论文标题
在高维框架中填补围绕复发事件的空白：文献综述和早期比较
Towards Filling the Gaps around Recurrent Events in High-Dimensional Framework: Literature Review and Early Comparison
论文作者
论文摘要
背景研究人员可能会在加时赛中面临重复的事件。但是，在用于生存数据的高维框架中使用学习方法尚无共识（当变量数量超过个人数量时，即P> n时）。这项研究旨在识别用于分析/预测复发事件的学习算法，并将其与各种数据模拟设置中的标准统计模型进行比较。方法进行了文献综述（LR），以提供最先进的方法。然后模拟数据，包括变量数量和活动变量比例的变化。将LR的学习算法与此类模拟方案中的标准方法进行了比较。评估措施是Harrell的一致性指数（C-Index），Kim的C-指数和活动变量的错误率。结果确定了七个出版物，其中包括四个方法论研究，一份申请文件和两项审查。使用折断的自适应山脊惩罚和RankDeepsUrv深神经网络进行比较。在模拟数据上，标准模型在p> n时失败。惩罚的安德森·吉尔（Andersen-Gill）和脆弱模式表现优于表现，而rankdeepsurv报告的表现较低。结论由于没有指南支持特定方法，因此本研究有助于更好地理解这种情况下研究方法的机制和限制。
Background Study individuals may face repeated events overtime. However, there is no consensus around learning approaches to use in a high-dimensional framework for survival data (when the number of variables exceeds the number of individuals, i.e., p>n). This study aimed at identifying learning algorithms for analyzing/predicting recurrent events and at comparing them to standard statistical models in various data simulation settings. Methods A literature review (LR) was conducted to provide state-of-the-art methodology. Data were then simulated including variations of the number of variables and proportion of active variables. Learning algorithms from the LR were compared to standard methods in such simulation scheme. Evaluation measures were Harrell's concordance index (C-index), Kim's C-index and error rate for active variables. Results Seven publications were identified, consisting in four methodological studies, one application paper and two review. The broken adaptive ridge penalization and the RankDeepSurv deep neural network were used for comparison. On simulated data, the standard models failed when p>n. Penalized Andersen-Gill and frailty models outperformed, whereas RankDeepSurv reported lower performances. Conclusion As no guidelines support a specific approach, this study helps to better understand mechanisms and limits of investigated methods in such context.
