The receiver operating-characteristic (ROC) curve is a graphical statistical tool routinely used for studying the classification accuracy in both, diagnostic and prognosis problems. Given the different nature of these situations, ROC curve estimation has been separately considered for binary (diagnostic) and time-to-event (prognosis) outcomes, even for data coming from the same study design. In this work, the authors propose a two-stage ROC curve estimator which allows to link both contexts through a general prediction model (first-stage) and the empirical cumulative estimator of the distribution function (second-stage) of the considered test (marker) on the total population. The so-called two-stage Mixed-Subject (sMS) approach proves its behavior on both, large-samples (theoretically) and finite-samples (via Monte Carlo simulations). Besides, a useful asymptotic distribution for the concomitant area under the curve is also computed. Results show the ability of the proposed estimator to fit non-standard situations by considering flexible predictive models. Two real-world examples, one with binary and one with time-dependent outcomes, help us to a better understanding of the proposed methodology on usual practical circumstances. The R code used for the practical implementation of the proposed methodology and its documentation is provided as supplementary material.

中文翻译：

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用于队列研究的两阶段接收器操作特性曲线估计器

接收器操作特征 (ROC) 曲线是一种图形统计工具，通常用于研究诊断和预后问题的分类准确性。鉴于这些情况的不同性质，即使对于来自相同研究设计的数据，ROC 曲线估计也被单独考虑用于二元（诊断）和事件发生时间（预后）结果。在这项工作中，作者提出了一个两阶段 ROC 曲线估计器，它允许通过一般预测模型（第一阶段）和所考虑测试的分布函数（第二阶段）的经验累积估计器（标记) 总人口。所谓的两阶段混合主题 (sMS) 方法证明了其在大样本（理论上）和有限样本（通过蒙特卡罗模拟）上的行为。除了，还计算了曲线下伴随面积的有用渐近分布。结果表明，通过考虑灵活的预测模型，建议的估计器能够适应非标准情况。两个真实世界的例子，一个是二元的，一个是时间依赖的结果，帮助我们在通常的实际情况下更好地理解所提出的方法。用于实际实施所提议方法的 R 代码及其文档作为补充材料提供。帮助我们在通常的实际情况下更好地理解所提议的方法。用于实际实施所提议方法的 R 代码及其文档作为补充材料提供。帮助我们在通常的实际情况下更好地理解所提议的方法。用于实际实施所提议方法的 R 代码及其文档作为补充材料提供。