Survival stacking with multiple data types using pseudo-observation-based-AUC loss

There have been many strategies to adapt machine learning algorithms to account for right censored observations in survival data in order to build more accurate risk prediction models. These adaptions have included pre-processing steps such as pseudo-observation transformation of the survival outcome or inverse probability of censoring weighted (IPCW) bootstrapping of the observed binary indicator of an event prior to a time point of interest. These pre-processing steps allow existing or newly developed machine learning methods, which were not specifically developed with time-to-event data in mind, to be applied to right censored survival data for predicting the risk of experiencing an event. Stacking or ensemble methods can improve on risk predictions, but in general, the combination of pseudo-observation-based algorithms, IPCW bootstrapping, IPC weighting of the methods directly, and methods developed specifically for survival has not been considered in the same ensemble. In this paper, we propose an ensemble procedure based on the area under the pseudo-observation-based-time-dependent ROC curve to optimally stack predictions from any survival or survival adapted algorithm. The real application results show that our proposed method can improve on single survival based methods such as survival random forest or on other strategies that use a pre-processing step such as inverse probability of censoring weighted bagging or pseudo-observations alone.