Left barrier loss for unbiased survival analysis prediction

Survival analysis (SA) prediction involves the prediction of the time until an event of interest occurs (TTE), based on input attributes. The main challenge of SA is instances where the event is not observed (censored), typically through an alternative (censoring) event. Most SA prediction methods suffer from drawbacks limiting the usage of advanced machine learning methods: Ignoring the input of the censored samples, no separation between model and loss, typical small datasets and high input dimensions.

We show that current approaches misinterpret the event likelihood, and propose a loss function, denoted suRvival Analysis lefT barrIer lOss (RATIO), that explicitly incorporates the censored samples input in the prediction. RATIO accounts for the difference between censored and uncensored samples, by only considering censoring events occurring after the predicted, and through a linear term on the uncensored data event time. RATIO can be used with any prediction model. We further propose FIESTA, a data augmentation method, combining the TTE of uncensored samples with the input of censored samples.

We show that RATIO drastically improves the precision and reduces the bias of SA prediction in both models and real-life SA problems, and FIESTA allows for the inclusion of high-dimension data in SA methods even with a small number of uncensored samples.

Based on joint work with Oshrit Shtossel and Omry Koren.