Analytic Methods for Heterogeneous Multilevel Data

描述（由申请人提供）：多层次数据在社会学、行为学和生物医学研究中非常常见。这些数据可能来自纵向社区调查、遗传家族研究或时空研究，以调查某些健康结果。通常，兴趣集中在某些治疗干预的影响上。当存在多层数据结构时，这样的数据可能非常复杂。这些数据可能包含社区、家庭、患者和长期重复测量等因素，它们相互嵌套或交叉。对于连续响应，人们研究并应用了线性混合效应模型或潜变量模型等层次模型。在分析中，主要关注的是研究特定病因途径对健康结果的影响。由于每组的记录往往是相关的，研究者必须调整一组观察内或组间的异质性。在这类数据中，过色散也很常见。本项目的主要兴趣是研究上述性质的连续和离散结果的分析方法。在这一领域，人们通常对非连续数据采用广义线性混合效应模型GLMM、边际模型或过渡模型。GLMM等模型的难点在于估计方法往往难以达到无偏性、一致性和高效性。我们感兴趣的是开发更健壮的方法来实现这些目标的连续和离散多层次数据任意维。最终的结果是一个具有灵活的多层次建模方法的软件库，用于分析复杂的多层次数据。该软件将对从事复杂数据结构的社会学、行为学和生物医学研究的生物医学研究人员有用。将开发手稿和课程包，以协助从业者将适当的方法和软件工具应用于他们的研究。

DESCRIPTION (provided by applicant): Multilevel data are very common in sociological, behavioral and biomedical researches. The data could come from longitudinal community surveys, genetic family studies or spatial-temporal studies to investigate some health outcomes. Typically, the interest focuses on the impact of some treatment intervention. Such data could be very complex when there are multiple levels of data structures. The data might have factors such as community, family, patient and repeated measures over time nested or crossed in each other. For continuous response, hierarchical models such as linear mixed-effects models or latent variable models have been studied and applied. In the analysis, the major interest is to study the impact of specific cause pathway on health outcome. Since the records in each cluster are often correlated, investigator has to adjust the heterogeneity within a cluster of observations or between clusters. Overdispersion is also very common in such data. The major interest of this project is to investigate the analytic methods for continuous and discrete outcomes of the above nature. In this area, typically, people apply generalized linear mixed-effects models GLMM, marginal models or transition models to non-continuous data. The difficulties for such models such as GLMM is that estimation methods often have troubles to achieve unbiasness, consistency and efficiency. We are interested in the development of more robust methods to achieve these goals for continuous and discrete multilevel data with arbitrary dimension. The final result is a software library with flexible multilevel modeling approaches for the analysis of complex multilevel data. The software will be useful to biomedical researchers working on sociological, behavioral and biomedical studies with complex data structures. Manuscripts and course packs will be developed to assist practitioners in applying appropriate methods and the software tool to their studies.