  As in many fields of medical research, survival analysis has witnessed a
growing interest in the application of deep learning techniques to model
complex, high-dimensional, heterogeneous, incomplete, and censored medical
data. Current methods often make assumptions about the relations between data
that may not be valid in practice. In response, we introduce SAVAE (Survival
Analysis Variational Autoencoder), a novel approach based on Variational
Autoencoders. SAVAE contributes significantly to the field by introducing a
tailored ELBO formulation for survival analysis, supporting various parametric
distributions for covariates and survival time (as long as the log-likelihood
is differentiable). It offers a general method that consistently performs well
on various metrics, demonstrating robustness and stability through different
experiments. Our proposal effectively estimates time-to-event, accounting for
censoring, covariate interactions, and time-varying risk associations. We
validate our model in diverse datasets, including genomic, clinical, and
demographic data, with varying levels of censoring. This approach demonstrates
competitive performance compared to state-of-the-art techniques, as assessed by
the Concordance Index and the Integrated Brier Score. SAVAE also offers an
interpretable model that parametrically models covariates and time. Moreover,
its generative architecture facilitates further applications such as
clustering, data imputation, and the generation of synthetic patient data
through latent space inference from survival data.
