Bayesian variable selection for proportional hazards model with current status data

A Bayesian proportional hazards (PH) model is proposed for analyzing current status data based on Expectation-Maximization Variable Selection (EMVS) method. This model can estimate parameters and select variables simultaneously, which efficiently improves model interpretability and predictive ability. To identify risk factors, appropriate priors are assigned on the indicator variables that denote the existence of covariates. The baseline cumulative hazard function is approximated via monotone splines. A novel Expectation-Maximization (EM) algorithm is developed for model fitting by using a two-stage data augmentation procedure involving latent Poisson variables. Finally, the performance of proposed method is investigated by simulations and a real data analysis.