The receiver operating characteristic (ROC) curve has become one of the most used tools for analyzing the diagnostic capacity of continuous biomarkers. When the studied outcome is a time-dependent variable two main generalizations have been proposed, based on properly extensions of the sensitivity and the specificity. Different procedures have been suggested for their estimation mainly under the presence of right censorship. Most of them have been implemented, as well, in diverse types of software, including R packages. This work focuses on the R implementation for the smooth estimation of time-dependent ROC curves. The theoretical connection between them through the joint distribution function of the biomarker and time-to-event variables prompts an approximation method: considered estimators are based on the bivariate kernel density estimator for the joint density function of the bidimensional variable (Marker, Time-to-event). The use of the package is illustrated with two real-world examples.

中文翻译：

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smoothROCtime：R包，用于依赖时间的ROC曲线估计

接收器工作特性（ROC）曲线已成为分析连续生物标记物诊断能力的最常用工具之一。当研究结果是随时间变化的变量时，基于敏感性和特异性的适当扩展，提出了两个主要的概括。已经提出了主要在权利审查制度下进行估计的不同程序。它们中的大多数也已通过包括R包在内的各种软件来实现。这项工作专注于R平滑估计与时间有关的ROC曲线的实现。它们之间通过生物标记物的联合分布函数与事件发生时间变量之间的理论联系提出了一种近似方法：考虑的估计量基于二维变量的联合密度函数的二元核密度估计量（标记，时间到-event）。该软件包的用法通过两个实际示例进行了说明。