Statistical learning methods are widely used in medical literature for the purpose of diagnosis or prediction. Conventional accuracy assessment via sensitivity, specificity, and ROC curves does not fully account for clinical utility of a specific model. Decision curve analysis (DCA) becomes a novel complement as it incorporates a clinical judgment of the relative value of benefits (treating a true positive case) and harms (treating a false positive case) associated with prediction models. The preference of a patient or a policy‐maker is formulated statistically as the underlying threshold probability, above which the patient would choose to be treated. Net benefit is then calculated for possible threshold probability, which places benefits and harms on the same scale. We consider the inference problems for DCA in this paper. Interval estimation procedure and inference methodology are provided after we derive the relevant asymptotic properties. Our formulation can accommodate the classification problems with multiple categories. We carry out numerical studies to assess the performance of the proposed methods. An eye disease dataset is analyzed to illustrate our proposals.

中文翻译：

---

用于决策曲线分析的统计推断，并应用于白内障诊断。

统计学习方法被广泛用于医学文献中以进行诊断或预测。通过敏感性，特异性和ROC曲线进行的常规准确性评估不能完全说明特定模型的临床实用性。决策曲线分析（DCA）成为一种新颖的补充，因为它结合了对与预测模型相关的收益（处理真实阳性病例）和危害（处理虚假阳性病例）相对价值的临床判断。从统计学上将患者或政策制定者的偏好定义为潜在的阈值概率，高于该阈值概率患者将选择接受治疗。然后，针对可能的阈值概率计算净收益，这会将收益和危害置于相同的规模。在本文中，我们考虑了DCA的推理问题。推导了相关的渐近性质后，提供了区间估计程序和推理方法。我们的公式可以解决多个类别的分类问题。我们进行数值研究以评估所提出方法的性能。分析眼睛疾病数据集以说明我们的建议。