Statistical Methods for the Design and Analysis of Studies with Incomplete Data

不完整数据研究设计和分析的统计方法

• 批准号: RGPIN-2016-04384
• 负责人: McIsaac, Michael
• 金额: $ 1.46万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635793
• 关键词: Statistical; Methods; Design; Analysis; Studies

Missing data is a common problem that can be encountered in any research setting due to unanticipated problems in the implementation of a study. Conclusions drawn from such a study are only valid if the missing data are appropriately handled in the statistical analyses. However, statistical methodology is underdeveloped in settings where there are multiple mechanisms contributing to missingness. If interest lies in a composite score that is constructed from several potentially missing variables, then the missing data mechanism will be complex and model misspecification becomes a real concern. I will develop statistical methodologies and provide practical recommendations for effective analysis strategies in these settings.

数据缺失是任何研究环境中都可能遇到的常见问题，因为在研究实施过程中会出现意想不到的问题。从这种研究中得出的结论只有在统计分析中适当处理缺失数据的情况下才有效。然而，在有多种机制导致遗漏的情况下，统计方法还不够发达。如果感兴趣的是由几个可能缺失的变量构成的合成分数，那么缺失数据的机制将是复杂的，模型错误指定将成为一个真正的问题。我将制定统计方法，并为在这些环境中制定有效的分析战略提出切实可行的建议。