Reviews: Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks

The model has two appealing characteristics. First, it allows predictors to affect the hazard function non-linearly. Second, the non-linearity is formulated using latent "sub-events" that compete to determine when an observable event of interest will occur. This arguably makes the non-linearity more interpretable than approaches like random forests or multilayer perceptrons. Moreover, the number of sub-events is specified using a nonparameteric Bayesian model and so model complexity can adapt to the problem.