, Ziding;Zheng, Yingye
通讯作者：
Zheng, Yingye
影响因子：
5
作者：
Pencina, Michael J.;D'Agostino, Ralph B.;Greenland, Philip
通讯作者：
Greenland, Philip