Survival Cluster Analysis
This addresses the unmet need for better characterizing individual outcomes in survival analysis by accounting for phenotypic heterogeneity, though it appears incremental as it builds on existing Bayesian nonparametrics methods.
The paper tackles the problem of population-level heterogeneity in survival analysis by proposing a Bayesian nonparametrics approach that identifies subpopulations with distinct risk profiles while improving individualized time-to-event predictions, showing consistent improvements in predictive performance and interpretability on real-world datasets.
Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population-level phenotypic heterogeneity, resulting from (unknown) subpopulations with diverse risk profiles or survival distributions. As a result, there is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles, while jointly accounting for accurate individualized time-to-event predictions. An approach that addresses this need is likely to improve characterization of individual outcomes by leveraging regularities in subpopulations, thus accounting for population-level heterogeneity. In this paper, we propose a Bayesian nonparametrics approach that represents observations (subjects) in a clustered latent space, and encourages accurate time-to-event predictions and clusters (subpopulations) with distinct risk profiles. Experiments on real-world datasets show consistent improvements in predictive performance and interpretability relative to existing state-of-the-art survival analysis models.