Survival Cluster Analysis
- URL: http://arxiv.org/abs/2003.00355v1
- Date: Sat, 29 Feb 2020 22:41:21 GMT
- Title: Survival Cluster Analysis
- Authors: Paidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta, Lawrence Carin, Ricardo
Henao
- Abstract summary: There is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles.
An approach that addresses this need is likely to improve characterization of individual outcomes.
- Score: 93.50540270973927
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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