A Bayesian semiparametric accelerated failure time model for arbitrarily censored data with covariates subject to measurement error. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Lin, Xiaoyan
• Is Part Of: Communications in statistics. Volume 46:Issue 1(2017)
• Page Start: 747
• Page End: 756
• Publication Date: 2017-01-02
• Subjects: Arbitrarily censored -- Dirichlet process mixture -- Measurement error -- Semiparametric AFT model; 62 Statistics -- 62N86 Fuzziness, and survival analysis and censored data; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2014.977919
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: A flexible Bayesian semiparametric accelerated failure time (AFT) model is proposed for analyzing arbitrarily censored survival data with covariates subject to measurement error. Specifically, the baseline error distribution in the AFT model is nonparametrically modeled as a Dirichlet process mixture of normals. Classical measurement error models are imposed for covariates subject to measurement error. An efficient and easy-to-implement Gibbs sampler, based on the stick-breaking formulation of the Dirichlet process combined with the techniques of retrospective and slice sampling, is developed for the posterior calculation. An extensive simulation study is conducted to illustrate the advantages of our approach.