caa a22 4500 605476101 CHVBK 20210128100354.0 cr unu---uuuuu 210128e20150701xx s 000 0 eng 10.1007/s10985-014-9305-9 doi (NATIONALLICENCE)springer-10.1007/s10985-014-9305-9 A Bayesian proportional hazards model for general interval-censored data [Elektronische Daten] [Xiaoyan Lin, Bo Cai, Lianming Wang, Zhigang Zhang] The proportional hazards (PH) model is the most widely used semiparametric regression model for analyzing right-censored survival data based on the partial likelihood method. However, the partial likelihood does not exist for interval-censored data due to the complexity of the data structure. In this paper, we focus on general interval-censored data, which is a mixture of left-, right-, and interval-censored observations. We propose an efficient and easy-to-implement Bayesian estimation approach for analyzing such data under the PH model. The proposed approach adopts monotone splines to model the baseline cumulative hazard function and allows to estimate the regression parameters and the baseline survival function simultaneously. A novel two-stage data augmentation with Poisson latent variables is developed for the efficient computation. The developed Gibbs sampler is easy to execute as it does not require imputing any unobserved failure times or contain any complicated Metropolis-Hastings steps. Our approach is evaluated through extensive simulation studies and illustrated with two real-life data sets. Springer Science+Business Media New York, 2014 Interval-censored data nationallicence Monotone splines nationallicence Nonhomogeneous Poisson process nationallicence Proportional hazards model nationallicence Semiparametric regression nationallicence Lin Xiaoyan Department of Statistics, University of South Carolina, 29208, Columbia, SC, USA aut Cai Bo Department of Epidemiology and Biostatistics, University of South Carolina, 29208, Columbia, SC, USA aut Wang Lianming Department of Statistics, University of South Carolina, 29208, Columbia, SC, USA aut Zhang Zhigang Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 10065, New York, NY, USA aut Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/3(2015-07-01), 470-490 1380-7870 21:3<470 2015 21 10985 https://doi.org/10.1007/s10985-014-9305-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10985-014-9305-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lin Xiaoyan Department of Statistics, University of South Carolina, 29208, Columbia, SC, USA aut NATIONALLICENCE 700 1- Cai Bo Department of Epidemiology and Biostatistics, University of South Carolina, 29208, Columbia, SC, USA aut NATIONALLICENCE 700 1- Wang Lianming Department of Statistics, University of South Carolina, 29208, Columbia, SC, USA aut NATIONALLICENCE 700 1- Zhang Zhigang Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 10065, New York, NY, USA aut NATIONALLICENCE 773 0- Lifetime Data Analysis Springer US; http://www.springer-ny.com 21/3(2015-07-01), 470-490 1380-7870 21:3<470 2015 21 10985