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Statistical Analysis of Failure Time Data with Missing Information.

机译：故障时间数据缺失信息的统计分析。

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摘要

Failure time data arise in many fields and can involve different types of censoring structures and missing information. We consider three cases: right-censored data with missing censoring indicators, clustered current status data, and clustered interval-censored data. Right-censored data with missing indicators appear when the censoring indicator, the information if the observed time is the survival time of interest or the censoring time, is missing. Clustered current status data arise when the failure times of interest are clustered into small groups and the observed times are either left- or right-censored. Clustered interval-censored data arise when the failure times of interest are clustered into small groups and the observed times are known to fall within certain intervals.;In Chapter 1, three real-life examples are discussed to illustrate right-censored data with missing censoring indicators, clustered current status data and clustered interval-censored data. Also we will review the existing literature on statistical analysis of right-censored failure time data with missing censoring indicators, current status data, interval-censored data and general clustered failure time data.;Chapter 2 discusses regression analysis of right-censored failure time data with missing censoring indicators and presents an efficient estimation procedure based on the EM algorithm. The simulation study performed indicates that the proposed methodology performs well for practical situations. An illustrative example from a breast cancer clinical trial is provided.;Chapter 3 discusses regression analysis of clustered current status data. For inference, a Cox frailty model and a two-step EM algorithm are presented. A simulation study was conducted for the evaluation of the proposed methodology and indicates that the approach performs well for practical situations. An illustrative example from a tumorigenicity experiment is provided.;Chapter 4 generalizes the study of Chapter 3 to clustered interval-censored data. For inference, similar Cox frailty model and two-steps EM algorithm are adopted. Due to the more complex structure of the censoring mechanism, the EM algorithm and the inference procedure are much more complicated for clustered interval-censored data. A simulation study indicates that the approach performs well for practical situations. An illustrative example from a lymphatic filariasis study is provided.;Chapter 5 discusses some directions for future research.
机译：故障时间数据出现在许多领域，并且可能涉及不同类型的审查结构和信息丢失。我们考虑三种情况：缺少检查指标的右检查数据，聚类的当前状态数据和聚类的间隔检查数据。当检查指示符丢失时，将显示缺少指示符的右检查数据，该信息包括观察时间是目标生存时间还是检查时间。当关注的故障时间分为几类，并且观察到的时间被左或右截尾时，会出现聚集的当前状态数据。当将感兴趣的故障时间分为几类，并且观察到的已知时间落在一定的时间间隔内时，便会出现时间间隔聚类的数据；在第1章中，我们讨论了三个真实的例子，以说明在缺少检查的情况下进行右删失的数据指示器，聚集的当前状态数据和聚集的间隔检查数据。我们还将回顾现有的关于缺少删失检查指标的右删失时间数据统计分析的文献，当前状态数据，间隔删失数据和一般聚类的故障时间数据。第二章讨论了右删失时间数据的回归分析。缺少检查指标，并提出了一种基于EM算法的有效估算程序。进行的仿真研究表明，所提出的方法在实际情况下效果很好。提供了一个来自乳腺癌临床试验的说明性示例。第三章讨论了聚类的当前状态数据的回归分析。为了进行推断，提出了Cox脆弱模型和两步EM算法。进行了仿真研究，以评估所提出的方法，并表明该方法在实际情况下效果很好。提供了一个来自致瘤性实验的示例。第四章将第3章的研究概括为聚类的区间删节数据。为了进行推断，采用了相似的Cox脆弱模型和两步EM算法。由于检查机制的结构更加复杂，因此对于聚类间隔检查的数据，EM算法和推理过程要复杂得多。仿真研究表明，该方法在实际情况下效果很好。提供了淋巴丝虫病研究的一个示例性例子。第5章讨论了未来研究的一些方向。

著录项

  • 作者

    Chen, Ping.;

  • 作者单位

    University of Missouri - Columbia.;

  • 授予单位 University of Missouri - Columbia.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 81 p.
  • 总页数 81
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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