• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Statistical Methods for Joint Analysis of Survival Time and Longitudinal Data.
【24h】

Statistical Methods for Joint Analysis of Survival Time and Longitudinal Data.

机译：生存时间和纵向数据联合分析的统计方法。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In biomedical studies, researchers are often interested in the relationship between patients' characteristics or risk factors and both longitudinal outcomes such as quality of life measured over time and survival time. However, despite the progress in the joint analysis for longitudinal data and survival time, investigation on modeling approach to find which factor or treatment can simultaneously improve the patient's quality of life and reduce the risk of death has been limited. In this dissertation, we investigate joint modeling of longitudinal outcomes and survival time. We consider the generalized linear mixed models for the longitudinal outcomes to incorporate both continuous and categorical data and the stratified multiplicative proportional hazards model for the survival data. We study both Gaussian process and distribution free approaches for the random effect characterizing the joint process of longitudinal data and survival time.;We consider three estimation approaches in this dissertation. First, we consider the maximum likelihood approach with Gaussian process for random effects. The random effects, which are introduced into the simultaneous models to account for dependence between longitudinal outcomes and survival time due to unobserved factors, are assumed to follow a multivariate Gaussian process. The full likelihood, obtained by integrating the complete data likelihood over the random effects, is used for estimation. The Expectation-Maximization (EM) algorithm is used to compute the point estimates for the model parameters, and the observed information matrix is adopted to estimate their asymptotic variances. Second, the normality assumption of random effects in the likelihood approach is relaxed. Assuming the underlying distribution of random effects to be unknown, we propose using a mixture of Gaussian distributions as an approximation in estimation. Weights of the mixture components are estimated with model parameters using the EM algorithm, and the observed information matrix is used for estimation of the asymptotic variances of the proposed estimators. For the two maximum likelihood approaches with and without normality assumption of random effects, asymptotic properties of the proposed estimators are investigated and their finite sample properties are assessed via simulation studies. Third, we consider a penalized likelihood approach. This approach is expected to be computationally less intensive than the maximum likelihood approach. It gives a penalty for regarding the random effect as a fixed effect in the likelihood and avoids the need to integrate the likelihood over random effects. The penalized likelihood is obtained through Laplace approximation. We compare the numerical performances of the penalized likelihood method and the EM algorithm used in maximum likelihood estimation for the simultaneous models with Gaussian process for random effects via simulation studies. All the proposed methods in this dissertation are illustrated with the real data from the Carolina Head and Neck Cancer Study (CHANCE).
机译：在生物医学研究中，研究人员通常对患者特征或危险因素与纵向结果（例如随时间和生存时间测得的生活质量）之间的关系感兴趣。然而，尽管在纵向数据和生存时间的联合分析方面取得了进展，但是对于寻找能够同时改善患者生活质量并降低死亡风险的因素或治疗方法的建模方法的研究仍然受到限制。在本文中，我们研究了纵向结果和生存时间的联合建模。我们考虑纵向结果的广义线性混合模型，其中要包含连续数据和分类数据，以及生存数据的分层乘性比例风险模型。我们研究了高斯过程和自由分布方法，以表征纵向数据和生存时间的联合过程的随机效应。；本文考虑了三种估计方法。首先，我们考虑采用高斯过程的最大似然方法来获得随机效应。假定将随机效应引入多元模型中，以说明纵向结果与由于未观察到的因素而导致的生存时间之间的相关性，该随机效应遵循多元高斯过程。通过将完整数据似然度与随机效应积分而获得的完全似然度用于估计。期望最大化（EM）算法用于计算模型参数的点估计，观测数据矩阵用于估计其渐近方差。其次，放宽了似然法中随机效应的正态性假设。假设随机效应的基本分布是未知的，我们建议使用高斯分布的混合作为估计的近似值。使用EM算法使用模型参数估计混合物成分的权重，并将观察到的信息矩阵用于估计拟议估计量的渐近方差。对于带有和不带有随机效应正态性假设的两种最大似然方法，研究了拟议估计量的渐近性质，并通过模拟研究评估了它们的有限样本性质。第三，我们考虑一种惩罚似然法。与最大似然方法相比，该方法的计算强度较小。它将随机效应视为可能性中的固定效应而受到惩罚，并且避免了将可能性与随机效应进行积分的需求。惩罚似然是通过拉普拉斯近似获得的。我们通过模拟研究比较了高斯过程同时模型的惩罚似然法和最大似然估计中使用的EM算法的数值性能。本文利用卡罗来纳州头颈癌研究（CHANCE）的真实数据说明了所有提出的方法。

著录项

  • 作者

    Choi, Jaeun.;

  • 作者单位

    The University of North Carolina at Chapel Hill.;

  • 授予单位 The University of North Carolina at Chapel Hill.;
  • 学科 Biology Biostatistics.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 240 p.
  • 总页数 240
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号