Bayesian empirical analysis of the proportional hazards model for right-censored failure time data

Abstract

Statistical analysis of right-censored failure time data has been extensively discussed in the literature as such data often occur in many fields. In this article, we propose a new Bayesian-based empirical likelihood approach for the problem under the proportional hazards model. The new method allows one to take into account the existing prior information among other advantages, and for the implementation of the method, a Metropolis–Hasting algorithm is developed. To assess the performance of the proposed approach, a simulation study is conducted and suggests that it works well. The method is applied to a set of kidney dialysis data.