The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models
The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models
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基于残差的预测曲线：评估预测模型性能的可视化工具
作者：
Casalicchio;B. Bischl;A.-L. Boulesteix;M. Schmid
生物统计学家一致认为，二元结果的预测模型应满足两个基本标准：第一，预测模型应具有较高的区分能力，这意味着它能够明确区分病例和对照。其次，模型应该经过很好的校准，这意味着预测的风险应该与数据中观察到的相对频率密切一致。这项工作的重点是预测性曲线，这是由黄等人提出的。（生物统计学63，2007）作为一个图形工具，以评估上述标准。通过对其性质的详细分析，我们回顾了预测性曲线在生物医学预测模型性能评估中的作用。特别是，我们证明了标记物比较不应该仅仅基于预测性曲线，因为通过比较从竞争模型获得的预测性曲线，不可能一致地可视化新标记物的附加预测值。基于我们的分析，我们提出了“基于残差的预测性曲线”（RBP曲线），它解决了上述问题，并将原始方法扩展到对独立测试数据的预测模型评估特别感兴趣的设置。与预测性曲线类似，RBP曲线反映了预测模型的校准和区分能力。此外，该曲线可以方便地用于进行有效的性能检查和标记物比较。
It is agreed among biostatisticians that prediction models for binary outcomes should satisfy two essential criteria: first, a prediction model should have a high discriminatory power, implying that it is able to clearly separate cases from controls. Second, the model should be well calibrated, meaning that the predicted risks should closely agree with the relative frequencies observed in the data. The focus of this work is on the predictiveness curve, which has been proposed by Huang et al. (Biometrics 63, 2007) as a graphical tool to assess the aforementioned criteria. By conducting a detailed analysis of its properties, we review the role of the predictiveness curve in the performance assessment of biomedical prediction models. In particular, we demonstrate that marker comparisons should not be based solely on the predictiveness curve, as it is not possible to consistently visualize the added predictive value of a new marker by comparing the predictiveness curves obtained from competing models. Based on our analysis, we propose the “residual‐based predictiveness curve” (RBP curve), which addresses the aforementioned issue and which extends the original method to settings where the evaluation of a prediction model on independent test data is of particular interest. Similar to the predictiveness curve, the RBP curve reflects both the calibration and the discriminatory power of a prediction model. In addition, the curve can be conveniently used to conduct valid performance checks and marker comparisons.
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回复：“弗雷明汉心血管风险概况的临床实用性超出其统计表现：德黑兰脂质和血糖研究”。
影响因子：
5
作者：
Janes,Holly;Pepe,Margaret
通讯作者：
Pepe,Margaret
DOI：
10.1001/jama.2011.593
发表时间：
2011-05-11
期刊：
JAMA
影响因子：
--
作者：
Hatzis C;Pusztai L;Valero V;Booser DJ;Esserman L;Lluch A;Vidaurre T;Holmes F;Souchon E;Wang H;Martin M;Cotrina J;Gomez H;Hubbard R;Chacón JI;Ferrer-Lozano J;Dyer R;Buxton M;Gong Y;Wu Y;Ibrahim N;Andreopoulou E;Ueno NT;Hunt K;Yang W;Nazario A;DeMichele A;O'Shaughnessy J;Hortobagyi GN;Symmans WF
通讯作者：
Symmans WF
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：
--
发表时间：
2014
期刊：
Biometrical journal. Biometrische Zeitschrift
影响因子：
--
作者：
E. Steyerberg;M. Vedder;M. Leening;D. Postmus;R. D'Agostino;B. Van calster;M. Pencina
通讯作者：
M. Pencina
影响因子：
1.9
作者：
Huang, Ying;Pepe, Margaret Sullivan;Feng, Ziding
通讯作者：
Feng, Ziding
反驳南希·库克关于“总结和比较标记预测能力的措施”的评论
DOI：
10.2202/1557-4679.1280
发表时间：
2010
期刊：
The International Journal of Biostatistics
影响因子：
--
作者：
Erdal Yiğit
通讯作者：
Erdal Yiğit