TREE-STRUCTURED METHODS FOR LONGITUDINAL & SURVIVAL DATA

纵向树结构方法

• 批准号: 3468350
• 负责人: MARK R SEGAL
• 金额: $ 9.92万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-01-01 至 1995-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3468350
• 关键词: AIDS; computer data analysis; computer program /software; computer simulation; disease /disorder proneness /risk; epidemiology; health surveys; human data; human mortality; longitudinal human study; lung; mathematical model; prognosis; respiratory disorder epidemiology; respiratory function; statistics /biometry

Longitudinal and survival designs are fundamental to epidemiology [e.g. studies of acquired immune deficiency syndrome (AIDS) and pulmonary function (PF)]. Such designs are the only way to assess changes in health status over time and factors that determine those changes. Many already articulated research questions concern whether prognostic subgroups of patients exist such that patients within the subgroup have similar changes in health status, while patients from differing subgroups have distinct patterns of change. Again, determining what factors characterize the differing subgroups is of central interest. However, the current statistical methodology for addressing such questions is inadequate. This project will develop statistical methodology to perform such subgroup identification by extending tree-structured techniques to longitudinal and survival data settings. Tree-structured methodology has already proved adept at subgroup identification in regression and classification problems. We will apply the techniques to at least seven datasets from AIDS and PF epidemiologic studies. The analyses will focus on resolving existing research hypotheses surrounding each dataset. Additionally, comparisons with standard techniques will be made. The development of methodology will focus on choice and properties of split functions. We will pay particular heed to data analytic issues that arise in epidemiologic studies. These include handling missing values and time- varying covariates. Additionally, problems inherent to HIV seroprevalent cohorts deriving from unknown times of infection will be addressed. The research questions to be studied by this project have also been motivated by many of the issues raised by the 1986 NHLBI workshop on longitudinal data analysis. The results of this research will furnish epidemiologists and statisticians with new analytic tools that will enable investigators to take full advantage of the benefits of longitudinal and survival designs. The ability to directly contend with prognostic group identification will enable them to resolve many outstanding research hypotheses. Tree-structured methods avoid the assumptions of more parametric analyses and therefore also afford a basis for checking such assumptions. Thus the methodologic developments proposed in this project will both expand and enhance the investigators' data analysis options.

纵向和生存设计是流行病学的基础[例如 获得性免疫缺陷综合征(AIDS)与肺脏的研究 函数(PF)]。这样的设计是评估健康变化的唯一方法 随时间变化的状态以及决定这些变化的因素。已经有很多人 明确的研究问题关系到预后亚组是否 患者的存在使得亚组中的患者具有类似的变化 在健康状况方面，而来自不同亚组的患者有不同的 变化的模式。再一次，确定哪些因素表征了 不同的亚群是核心利益所在。然而，目前 解决这类问题的统计方法不足。 该项目将制定统计方法来执行这类分组 通过将树形结构技术扩展到纵向和 生存数据设置。树结构的方法论已经证明 擅长于回归和分类问题中的子组识别。 我们将把这些技术应用于至少七个来自艾滋病和PF的数据集 流行病学研究。分析将集中于解决现有的 围绕每个数据集的研究假设。此外，比较 标准技术将被制造出来。 方法论的发展将侧重于拆分的选择和性质 功能。我们将特别注意出现的数据分析问题 在流行病学研究中。其中包括处理遗漏的值和时间- 不同协变量。此外，艾滋病毒血清流行固有的问题 将解决因感染时间未知而产生的队列。 本项目要研究的研究问题也有 受1986年NHLBI研讨会提出的许多问题的推动 纵向数据分析。这项研究的结果将提供 流行病学家和统计学家拥有新的分析工具，将使 调查人员充分利用纵向和纵向的优势 生存设计。与预见组直接对抗的能力 鉴定将使他们能够解决许多未完成的研究 假设。树结构方法避免了更多的假设 参数分析，因此也提供了检查的基础 假设。因此，本项目中提出的方法论发展 将扩大和加强调查人员的数据分析选择。