Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
This work addresses survival analysis with competing risks for applications like medical research, offering a novel method to handle non-monotonic effects and missing data, though it appears incremental in the context of existing survival models.
The paper tackles survival analysis with competing risks by proposing Lomax delegate racing (LDR) to model non-monotonic covariate effects and relax the proportional-hazards assumption, achieving distinguished performance in experiments on synthetic and real datasets.
We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event. LDR explains non-monotonic covariate effects by racing a potentially infinite number of sub-risks, and consequently relaxes the ubiquitous proportional-hazards assumption which may be too restrictive. Moreover, LDR is naturally able to model not only censoring, but also missing event times or event types. For inference, we develop a Gibbs sampler under data augmentation for moderately sized data, along with a stochastic gradient descent maximum a posteriori inference algorithm for big data applications. Illustrative experiments are provided on both synthetic and real datasets, and comparison with various benchmark algorithms for survival analysis with competing risks demonstrates distinguished performance of LDR.