Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores
This work addresses the challenge of robust and efficient survival analysis for time-to-event modeling, offering a method that avoids computational inefficiencies and hyperparameter tuning, though it appears incremental in its approach.
The paper tackles the problem of improving survival analysis models by decoupling them into an aggregated baseline hazard and independent survival scores, achieving competitive performance with state-of-the-art methods on real-world datasets without requiring fine-tuning.
Survival Analysis (SA) constitutes the default method for time-to-event modeling due to its ability to estimate event probabilities of sparsely occurring events over time. In this work, we show how to improve the training and inference of SA models by decoupling their full expression into (1) an aggregated baseline hazard, which captures the overall behavior of a given population, and (2) independently distributed survival scores, which model idiosyncratic probabilistic dynamics of its given members, in a fully parametric setting. The proposed inference method is shown to dynamically handle right-censored observation horizons, and to achieve competitive performance when compared to other state-of-the-art methods in a variety of real-world datasets, including computationally inefficient Deep Learning-based SA methods and models that require MCMC for inference. Nevertheless, our method achieves robust results from the outset, while not being subjected to fine-tuning or hyperparameter optimization.