EFFICIENT STATISTICAL ALGORITHMS FOR DROPOUT DATA

数据缺失和丢失是纵向研究中的常见特征。在许多情况下，辍学过程与结果过程有关。这种情况给分析此类数据带来了极大的困难。目前还没有商业软件在处理非随机辍学时考虑辍学机制。因此，结果是有偏见和误导性的。我们研究的最终目的是开发S+Dropout：一个处理各种辍学机制的软件包。拟议的研究将同时考虑辍学和应对过程。我们将开发基于模型的方法和分层结构来测试辍学机制。将开发有效的EM算法和Gibbs抽样来拟合各种模型。由于这些算法严重依赖于未收集数据的建模假设，因此这些假设的有效性必须在数据分析中进行验证。为了进行这项调查，我们提供了一套用于敏感性分析的分析和图形套件。S+辍学模块将作为S+语言的模块实现。还将利用涉及辍学数据的实际问题编写一本全面的案例研究指南。拟议的商业应用：S+辍学将成为S+软件系统的一个模块。此模块将对现有的S+用户群以及更广泛的生物医学研究人员和数据分析师社区具有吸引力。这项研究还将导致开发短期课程、书籍和其他教育材料。

Missing and dropout data are common features in longitudinal studies. In many cases, the dropout process is related to the outcome process. This situation creates tremendous difficulties in analyzing such data. No commercial software currently considers the dropout mechanisms in dealing with non-random dropout. Consequently, the results are biased and misleading. The ultimate objective of our research is the development of S+DROPOUT: a software package for handling various dropout mechanisms. The proposed research will simultaneously consider the dropout and the response processes. We will develop model-based approaches and hierarchical structures for testing the dropout mechanisms. Efficient EM algorithms and Gibbs sampling will be developed for fitting various models. Since these algorithms are relying heavily on the modeling assumptions of the uncollected data, the validity of the assumptions has to be verified in data analysis. To perform this investigation, we provide an analytic and graphic suite for sensitivity analysis. The S+ DROPOUT module will be implemented as a module in the S-Plus language. A comprehensive case study guidebook will also be developed using real problems involving dropout data. PROPOSED COMMERCIAL APPLICATION: S+DROPOUT will be a module in the S-Plus software system. This module will be attractive both to the existing S-Plus user base, as well as the broader community of biomedical researchers and data analysts. This research will also lead to the development of short courses, books, and other educational materials.