标题: | 三种资料结构下双变数存活时间之回归分析 Regression Analysis for Bivariate Failure-Time Data under Three Types of Data Structures |
作者: | 谢进见 Jin-Jian Hsieh 王维菁 Weijing Wang 统计学研究所 |
关键字: | 阿基米得关联模式;双变数存活时间;相关设限;相关截切;局部胜算比值;多重事件资料;半竞争风险资料;转换模型;Archimedean copula;Bivariate failure-time;Dependent censoring;Dependent truncation;Local odds ratio;Multiple events data;Semi-competing risks data;Transformation model |
公开日期: | 2006 |
摘要: | 双变数存活分析已被广泛应用于生物医学的研究。早期的研究课题较偏向于探讨两个不同生物个体或器官组织存活时间之关连性。近年来的应用方向则拓广到同一个体所发生不同事件的时间。后者在分析上,往往伴随所探讨的时间变数间彼此具有设限或截切关系,使得统计推论变得更为复杂。本论文包含两个研究计划,均在回归的架构下分析双变数之存活时间。我们特别针对前面所述特殊的设限或截切资料,提出统计推论的方法。 第一个计划针对半竞争风险资料,探讨解释变数对“中介事件发生时间”的影响。分析的难度在于所欲探讨的时间长度受制于相关设限。大部分文献所提出的方法均利用“人为设限”[artificial censoring]的技巧,以处理相关设限所造成的偏误。不过这个方法因把部份观测值舍弃而会产生估计效率上的损失,亦因添加了额外的模型假设而有缺乏稳健性的缺点。我们提出两阶段估计方法可改善前述方法的缺点。我们亦针对两个所提出的假设,发展模型检验的方法。论文中并推导了大样本性质,并且透过数值分析评估各推论方法在有限样本下的表现。 在第二个计划中,我们建构关联性的回归模式,并且发展一套推论方法可以弹性的分析三种截然不同的资料结构。我们也针对此模式假设,提出模型检验的方法。论文中亦呈现大样本分析与数值分析。 Bivariate survival analysis has received substantial attentions due to its wide applications. The variables of interest may represent failure times occurred to two different biological units or different event times measured from the same subject. In the latter situation, the two failure times may have censoring or truncation relationship which complicates statistical analysis. The thesis contains two projects, both of which consider regression analysis for bivariate survival data. The first project focuses on semi-competing risks data in which a terminal event censors a non-terminal event. In particular we investigate how covariates affect the marginal distribution of the time to a non-terminal event subject to dependent censoring. Most existing methods utilize the technique of artificial censoring to remove the sampling bias. However these approaches may result in efficiency loss and may not be robust under model mis-specification. We propose a two-stage procedure to tackle this problem. We also propose model selection methods to verify the two main assumptions. Large-sample properties are also proved. Numerical analysis is performed to evaluate finite-sample performances of the proposed methods. In the second part of the thesis, we consider the situation that covariates may affect the level of association. We propose a flexible regression model and then develop a unified inference procedure which can be applied to three different types of data structures. For this part, we also present a model checking method for assessing the appropriateness of the Clayton assumption. Large-sample analysis and numerical studies are also presented. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT009226803 http://hdl.handle.net/11536/76899 |
显示于类别: | Thesis |
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