Analytic, Sensitivity and Graphical Methods for Investigating Dropout Data

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，它可以是连续的也可以是离散的。通常，人们的兴趣集中在某些治疗干预的影响或随时间变化的反应模式。当存在多个级别的数据结构时，此类数据可能非常复杂。此外，数据中经常存在缺失响应的情况。在纵向数据分析中，必须纳入缺失数据机制才能得出有效结果。在最严重的情况下，缺失的机制是不可忽略的，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，必须依靠敏感性分析和图解方法来研究假设的稳健性。我们有兴趣开发一种将分析方法、敏感性分析和图形方法整合到一个软件中的软件。市场上还没有这样的软件。我们将开发一个带有网络和桌面应用程序的用户友好系统。我们还将开发用于分析丢失数据和诊断建模假设的算法和动态图形方法。该软件对于从事复杂数据结构的社会学、行为和生物医学研究的生物医学研究人员来说非常有用。将开发手稿和课程包，以帮助从业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略的缺失响应的复杂纵向数据。这些方法和软件将有助于生物医学研究，例如。纵向临床试验。我们将开发用于模型拟合、模型诊断和假设论证的算法、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.