Analytic Methods for Heterogeneous Multilevel Data

异构多级数据的分析方法

• 批准号: 7433839
• 负责人: Edward C Chao
• 金额: $ 36.44万
• 依托单位: DATA NUMERICA INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2010-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7433839
• 关键词: Affect; Algorithms; Area; Awareness; Behavior; Behavioral; Biological; Biomedical Research; Categories; Characteristics; Clinical Trials; Communities; Community Surveys; Complex; Computational Technique; Computer software; Count; Data; Dependence; Development; Dimensions; Disease; Educational process of instructing; Effectiveness; Equation; Error Sources; Evaluation; Family; Family Study; Feasibility Studies; Genetic; Geographic Locations; Goals; Health; Health Promotion; Heterogeneity; Individual; Instruction; Intervention; Java; Learning; Libraries; Link; Longitudinal Studies; Manuals; Manuscripts; Maps; Measures; Methodology; Methods; Modeling; Monitor; Nature; Numbers; Outcome; Pathway interactions; Patients; Phase; Population; Procedures; Race; Records; Research; Research Personnel; Risk-Taking; Running; Schedule; Scientist; Small Business Funding Mechanisms; Small Business Innovation Research Grant; Social Environment; Social Sciences; Software Design; Software Tools; Structure; Surveys; Techniques; Time; Variant; Work; data structure; interest; multilevel analysis; novel; prototype; psychologic; response; simulation; social; trend; user friendly software; user-friendly

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.

描述(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。这些数据可以来自纵向社区调查、遗传家庭研究或时空研究，以调查一些健康结果。通常，人们的兴趣集中在一些治疗干预的影响上。当存在多个级别的数据结构时，这样的数据可能非常复杂。这些数据可能有社区、家庭、患者和随着时间的推移而重复测量的因素相互嵌套或交叉。对于连续响应，已有线性混合效应模型或潜变量模型等递阶模型被研究和应用。在分析中，主要的兴趣是研究特定原因路径对健康结果的影响。由于每个簇中的记录通常是相关的，研究人员必须调整观察簇内或簇间的异质性。在这类数据中，过度分散也是非常常见的。这个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，人们通常将广义线性混合效应模型GLMM、边际模型或过渡模型应用于非连续数据。像GLMM这样的模型的困难在于，估计方法往往难以实现无偏、一致性和效率。我们对开发更稳健的方法来实现这些目标感兴趣，这些方法适用于任意维度的连续和离散多水平数据。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。该软件将对从事社会学、行为和生物医学研究的生物医学研究人员有用，这些研究具有复杂的数据结构。将编写手稿和课程包，以协助实践者将适当的方法和软件工具应用于他们的研究。