Missing Data Methods for Non-Random Attrition in Longitudinal Studies

纵向研究中非随机损耗的缺失数据方法

• 批准号: 9408837
• 负责人: Roderick J.A. Little
• 金额: $ 14.22万
• 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: 1994
• 资助国家: 美国
• 起止时间: 1994-07-01 至 1997-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9408837&HistoricalAwards=false
• 关键词: Missing; Data; Methods; Non; Random

This project will develop new methods for multivariate longitudinal data with nonrandomly missing data such as arise with attrition from the sample. Maximum likelihood and Bayesian inference will be derived for a variety of pattern- mixture models, which stratify the data by the pattern of missing data and identify parameters by exploiting assumptions about the missing-data mechanism. Methods will be developed for general patterns of missing data, normal repeated measures models, multivariate t models that accommodate outlying values, and data involving mixtures of continuous and categorical variables. The methods will be compared with existing methods, including those based on stochastic censoring models. Tests for the type of missing-data mechanism will also be developed. Many important scientific studies involve repeated measures of subjects over time. A common problem in analyzing such studies is that some subjects are missing some of their measurements, because they miss visits or do not stay in the study to the end. Discarding such incomplete cases is wasteful, and can lead to erroneous conclusions when the cases that are completely observed differ systematically from those that are incomplete. This grant studies methods of analyses that include all the data and incorporate a variety of reasons for why values are missing.

本项目将开发具有非随机缺失数据的多变量纵向数据的新方法，如样本磨损产生的数据。将为各种模式混合模型导出最大似然和贝叶斯推理，这些模型通过缺失数据的模式对数据进行分层，并通过利用关于缺失数据机制的假设来识别参数。将为缺失数据的一般模式、正常重复测量模型、容纳离群值的多元t模型以及涉及连续变量和分类变量混合的数据开发方法。将这些方法与现有的方法进行比较，包括基于随机审查模型的方法。还将为丢失数据机制的类型制定测试。许多重要的科学研究都涉及到在一段时间内反复测量受试者。在分析这类研究时，一个常见的问题是，一些研究对象缺少一些测量数据，因为他们错过了访问或没有坚持到研究结束。抛弃这些不完整的案例是一种浪费，当完全观察到的案例与不完整的案例在系统上不同时，可能会导致错误的结论。这项拨款研究的分析方法，包括所有的数据，并纳入各种原因的价值丢失。