讲座题目：Bayesian hierarchical model of clustered current status data with the additive hazards model
The additive hazard regression (AH) model is known for its convenience in interpretation, as hazard is modeled as a linear function of covariates. In this paper, we investigate bayesian hierarchical model with additive hazard model under cluster current status data. An efficient computational scheme based on the Matropolis-Hastings algorithm is developed and has been implemented in R software. The proposed approach adopts the piecewise exponential model (PEM) and weibull distribution to model the log-baseline hazard function and allows to estimate the regression parameters and the baseline hazard function simultaneously. A simulation study is presented to show that the proposed method performs well with a finite sample and is easy to use in practice. The proposed methodology is further demonstrated by applying it to a lymphatic filariasis study.