In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring and some covariates. We present a fully Bayesian semi-parametric method for such survival data to estimate regression parameters of the proportional hazards model of Cox (1972). Theoretical investigation and simulation studies show that our method performs better than competing methods. We apply the proposed method to analyze the survival data with missing censoring indicators from the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) study. This article is protected by copyright. All rights reserved.

中文翻译：

---

缺少审查指标的生存数据的贝叶斯分析

在一些大型临床研究中，在每个受试者的最后一次监测时间对其进行身体检查以诊断感兴趣事件的发生可能是不切实际的。这会产生缺少审查指标的生存数据，其中缺失的概率可能取决于上次监测的时间和一些协变量。我们为此类生存数据提出了一种完全贝叶斯半参数方法，以估计 Cox (1972) 的比例风险模型的回归参数。理论调查和模拟研究表明，我们的方法比竞争方法表现更好。我们应用所提出的方法来分析口腔疼痛：前瞻性评估和风险评估 (OPPERA) 研究中缺少审查指标的生存数据。本文受版权保护。