王纯杰：Bayesian hierarchical model of clustered current status data with the additive hazards model

Abstact:  

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.