A Bayesian semiparametric accelerate failure time mixture cure model

Wang Yijun, Wang Weiwei () and Tang Yincai
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Wang Yijun: School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, People’s Republic of China
Wang Weiwei: School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang Province, 310018, People’s Republic of China
Tang Yincai: Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE, School of Statistics, East China Normal University, Shanghai, 200062, People’s Republic of China

The International Journal of Biostatistics, 2022, vol. 18, issue 2, 473-485

Abstract: The accelerated failure time mixture cure (AFTMC) model is widely used for survival data when a portion of patients can be cured. In this paper, a Bayesian semiparametric method is proposed to obtain the estimation of parameters and density distribution for both the cure probability and the survival distribution of the uncured patients in the AFTMC model. Specifically, the baseline error distribution of the uncured patients is nonparametrically modeled by a mixture of Dirichlet process. Based on the stick-breaking formulation of the Dirichlet process, the techniques of retrospective and slice sampling, an efficient and easy-to-implement Gibbs sampler is developed for the posterior calculation. The proposed approach can be easily implemented in commonly used statistical softwares, and its performance is comparable to fully parametric method via comprehensive simulation studies. Besides, the proposed approach is adopted to the analysis of a colorectal cancer clinical trial data.

Keywords: accelerated failure time mixture cure model; Dirichlet process; Gibbs sampler (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2021-0012

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