Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, 20892, U.S.A.
Stat Med. 2014 Sep 28;33(22):3946-59. doi: 10.1002/sim.6195. Epub 2014 May 13.
An important question in the evaluation of an additional risk prediction marker is how to interpret a small increase in the area under the receiver operating characteristic curve (AUC). Many researchers believe that a change in AUC is a poor metric because it increases only slightly with the addition of a marker with a large odds ratio. Because it is not possible on purely statistical grounds to choose between the odds ratio and AUC, we invoke decision analysis, which incorporates costs and benefits. For example, a timely estimate of the risk of later non-elective operative delivery can help a woman in labor decide if she wants an early elective cesarean section to avoid greater complications from possible later non-elective operative delivery. A basic risk prediction model for later non-elective operative delivery involves only antepartum markers. Because adding intrapartum markers to this risk prediction model increases AUC by 0.02, we questioned whether this small improvement is worthwhile. A key decision-analytic quantity is the risk threshold, here the risk of later non-elective operative delivery at which a patient would be indifferent between an early elective cesarean section and usual care. For a range of risk thresholds, we found that an increase in the net benefit of risk prediction requires collecting intrapartum marker data on 68 to 124 women for every correct prediction of later non-elective operative delivery. Because data collection is non-invasive, this test tradeoff of 68 to 124 is clinically acceptable, indicating the value of adding intrapartum markers to the risk prediction model.
评估额外风险预测标志物时的一个重要问题是如何解释接收者操作特征曲线（AUC）下面积的微小增加。许多研究人员认为 AUC 的变化是一个较差的指标，因为它仅随着具有大比值比的标志物的添加而略有增加。由于纯粹基于统计学理由无法在比值比和 AUC 之间进行选择，因此我们诉诸于决策分析，该分析结合了成本和收益。例如，对以后非择期手术分娩风险的及时估计可以帮助产妇决定是否希望早期进行择期剖宫产以避免以后非择期手术分娩可能带来的更大并发症。用于以后非择期手术分娩的基本风险预测模型仅涉及产前标志物。因为将产时标志物添加到该风险预测模型中会使 AUC 增加 0.02，所以我们质疑这种微小的改进是否值得。关键的决策分析量是风险阈值，即在此风险阈值下，患者在早期择期剖宫产和常规护理之间将无差异。对于一系列风险阈值，我们发现，风险预测的净收益增加需要对 68 至 124 名妇女进行产时标志物数据收集，才能正确预测以后的非择期手术分娩。由于数据收集是无创的，因此这种 68 至 124 的测试权衡在临床上是可以接受的，表明将产时标志物添加到风险预测模型中的价值。
