Statistical Analysis of Longitudinal Studies and Surveys with Missing Values

- 建议编号：DMS 9803720 PI： 罗德里克小机构： 密歇根大学项目： 纵向研究的统计分析 缺失值调查 摘要： 许多实证研究都存在数据缺失的问题， 缺失值的受试者与完全缺失值的受试者系统性不同 数据丢弃不完整的案例会导致偏倚和损失 效率基于模型的统计方法允许高效和 基于所有数据的有效推断，但需要仔细注意数据的正确建模和缺失数据机制。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，其中研究受试者随着时间的推移被反复测量，并且受试者在某些时间缺失，或过早退出研究;以及来自复杂调查设计的调查数据，其中受试者具有选择和不响应的差异概率。 许多研究的结果很难解释，因为 缺少数据。在一项民意调查中，一些随机选择的对象 拒绝回答一些问题，结果基于 受访者可能不代表人口。临床 一些患者可能会退出研究，因为他们不能 容忍某种特殊待遇，或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，以提供比仅分析完整病例更精确和更准确的答案。

----------------------------------------------------------------------- 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.