Statistical Methods for Incomplete Data with Measurement Errors

存在测量误差的不完整数据的统计方法

• 批准号: 8252746
• 负责人: Edward C Chao
• 金额: $ 19.86万
• 依托单位: DATA NUMERICA INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8252746
• 关键词: Adverse effects; Algorithms; Case Study; Clinical; Clinical Research; Clinical Trials; Communities; Computer software; Data; Data Analyses; Dropout; Eating; Environment; Evaluation; Event; Health Surveys; Informatics; Internet; Investigation; Joints; Learning; Libraries; Literature; Longitudinal Studies; Measurement; Methods; Modeling; Nutritional Study; Observational Study; Outcome; Phase; Physical activity; Publications; Research; Resources; Running; Solutions; Statistical Data Interpretation; Statistical Methods; Statistical sensitivity; Structure; Study Subject; Surrogate Markers; Time; Validation; Variant; animation; attenuation; base; cost; design; experience; graphical user interface; innovation; interest; method development; prototype; research study; response; simulation; statistics; tool

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.

描述（申请人提供）：缺失数据和测量错误是统计数据分析中常见的问题。我们感兴趣的实验和观察研究中存在缺失数据和测量误差的问题。例子包括健康调查包含无应答者或缺失项目，替代标记数据与测量误差等的应用程序可以是纵向临床试验，多层次的社区研究和健康调查。不完整数据可以是模型中使用的不可验证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组合，即缺失的响应与协变量测量误差。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，用于共享和说明。Longit信息中心是一个在线数据分析环境。订阅者可以访问许多统计软件包和动态图形进行数据分析。在本项目中，最终结果将是Longit中添加的两个统计软件包：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局的联合建模方法。这些软件包包括创新的统计方法，敏感性分析和图形方法。目前还没有商业软件能够像Laso那样处理复杂的情况。 公共卫生关系：该项目旨在开发用于分析具有缺失数据和测量误差的不完整数据的统计方法和工具。