CR-SAVAE: A Parametric Method for Survival Analysis with Competing Risks

Competing risks in survival analysis pose a significant challenge in healthcare, but few methods effectively address this problem. Moreover, the use of deep learning techniques remains limited, with the most prevalent approach, DeepHit, being non-parametric. This often leads to limitations in inter-pretability and statistical inference, which are crucial aspects. We propose a new alternative that harnesses the power of variational autoencoders and deep learning to tackle survival analysis with competing risks within a parametric framework. Our model, CR-SAVAE, allows direct interpretation of covariate effects on survival outcomes and enables researchers to perform robust statistical analysis compared to non-parametric approaches, essential for understanding risk mechanisms and making informed clinical decisions. It provides personalized medicine insight by accurately estimating the cumulative incidence function and avoiding the need for proportional hazards assumptions inherent in other models. Through comprehensive experiments on datasets with varying degrees of censoring and competing risks, we demonstrate the potential of our approach to achieve performance comparable to that of DeepHit based on the concordance index and integrated Brier score. This study highlights the potential of CR-SAVAE to advance survival analysis, improve interpretability, and enable more accurate and personalized clinical decision making in healthcare settings.