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首页> 外文学位 >Practical issues arising in clustered data: Modified GEE and goodness-of-fit test, overdispersion, and changepoint models.
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Practical issues arising in clustered data: Modified GEE and goodness-of-fit test, overdispersion, and changepoint models.

机译：聚集数据中出现的实际问题：修改后的GEE和拟合优度测试，过度分散和变更点模型。

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摘要

This thesis consists of three articles dealing with methods for analysing clustered data. In the first article a modified generalized estimating equation (GEE) and a goodness-of-fit test for correlated binary responses are developed. The estimating equation is obtained by using a multinomial covariance structure as a working covariance matrix. We here propose a Goodness-of-Marginal-Fit (GOMF) test statistic based on the modified GEEs for the correlated binary data from contingency tables. It is shown that the asymptotic distribution of the GOMF test statistics under the null is a chi-square distribution. The power of the GOMF test is assessed by simulation studies. We illustrate the modified GEEs and the proposed GOMF tests with two examples.; The second article deals with testing for overdispersion in small samples. In toxicological experiments, the number of experimental units is often small. Exact methods for testing and estimation of treatment effects in logistic regression models are available. However, the validity of these methods relies on the binomial assumption. Animal litter effects in toxicological experiments naturally lead to overdispersion, making the binomial assumption untenable. Several tests of that assumption are available. However, those tests rely on large-sample theory, which may not provide a good approximation in smaller samples. An exact test of the binomial assumption is developed in the paper; we develop a conditional likelihood approach for the testing of overdispersion effect based on the multiplicative binomial model. Several examples are used to illustrate our approach.; The third article deals with the analysis of correlated biomarker data in relation to recurrence or progression of disease. An example is the number of CD4 cells that is an important biomarker of disease progression to AIDS in persons infected with HIV. Another example is the use of prostate specific antigen to monitor recurrence of prostate cancer after surgery or radiotherapy treatment. A parametric model that contains a random changepoint in the expected biomarker values is considered. We propose a latent changepoint model (LCM), which posits a latent disease process with random changepoints and an observable surrogate biomarker process. In this paper, estimation through generalized estimating equations is proposed for the LCM. The procedure allows estimation of the biomarker trend over time and the changepoint distribution. The small-sample performance of the estimates for the LCM are investigated using a Monte Carlo simulation study.
机译：本文由三篇文章组成，涉及分析聚类数据的方法。在第一篇文章中，开发了改进的广义估计方程（GEE）和相关二进制响应的拟合优度检验。通过使用多项式协方差结构作为工作协方差矩阵来获得估算方程。我们在此提出基于修正的GEE的偶然性拟合优度（GOMF）测试统计数据，用于列联表中的相关二进制数据。结果表明，空值下GOMF检验统计量的渐近分布为卡方分布。 GOMF测试的能力通过仿真研究进行评估。我们用两个例子说明修改后的GEE和建议的GOMF测试。第二篇文章介绍了小样本中过度分散的测试。在毒理学实验中，实验单位的数量通常很少。有可用的逻辑回归模型中用于测试和评估治疗效果的确切方法。但是，这些方法的有效性取决于二项式假设。毒理学实验中的动物垫料自然会导致过度分散，从而使二项式假设难以成立。有几种关于该假设的检验方法。但是，这些测试依赖于大样本理论，在小样本中可能无法提供良好的近似值。本文提出了对二项式假设的精确检验。我们基于乘法二项式模型开发了条件似然方法来测试过度分散效应。用几个例子来说明我们的方法。第三篇文章涉及与疾病复发或进展有关的相关生物标记数据的分析。一个例子是CD4细胞的数量，它是感染HIV的人疾病发展为AIDS的重要生物标志。另一个例子是在手术或放疗治疗后使用前列腺特异性抗原来监测前列腺癌的复发。考虑在期望的生物标志物值中包含随机变化点的参数模型。我们提出了一个潜在的变化点模型（LCM），它提出了一个具有随机变化点和可观察的替代生物标志物过程的潜在疾病过程。本文提出了通过广义估计方程的估计。该程序允许估计生物标志物随时间的变化趋势和变化点分布。使用蒙特卡洛模拟研究调查了LCM估计值的小样本性能。

著录项

  • 作者

    Lee, Ji-Hyun.;

  • 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 Dr.P.H.
  • 年度 2003
  • 页码 86 p.
  • 总页数 86
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;
  • 关键词

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