Statistical Analysis of Longitudinal Studies and Surveys with Missing Values

纵向研究和缺失值调查的统计分析

• 批准号: 9803720
• 负责人: Roderick J.A. Little
• 金额: $ 18.71万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-15 至 2001-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9803720&HistoricalAwards=false
• 关键词: Statistical; Analysis; Longitudinal; Studies; Surveys

----------------------------------------------------------------------- Proposal Number: DMS 9803720 PI: Roderick Little Institution: University of Michigan Project: Statisitcal Analysis of Longitudinal Studies and Surveys with Missing Values Abstract: Many empirical studies have problems with missing data, where subjects with missing values differ systematically from subjects with complete data. Discarding the incomplete cases leads to bias and loss of efficiency. Model-based statistical methods allow for efficient and valid inferences based on all the data, but require careful attention to correct modeling of the data and the missing data mechanism. This research will develop new Bayesian and maximum likelihood methods for handling incomplete data in the following problems: Longitudinal data where study subjects are measured repeatedly over time, and subjects are missing at certain times, or drop out of the study prematurely; and survey data from complex survey designs where subjects have differential probabilities of selection and nonresponse. Results from many research studies are hard to interpret because of missing data. In an opinion poll some of the randomly chosen subjects refuse to answer some of the questions, and results based on the respondents may not be representative of the population. In clinical trials some patients may drop out of the study because they cannot tolerate a particular treatment, or simply move to a different location and cannot be traced. Statistical methods for analyzing data with missing values use partial information on nonrespondents to provide more precise and more accurate answers than can be obtained by analyzing only the complete cases.

-----------------------------------------------------------------------提案编号：DMS 9803720 PI: Roderick Little机构：密歇根大学项目：缺失值的纵向研究和调查的统计分析摘要：许多实证研究存在缺失数据的问题，缺失值的研究对象与数据完整的研究对象存在系统性差异。丢弃不完整的案例会导致偏见和效率的损失。基于模型的统计方法允许基于所有数据进行有效和有效的推断，但需要仔细注意正确的数据建模和缺失的数据机制。本研究将开发新的贝叶斯和最大似然方法来处理以下问题中的不完整数据：纵向数据，即研究对象在一段时间内反复测量，并且在某些时间缺失或过早退出研究；以及来自复杂调查设计的调查数据，其中受试者有不同的选择和不回应的概率。由于缺少数据，许多研究的结果难以解释。在民意调查中，一些随机选择的被调查者拒绝回答某些问题，基于被调查者的结果可能不能代表总体。在临床试验中，一些患者可能会退出研究，因为他们不能忍受某种特定的治疗，或者只是搬到另一个地方，无法追踪。分析缺失值数据的统计方法使用非受访者的部分信息来提供比仅分析完整案例更精确和准确的答案。