MISSING DATA, MEASUREMENT ERROR AND APPLICATIONS

数据缺失、测量错误和应用

• 批准号: 6475608
• 负责人: CHING-YUN WANG
• 金额: $ 12.21万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-12-15 至 2004-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6475608
• 关键词: cancer risk; data collection methodology /evaluation; dietary calcium; dietary supplements; female; hormone therapy; human data; method development; neoplasm /cancer epidemiology; nutrition related tag; postmenopause; statistics /biometry; vitamin D; vitamin therapy; women's health

DESCRIPTION: (Adapted from Investigator's Abstract) Missing or mismeasured regression variables are frequently encountered in the analysis of cancer research data. This problem is a major issue in dietary research, for example. The broad goal of this research is to develop statistical methods for data analysis in the presence of missing or mismeasured regression variables, and to apply these methods to epidemiologic and clinical studies. Specific areas of research include the following: 1) development of a nonparametric regression calibration method for Cox regression when validation data are available; 2) development of methods for Cox regression with reliability data; 3) development of methods for logistic regression analysis using conditional exposure mean for missing or mismeasured exposures; and 4) development of methods for characterizing associations between multiple variables. Asymptotic theory will be developed and simulation studies will be conducted. The methods will be applied to data from the Women's Health Initiative, a large disease prevention trial and observational study involving 164,500 women with the objective of evaluating the benefits and risks of dietary modification, hormone replacement therapy and supplementation with calcium and vitamin D on the overall health of postmenopausal women. The investigator states that the proposed methods will also accommodate two-stage designs which are becoming popular in epidemiologic research and particular in genetic epidemiology.

描述：（改编自研究者摘要）缺失或测量错误 回归变量在癌症分析中经常遇到 研究数据。 这个问题是饮食研究中的一个重大问题，因为 例子。 这项研究的总体目标是开发统计方法 用于在存在缺失或误测回归的情况下进行数据分析 变量，并将这些方法应用于流行病学和临床研究。 具体研究领域包括： 1) 开发 Cox 回归的非参数回归校准方法 验证数据可用； 2) Cox回归方法的开发 具有可靠性数据； 3）逻辑回归方法的开发 使用条件暴露均值对缺失或误测进行分析 曝光； 4) 开发表征关联的方法 多个变量之间。 渐近理论将得到发展并 将进行模拟研究。 这些方法将应用于数据 来自妇女健康倡议、一项大型疾病预防试验和 一项涉及 164,500 名女性的观察性研究，旨在评估 饮食调整、激素替代疗法的益处和风险 补充钙和维生素 D 对整体健康的影响 绝经后妇女。 研究人员表示，所提出的方法 还将适应在流行的两阶段设计 流行病学研究，特别是遗传流行病学。