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Background Study individuals may face repeated events overtime. However,there is no consensus around learning approaches to use in a high-dimensionalframework for survival data (when the number of variables exceeds the number ofindividuals, i.e., p>n). This study aimed at identifying learning algorithmsfor analyzing/predicting recurrent events and at comparing them to standardstatistical models in various data simulation settings. Methods A literaturereview (LR) was conducted to provide state-of-the-art methodology. Data werethen simulated including variations of the number of variables and proportionof active variables. Learning algorithms from the LR were compared to standardmethods in such simulation scheme. Evaluation measures were Harrell'sconcordance index (C-index), Kim's C-index and error rate for active variables.Results Seven publications were identified, consisting in four methodologicalstudies, one application paper and two review. The broken adaptive ridgepenalization and the RankDeepSurv deep neural network were used for comparison.On simulated data, the standard models failed when p>n. Penalized Andersen-Gilland frailty models outperformed, whereas RankDeepSurv reported lowerperformances. Conclusion As no guidelines support a specific approach, thisstudy helps to better understand mechanisms and limits of investigated methodsin such context.

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