Model Building--Marginal Regression with Dependent Data

模型构建--相关数据的边际回归

• 批准号: 6422998
• 负责人: Wei Pan
• 金额: $ 10.73万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2004-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6422998
• 关键词: computer data analysis; computer graphics /printing; computer program /software; computer simulation; computer system design /evaluation; human data; mathematical model; model design /development; statistics /biometry

Dependent response data are common in biomedical studies. One typical example is longitudinal data. Subsequent to the seminal work by Liang and Zeger (1986), marginal regression and its associated generalized estimating equations (GEE) method have become increasingly important in analyzing such data. However, model building, including model checking and model selection, have been relatively neglected for GEE, although there is a large literature in model building for independent data. Since any scientific conclusions drawn from statistical analysis crucially depend on the statistical model being used, and there is always some uncertainty with regard to the correct model due to limited prior knowledge, the importance and necessity of model building are apparent. The subject of this proposed research is model building techniques in marginal regression for dependent data. Specifically, first, formal goodness-of-fit tests are to be investigated. Second, I propose graphical model checking using marginal model plots and the generalized additive model plots. Third, I investigate how to adjust statistical inference with small samples since the commonly used large sample results may not be applicable. The above model building techniques will be evaluated by simulation and using real data. All the techniques will be implemented in the commonly used statistical language S-Plus and made freely available to practitioners.

依赖性反应数据在生物医学研究中很常见。 一个典型的例子是纵向数据。 在Liang 和Zeger (1986) 的开创性工作之后，边际回归及其相关的广义估计方程(GEE) 方法在分析此类数据中变得越来越重要。 然而，尽管有大量关于独立数据模型构建的文献，但 GEE 相对忽视了模型构建，包括模型检查和模型选择。 由于从统计分析中得出的任何科学结论都很大程度上取决于所使用的统计模型，并且由于先验知识的有限，正确的模型总是存在一些不确定性，因此模型构建的重要性和必要性是显而易见的。 这项研究的主题是相关数据边际回归的模型构建技术。 具体来说，首先要研究正式的拟合优度检验。 其次，我建议使用边际模型图和广义加性模型图进行图形模型检查。 第三，我研究如何调整小样本的统计推断，因为常用的大样本结果可能不适用。 上述模型构建技术将通过模拟和使用真实数据进行评估。 所有技术都将以常用的统计语言 S-Plus 实现，并免费提供给从业者。