Towards Filling the Gaps around Recurrent Events in High-Dimensional Framework: Literature Review and Early Comparison

Abstract

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, the proportion of active variables, and the number of events. Learning algorithms from the LR were compared to standard methods in such simulation scheme. Evaluation measures were Harrell's concordance 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 reviews. 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 RandkDeepSurv 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.