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: 7771937
• 负责人: Edward C Chao
• 金额: $ 37.17万
• 依托单位: DATA NUMERICA INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-16 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7771937
• 关键词: Algorithms; Area; Awareness; Binomial Model; Clinical Trials; Complex; Computer software; Data; Development; Diagnosis; Dropout; Educational process of instructing; Environment; Equation; Evaluation; Feasibility Studies; Imagery; Influentials; Instruction; Internet; Link; Logistic Regressions; Manuals; Methodology; Methods; Modeling; Outcome; Phase; Reporting; Research; Research Personnel; Running; Schedule; Small Business Innovation Research Grant; Software Tools; Technology; Work; animation; data modeling; design; interest; novel; prototype; public health relevance; 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.

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。在一段时间内反复测量响应，可以是连续的，也可以是离散的。通常，兴趣集中在一些治疗干预的影响或随时间变化的反应模式。当存在多层数据结构时，这样的数据可能非常复杂。此外，数据中还经常存在响应缺失的情况。在纵向数据的分析中，为了得出有效的结果，必须纳入缺失的数据机制。在最严重的情况下，缺失的机制是不可忽视的，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，必须依靠灵敏度分析和图形方法来研究假设的鲁棒性。我们感兴趣的是开发软件，结合分析方法，灵敏度分析和图形方法在一个软件。市场上还没有这样的软件。我们将开发一个用户友好的网络和桌面应用系统。我们还将开发算法和动态图形方法来分析辍学数据和诊断建模假设。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，以协助从业者在他们的研究中应用适当的方法和工具。