影响因子：2.373
所属单位：数学与统计学院
教研室：统计教研室
发表刊物：Statistics in Medicine
关键字：Bayesian method, data augmentation, MCMC algorithm, partly interval-censored data, transformation model
摘要：In many scientific fields, partly interval-censored data, which consist of exactly observed and interval-censored observations on the failure time of interest, appear frequently. However, methodological developments in the analysis of partly interval-censored data are relatively limited and have mainly focused on additive or proportional hazards models. The general linear transformation model provides a highly flexible modeling framework that includes several familiar survival models as special cases. Despite such nice features, the inference procedure for this class of models has not been developed for partly interval-censored data. We propose a fully Bayesian approach coped with efficient Markov chain Monte Carlo methods to fill this gap. A four-stage data augmentation procedure is introduced to tackle the challenges presented by the complex model and data structure. The proposed method is easy to implement and computationally attractive. The empirical performance of the proposed method is evaluated through two simulation studies, and the model is then applied to a dental health study.
论文类型：期刊论文
卷号：41
期号：7
页面范围：1263-1279
是否译文：否
发表时间：2022-03-05
收录刊物：SCI