Bayesian Analysis of Survival Data with Missing Censoring Indicators and Simulation of Interval Censored Dat

In some large clinical studies, it may be impractical to give physical examinations to every subject at his/her last monitoring time in order to diagnose the occurrence of an event of interest. This challenge creates survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring. We present a fully Bayesian semi-parametric method for such survival data to estimate regression parameters of Cox's proportional hazards model Cox. Simulation studies show that our method performs better than competing methods. We apply the proposed method to data from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study.;Clinical studies often include interval censored data. We present a method for the simulation of interval censored data based on Poisson processes. We show that our method gives simulated data that fulfills the assumption of independent interval censoring, and is more computationally efficient that other methods used for simulating interval censored data.