TREE-STRUCTURED METHODS FOR LONGITUDINAL & SURVIVAL DATA

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（艾滋病）和肺结核的研究 function（PF）]。 这样的设计是评估健康变化的唯一方法 以及决定这些变化的因素。 许多人已经 明确的研究问题涉及是否预后亚组 存在患者，使得亚组内的患者具有相似的变化 在健康状况方面，来自不同亚组的患者具有不同的 变化的模式。 再次，确定哪些因素表征了 不同的分组是核心利益。 但目前 处理这些问题的统计方法是不够的。 该项目将开发统计方法来执行此类分组 通过将树结构技术扩展到纵向和 生存数据设置。 树结构方法已经证明 擅长回归和分类问题中的子群识别。 我们将把这些技术应用到至少七个来自艾滋病和PF的数据集上 流行病学研究。 分析将侧重于解决现有的 围绕每个数据集的研究假设。 此外，比较 将采用标准技术。 方法论的发展将集中在分裂的选择和性质上 功能协调发展的 我们将特别注意出现的数据分析问题， 在流行病学研究中。 其中包括处理缺失值和时间- 变协变量 此外，HIV血清阳性的固有问题 将讨论来自未知感染时间的队列。 本项目所要研究的研究问题也已 1986年NHLBI研讨会提出的许多问题， 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的新分析工具， 调查人员充分利用纵向和 生存设计 直接与预后组对抗的能力 识别将使他们能够解决许多突出的研究 假设 树结构的方法避免了更多的假设 参数分析，因此也提供了检查这种基础 假设。 因此，本项目中提出的方法学发展 将扩大和加强调查人员的数据分析选择。

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.