Novel nonparametric methods for prognosis studies with missing covariates

用于缺失协变量的预后研究的新型非参数方法

• 批准号: 1106816
• 负责人: Xiao Song
• 金额: $ 15万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1106816&HistoricalAwards=false
• 关键词: Novel; nonparametric; methods; prognosis; studies

Survival data with missing time-independent or time-dependent covariates are commonly encountered in prognosis studies. Existing approaches mostly focus on the standard proportional hazards model, which may be too restricted in some applications. To obtain a comprehensive understanding of data, it is also important to identify variables that are associated with survival time. In this study, the investigators will develop nonparametric approaches for more flexible models with missing covariates, and investigate variable selection in this complicated setup.The proposed research is expected to have broad impacts and application in biomedical studies, econometrics, environmental studies, behavioral and social sciences, where information is collected on time to an event of interest and multiple covariates. This proposed study will foster collaborations among investigators from different institutions/departments and backgrounds. It will promote teaching, training and learning in the Statistics Department and the newly founded Epidemiology and Biostatistics Department at the University of Georgia. Research conducted in this study will help develop advanced graduate courses in survival analysis, missing data and variable selection. It will create challenging statistical projects for graduate students that the investigators are supervising. Research results from this proposal will be disseminated through presentation at major statistical meetings. Software developed will be made publicly available, so that the proposed methods can be readily used in practice in various fields.

在预后研究中，经常会遇到缺少时间无关或时间相关协变量的生存数据。现有的方法大多集中在标准比例风险模型上，该模型在某些应用中可能会受到太多的限制。为了全面了解数据，确定与生存时间相关的变量也很重要。在这项研究中，研究人员将为更灵活的带有缺失协变量的模型开发非参数方法，并研究这一复杂环境下的变量选择。所提出的研究有望在生物医学、计量经济学、环境研究、行为和社会科学中产生广泛的影响和应用，这些研究将在感兴趣的事件和多个协变量的及时收集信息。这项拟议的研究将促进来自不同机构/部门和背景的调查人员之间的合作。它将促进统计系和佐治亚大学新成立的流行病学和生物统计学系的教学、培训和学习。这项研究将有助于开发生存分析、缺失数据和变量选择方面的高级研究生课程。它将为调查人员正在监督的研究生创造具有挑战性的统计项目。这项提案的研究结果将通过在主要统计会议上的介绍来传播。所开发的软件将公之于众，以便建议的方法可以在各个领域的实践中随时使用。