Statistical Methods for Incomplete Data with Measurement Errors

描述（由申请人提供）：数据缺失和测量误差是统计数据分析中常见的问题。我们对存在数据缺失和测量误差问题的实验和观察研究感兴趣。例如，包含无应答者或缺失项目的健康调查、具有测量误差的替代标记数据等。应用可以是纵向临床试验、多层次社区研究和健康调查。不完整数据可能是模型中不可忽略的缺失响应或作为预测因子，即缺失响应、缺失协变量和协变量测量误差。最复杂的情况是这些困难的结合，即缺少响应和协变量测量误差。该项目的成果包括创新的统计方法、案例研究、工具、解决方案和出版物。这些资源将被纳入我们的Longit信息学中心，以供分享和说明。Longit信息中心是一个在线数据分析环境。用户可以访问许多统计软件包和动态图形进行数据分析。在这个项目中，最终的结果将是在Longit中增加两个统计包：1)MiMe：缺失数据和测量误差的统计方法；2)Laso：临床事件时间替代标志物研究中纵向和生存结果的联合建模方法。这些软件包包括创新的统计方法、灵敏度分析和图形方法。没有任何商业软件能像Laso那样处理复杂的情况。

DESCRIPTION (provided by applicant): Missing data and measurement errors are common problems in statistical data analysis. We are interested in experimental and observational studies where there exist missing data and measurement errors problems. Examples include health surveys containing non-responders or missing items, surrogate marker data with measurement errors, etc. The applications could be longitudinal clinical trials, multilevel community studies and health surveys. The incomplete data could be the non-ignorable missing response used in a model or as predictors, i.e. missing response, missing covariate, and covariate measurement errors. The most complicated scenario is the combination of such difficulties, i.e. the missing response with covariate measurement errors. The results from this project include innovative statistical methods, case studies, tools, solutions, and publications. These resources will be incorporated in our Longit Informatics Center for sharing and illustration. The Longit Informatics Center is an online data analysis environment. Subscribers can access many statistical packages and dynamic graphics for data analysis. In this project, the ultimate results will be two statistical packages added to Longit: 1) MiMe: statistical methods for missing data and measurement errors, and 2) Laso: joint modeling methods for longitudinal and survival outcomes in the study of surrogate marker for clinical event time. These packages include innovative statistical methods, sensitivity analysis and graphical methods. There is no commercial software to deal with complicated case as Laso. PUBLIC HEALTH RELEVANCE: This project aims to develop statistical methods and tools for analyzing incomplete data with missing data and measurement errors.