A Novel Model for Competing Risks Data with Masking

掩蔽竞争风险(MCR)模型是研究J元件串联系统寿命(用T表示)和相关失效原因(用C表示)的流行模型。如果其中一个组件出现故障，系统就会出现故障。故障时间可能会被审查，故障原因可能会被屏蔽。调查人员发现，MCR模型中使用的一些假设是错误的。在这个项目中，研究人员提出了一个新的、更现实的模型，这是对MCR模型的重大改进。基于新模型，PI提出研究T和C联合分布的参数、非参数和半参数估计问题。MCR数据出现在众多的医疗和工业应用中。例如，Dinse(1982)提供了胶质母细胞瘤(一种脑癌)患者的进展前时间和进展时患者状态的MCR数据。患者状态可能无法识别。Reiser等人(1995)表明，MCR数据来自对特定类型的IBM PS/2计算机的测试。计算机故障的原因只能缩小到一组可能的原因。T和C联合分布的估计对有效检测故障原因有很大影响。本研究的结果将为MCR数据提供一种新的、现实的模型，并将为分析MCR数据提供统计工具。

A masked competing risks (MCR) model is a popular model for studying the lifetime (denoted by T) and the associated failure cause (denoted by C) of a J-component series system. The system fails if one of its components fails. The failure time might be censored and the failure cause might be masked. The investigator discovered that some of the assumptions used in the MCR modelare erroneous. In this project, the investigator proposes a new and a more realistic model, which is a significant improvement of the MCR model. Based on the new model, the PI proposes to study the parametric, non-parametric and semi-parametric estimation problems of the joint distribution of T and C.The MCR data appear in numerous medical and industrial applications. For example, Dinse (1982) presents the MCR data of time until progression and the patient status at the time of progression for patients with glioblastoma (a cancer of the brain). The patient status may not be identified. Reiser et al.(1995) show that the MCR data arises from the testing of a particular type of IBM PS/2 computer. The cause of failure of a computer may only be narrowed down to a set of possible causes. The estimation of the joint distribution of T and C has a great impact in detecting the failure cause efficiently. The results of this research would provide a novel and realistic model for the MCR data and would provide statistical tools for analyzing the MCR data.