Using the weighted area under the net benefit curve for decision curve analysis.
Using the weighted area under the net benefit curve for decision curve analysis.
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DOI：
10.1186/s12911-016-0336-x
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发表时间：
2016-07-18
影响因子：
3.5
通讯作者：
Shete S
中科院分区：
文献类型：
--
作者：
Talluri R;Shete S
已经提出了各种疾病的风险预测模型，并随着新的预测因素的确定而得到改进。一个主要的挑战是确定新发现的预测因子是否改善了风险预测。决策曲线分析已被提出作为曲线下面积和净重新分类指数的替代方法，以评估预测模型在临床场景中的性能。使用净收益计算的决策曲线可以评估风险模型在给定阈值概率或阈值概率范围内的预测性能。然而，当2个竞争模型的决策曲线在感兴趣的范围内交叉时，很难确定最佳模型，因为没有现成的总结措施来评估预测性能。使用净效益曲线下面积等简单指标的主要障碍是假设阈值概率在患者中均匀分布。我们提出了一种新的措施进行决策曲线分析。该方法估计阈值概率的分布，而不需要额外的数据。使用阈值概率的估计分布，净效益曲线下的加权面积用作在感兴趣的范围内比较风险预测模型的汇总度量。我们比较了3种不同的方法，标准方法，净收益曲线下面积和净收益曲线下加权面积。第1类误差和功效比较表明，与其他方法相比，净效益曲线下加权面积具有更高的功效。几个模拟研究，以证明改进模型比较使用加权面积下的净效益曲线相比，标准方法。所提出的措施通过使用加权曲线下面积来改进决策曲线分析，从而提高了决策曲线分析在临床场景中比较风险预测模型的能力。本文的在线版本（doi：10.1186/s12911-016-0336-x）包含补充材料，可供授权用户使用。
Risk prediction models have been proposed for various diseases and are being improved as new predictors are identified. A major challenge is to determine whether the newly discovered predictors improve risk prediction. Decision curve analysis has been proposed as an alternative to the area under the curve and net reclassification index to evaluate the performance of prediction models in clinical scenarios. The decision curve computed using the net benefit can evaluate the predictive performance of risk models at a given or range of threshold probabilities. However, when the decision curves for 2 competing models cross in the range of interest, it is difficult to identify the best model as there is no readily available summary measure for evaluating the predictive performance. The key deterrent for using simple measures such as the area under the net benefit curve is the assumption that the threshold probabilities are uniformly distributed among patients. We propose a novel measure for performing decision curve analysis. The approach estimates the distribution of threshold probabilities without the need of additional data. Using the estimated distribution of threshold probabilities, the weighted area under the net benefit curve serves as the summary measure to compare risk prediction models in a range of interest. We compared 3 different approaches, the standard method, the area under the net benefit curve, and the weighted area under the net benefit curve. Type 1 error and power comparisons demonstrate that the weighted area under the net benefit curve has higher power compared to the other methods. Several simulation studies are presented to demonstrate the improvement in model comparison using the weighted area under the net benefit curve compared to the standard method. The proposed measure improves decision curve analysis by using the weighted area under the curve and thereby improves the power of the decision curve analysis to compare risk prediction models in a clinical scenario. The online version of this article (doi:10.1186/s12911-016-0336-x) contains supplementary material, which is available to authorized users.