Statistical methods for chronic disease research

慢性病研究的统计方法

• 批准号: 6621497
• 负责人: YIJIAN HUANG
• 金额: $ 12.33万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2005-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6621497
• 关键词: AIDS; chronic disease /disorder; computer simulation; data collection methodology /evaluation; extended care; health care cost /financing; human data; neoplasm /cancer; statistics /biometry

DESCRIPTION (provided by applicant): The broad objective of this research is to develop new statistical methods for several important and timely problems that arise in cancer and HIV/AIDS clinical research. Major efforts will be directed toward (1) lifetime medical cost analysis with incomplete follow-up data and (2) covariate measurement error in logistic and Cox regressions. Current development in these two areas is inadequate and substantial gaps of knowledge exist. In medical research cost evaluation is becoming an important component and has been integrated in many studies, as our health care system is increasingly constrained with limited resources. However, statistical methods for lifetime medical cost analysis with incomplete follow-up data have largely been lacking. This work will focus on developing semipararametric tests and regressions, which accommodate right-censored data. The second area of research concerns the situation that regression covariates are not accurately ascertainable, e.g. dietary intakes in cancer prevention studies or CD4 lymphocyte count and viral load in HIV/AIDS research. Parametric- and nonparametric-correction methods will be developed for widely-applied logistic and Cox regressions with various scenarios of available data. Despite the challenges of these long-standing problems, preliminary investigations have shown considerable promise for elegant and practical solutions. Large-sample properties of the proposed test statistics and estimators will be rigorously investigated by making use of marked point process theory, martingale theory, and modern empirical process theory. Extensive simulation studies will be performed to validate these proposals under practical sample sizes. The proposed methods will be applied to a number of cancer and HIV/AIDS clinical trials. User-friendly computer programs will be developed and made available to the research community.

描述（由申请人提供）：本研究的广泛目标是 为几个重要而及时的问题开发新的统计方法， 出现在癌症和艾滋病毒/艾滋病临床研究中。主要工作将集中在 （1）终身医疗成本分析，随访数据不完整， (2)logistic和考克斯回归的协变量测量误差。电流 在这两个领域的发展是不够的，知识的差距很大 存在.在医学科研中，成本评估已成为一个重要的组成部分 并已被纳入许多研究，因为我们的医疗保健系统是 资源越来越有限。然而，统计方法 对于终身医疗成本分析，不完整的随访数据在很大程度上 一直缺乏。这项工作将侧重于开发半参数测试， 回归，它适应右删失数据。第二个研究领域 关于回归协变量不准确的情况 可确定的，例如癌症预防研究中的饮食摄入量或CD 4 淋巴细胞计数和病毒载量。参数-和 非参数校正方法将被开发用于广泛应用的逻辑 和考克斯回归与各种情况下的可用数据。尽管 这些长期存在的问题的挑战，初步调查 显示出相当大的前景优雅和实用的解决方案。大样本 所提出的检验统计量和估计量的性质将严格 本文利用标记点过程理论、鞅论、 现代经验过程理论。广泛的模拟研究将 在实际样本量下验证这些建议。的 提出的方法将应用于一些癌症和艾滋病毒/艾滋病的临床 审判将开发用户友好的计算机程序并提供给 研究界。