Survival Analysis and Related Topics

生存分析及相关主题

• 批准号: 9626750
• 负责人: Zhiliang Ying
• 金额: $ 5.66万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-07-01 至 1999-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9626750&HistoricalAwards=false
• 关键词: Survival; Analysis; Related; Topics

Ying The research develops statistical models, methodologies and related theory to address issues arising from design, modeling and analysis of survival data, which are often subject to censoring, truncation and/or other types of missingness. It introduces new hazard-based regression models for survival data that extend and complement the standard proportional hazards regression, and yet still possess essential good features that the latter has. Besides the usual right-censored data, it also provides solutions to such important and difficult problems as interval censoring, random effects and multivariate survival times by exploiting model variation and developing suitable analytic tools. It involves finding computationally convenient representations and establishing sharp theoretical properties for the analysis of discrete (survival) data, particularly of those that can be summarized by two-by-two or more general contingency tables. New methods and theory tailored to retrospective studies, which are often summarized as the case-control and case-cohort regression problems, are also developed. Survival data are collected in many scientific investigations, including biomedical follow-up studies, industrial life tests, study of economic durations, astronomy, demography among others. Development of suitable statistical methods is crucial to handling of such data so that valid scientific conclusions can be derived. This project develops several novel approaches, some of which are suitable for identification of health related risk factors, such as exposure to hazardous environment, consumption of certain foods etc., and others are applicable to evaluation of therapeutic effects as well cost effectiveness of medical treatments. These new methods may impact other areas of public and scientific interests, an example of which is the study of factors influencing unemployment duration.

这项研究开发了统计模型、方法和相关理论，以解决因生存数据的设计、建模和分析而产生的问题，这些数据经常受到审查、截断和/或其他类型的遗漏的影响。它为生存数据引入了新的基于风险的回归模型，扩展和补充了标准比例风险回归，但仍具有后者所具有的基本良好特征。除了通常的右删失数据外，它还通过利用模型变量和开发合适的分析工具来解决区间截尾、随机效应和多变量生存时间等重点和难点问题。它涉及到为离散(生存)数据的分析寻找计算上方便的表示法和建立清晰的理论性质，特别是那些可以用两个或更多的一般列联表来总结的数据。为回顾研究量身定做的新方法和新理论也被总结为病例对照和病例队列回归问题。生存数据是在许多科学调查中收集的，包括生物医学跟踪研究、工业寿命测试、经济持续时间研究、天文学、人口学等。发展适当的统计方法对于处理这类数据至关重要，这样才能得出有效的科学结论。该项目开发了几种新的方法，其中一些适用于识别与健康有关的风险因素，如暴露在危险环境中、食用某些食物等，另一些适用于评估治疗效果和医疗成本效益。这些新方法可能会影响其他公共和科学利益领域，其中一个例子是对影响失业持续时间的因素的研究。