Analytic, Sensitivity and Graphical Methods for Investigating Dropout Data

描述(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，以调查一些健康结果。这些反应是在一段时间内重复测量的，可以是连续的，也可以是离散的。通常，人们的兴趣集中在一些治疗干预的影响或随着时间的推移反应模式的变化上。当存在多个级别的数据结构时，这样的数据可能非常复杂。此外，经常会出现数据中存在缺失响应的情况。在纵向数据分析中，为了得到有效的结果，必须结合缺失数据机制。在最严重的情况下，缺失机制是不可忽视的，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，人们不得不依靠灵敏度分析和图形方法来研究假设的稳健性。我们感兴趣的是开发软件，将分析方法、灵敏度分析和图形方法结合在一个软件中。这样的软件目前还没有上市。我们将开发一个使用Web和桌面应用程序的用户友好系统。我们还将开发算法和动态图形方法，用于分析辍学数据和诊断建模假设。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，以帮助实践者在研究中应用适当的方法和工具。公共卫生相关性该项目旨在开发统计软件，用于分析具有不可忽视的遗漏反应的复杂纵向数据。这些方法和软件将对生物医学研究有用，例如纵向临床试验。我们将开发用于模型拟合、模型诊断和假设证明的算法、分析方法和动态图形工具。

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.