Analytic, Sensitivity and Graphical Methods for Investigating Dropout Data

调查辍学数据的分析法、灵敏度法和图形法

• 批准号: 7539999
• 负责人: Edward C Chao
• 金额: $ 11.31万
• 依托单位: DATA NUMERICA INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-18 至 2009-01-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7539999
• 关键词: Algorithms; Area; Awareness; Behavioral; Binomial Model; Biomedical Research; Clinical Trials; Community Surveys; Complex; Computer software; Count; Data; Development; Diagnosis; Dropout; Educational process of instructing; Environment; Equation; Evaluation; Family; Family Study; Feasibility Studies; Hand; Health; Imagery; Influentials; Instruction; Internet; Intervention; Linear Models; Link; Logistic Regressions; Manuals; Manuscripts; Marketing; Measures; Methodology; Methods; Modeling; Outcome; Pattern; Phase; Public Health; Reporting; Research; Research Personnel; Running; Schedule; Small Business Funding Mechanisms; Small Business Innovation Research Grant; Software Tools; System; Technology; Time; Work; animation; data modeling; data structure; design; interest; novel; prototype; response; simulation; software development; tool; user-friendly

DESCRIPTION (provided by applicant): Longitudinal data are very common in sociological, behavioral and biomedical researches. The data may come from longitudinal clinical trials, community surveys, family studies or spatial-temporal studies to investigate some health outcomes. The responses are measured repeatedly over a period of time, and it could be either continuous or discrete. Typically, the interest focuses on the impact of some treatment intervention or the pattern of change in response over time. Such data could be very complex when there are multiple levels of data structures. In addition, it is often the case that there exists missing response in the data. In the analysis of longitudinal data, the missing data mechanisms have to be incorporated in order to derive valid results. In the most severe case, the missing mechanism is not ignorable, i.e. one has to model simultaneously the observed and unobserved outcome variables and the missing indicator. On the other hand, those modeling assumptions are often not testable, and one has to rely on the sensitivity analysis and graphical methods to study the robustness of the assumptions. We are interested in developing software that incorporates the analytic methods, sensitivity analysis and graphical methods in one software. Such software is not available in the market yet. We will develop a user-friendly system with web and desktop applications. We will also develop algorithms and dynamic graphical methods for the analysis of dropout data and the diagnosis of modeling assumptions. 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 tools in their studies. PUBLIC HEALTH RELEVANCE This project aims at statistical software for the analysis of complex longitudinal data with non-ignorable missing responses. The methods and software will be useful for biomedical studies, e.g. longitudinal clinical trials. We will develop algorithms, analytic methods and dynamic graphical tools for model fitting, model diagnosis and justification of assumptions.

描述（由申请人提供）：纵向数据在社会学、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临床试验、社区调查、家庭研究或时空研究，以调查某些健康结果。在一段时间内重复测量响应，它可以是连续的或离散的。通常，关注点集中在一些治疗干预的影响或随时间变化的反应模式。当存在多级数据结构时，这样的数据可能非常复杂。此外，数据中经常存在缺失响应。在纵向数据的分析中，缺失数据机制必须被纳入，以获得有效的结果。在最严重的情况下，缺失的机制是不可解释的，即必须同时对观察到的和未观察到的结果变量以及缺失的指标进行建模。另一方面，这些建模假设往往是不可检验的，人们不得不依赖于敏感性分析和图形方法来研究假设的鲁棒性。我们有兴趣开发软件，将分析方法，灵敏度分析和图形方法在一个软件中。目前市场上还没有这样的软件。我们将开发一个用户友好的系统与网络和桌面应用程序。我们还将开发用于分析辍学数据和诊断建模假设的算法和动态图形方法。该软件将是有用的生物医学研究人员工作的社会学，行为和生物医学研究与复杂的数据结构。将编制手册和成套课程，以协助从业人员在研究中运用适当的方法和工具。公共卫生相关性本项目的目的是统计软件，用于分析复杂的纵向数据与不可重复的缺失的答复。该方法和软件将用于生物医学研究，例如纵向临床试验。我们将开发算法，分析方法和动态图形工具，用于模型拟合，模型诊断和假设的合理性。