Abstract
Survival analysis is primarily used to identify the time-to-event for events of interest. The Cox proportional hazards model takes advantage that it accounts for the proportionate risks of covariates without estimating exact baseline hazards. However, estimation of exact hazard distribution is always accompanied by estimating baseline hazard function as well as regression parameters. In this study, we adopted nonparametric Bayesian hierarchical model with flexible priors in estimating cumulative baseline hazard function. We assume a monotone step function for the cumulative baseline hazard function, where the number, size, and location of jumps are random. By Estimating the step function through stick-breaking construction, we can obtain a totally data-driven step function.
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