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.

Keywords:

• Bayesian-based empirical likelihood
• Estimating equation
• Metropolis– Hasting algorithm
• Proportional hazards model
• Right-censored data

MATHEMATICS SUBJECT CLASSIFICATION:

• 62F15(Bayesian inference); 62N02(Estimation in survival analysis and censored data)

Additional information

Funding

This work was partly supported by the National Natural Science Foundation of China Grant Nos. 11901054, 11671054 and the Tian Yuan Mathematical Foundation of National Natural Science Foundation of China (Nos. 11926340, 11926341) and the School Foundation of Heilongjiang Bayi Agricultural University (RRCRY201906 and TDJH201908).