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LOF of logistic GEE models and cost efficient Bayesian optimal designs for nonlinear combinations of parameters in nonlinear regression models.

机译：Logistic GEE模型的LOF和具有成本效益的贝叶斯优化设计，用于非线性回归模型中参数的非线性组合。

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摘要

When the primary research interest is in the marginal dependence between the response and the covariates, logistic GEE (Generalized Estimating Equation) models are often used to analyze clustered binary data. Relative to ordinary logistic regression, very little work has been done to assess the lack of fit of a logistic GEE model. A new method addressing the LOF of a logistic GEE model was proposed. Simulation results indicate the proposed method performs better than or as well as other currently available LOF methods for logistic GEE models. A SAS macro was developed to implement the proposed method.;Nonlinear regression models are widely used in medical science. Before the models can be fit and parameters interpreted, researchers need to decide which design points in a prespecified design space should be included in the experiment. Careful choices at this stage will lead to efficient usage of limited resources. We proposed a cost efficient Bayesian optimal design method for nonlinear combinations of parameters in a nonlinear model with quantitative predictors. An R package was developed to implement the proposed method.
机译：当主要研究兴趣是响应和协变量之间的边际依赖性时，常使用logistic GEE（广义估计方程）模型来分析聚类的二进制数据。相对于普通的logistic回归，很少有工作可以评估logistic GEE模型的不匹配性。提出了一种解决后勤GEE模型LOF的新方法。仿真结果表明，对于逻辑GEE模型，该方法的性能优于或优于其他当前可用的LOF方法。开发了一个SAS宏来实现该方法。非线性回归模型在医学中被广泛使用。在对模型进行拟合和解释参数之前，研究人员需要确定实验中应包括预定设计空间中的哪些设计点。在此阶段的谨慎选择将导致有限资源的有效利用。我们为具有定量预测变量的非线性模型中的参数非线性组合提出了一种经济高效的贝叶斯优化设计方法。开发了一个R包来实现所提出的方法。

著录项

  • 作者

    Tang, Zhongwen.;

  • 作者单位

    Kansas State University.;

  • 授予单位 Kansas State University.;
  • 学科 Biology Biostatistics.;Statistics.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 169 p.
  • 总页数 169
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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