Analyzing Multiple Process Data in Studies of HIV/AIDS

描述(由申请人提供)多结果和多变量过程 在艾滋病毒和艾滋病研究中，数据是例行收集的。示例包括 疾病状态的多个标记，如CD4细胞计数和艾滋病毒-RNA水平 (病毒载量)；疾病进展的多个终点，如CD4的变化 和艾滋病进展的时间；双变量治疗-反应过程，例如 时变抗逆转录病毒治疗和病毒负荷反应；或几种措施 指基础结构，例如用于评估的多个标尺 神经认知功能。此外，在长期研究中，如自然 历史研究，辍学(自然减退)可以被认为是一个过程 本身，并调整对主要终点的分析，以进行潜在选择 偏差通常需要将估计过程框定为 端点和辍学机制的联合分布。存在着一种 用于一次分析一个结果或过程的各种统计工具， 例如事件历史模型(例如，COX比例风险模型)和 重复测量(例如随机效应模型或广义估计 方程式)，但允许对几个结果进行综合分析的要少得多 同时。这个项目的主要目标是开发和传播 新的生物统计学方法将使艾滋病毒和艾滋病的研究人员 研究以有意义和可解释的方式解决中心重要性 来自产生复杂结果数组的研究的问题。 这一目标将通过三种方式实现。首先，统计模型 基于CD4细胞计数联合进化的HIV疾病分期 和血浆病毒载量将被开发：这包括推导出单变量 进展风险的衡量，以及艾滋病毒阶段的经验分类。这个 后者将在潜在班级模型的背景下开发。这个 分类方法将应用于多个 遇到指标，如抑郁的纵向模式和 神经认知能力的多个指标。第二，最先进的方法 对于因果推理将进行比较，并应用于纵向研究 高效抗逆转录病毒治疗(HAART)方案对几种 艾滋病毒自然病史的各个方面，包括CD4和病毒载量的变化， 健康服务利用和身体质量分布。第三，高度的 估计协变量效应的灵活混合模型 将开发与结果相关的辍学的纵向数据。这些型号 旨在对辍学有透明的假设，并允许 用于检查选择偏差的可能范围的灵敏度分析。 新的统计方法论的研究一直受到分析方法的推动 艾滋病毒和艾滋病纵向队列研究中出现的问题。因此，我们 将使用我们的方法来解决三个关于艾滋病毒天然的研究中的关键问题 病史：她的一个由1300名女性组成的队列追踪了七年；活着的一个队列 3000例静脉吸毒人群健康生活营养状况调查 这项研究是在新英格兰对大约700人进行的一项队列研究。

DESCRIPTION (provided by applicant) Multiple outcomes and multivariate process data are collected routinely in HIV and AIDS research. Examples include multiple markers of disease state, such as CD4 cell counts and HIV-RNA Levels (viral load); multiple endpoints for disease progression, such as change in CD4 and time to AIDS progression; bivariate treatment-response processes, such as timevarying antiretroviral therapy and viral load response; or several measures of an underlying construct, such as multiple scales for assessing neurocognitive functioning. Moreover, in long-term studies such as natural history studies, dropout (attrition) can be considered as a process unto itself, and adjusting analyses of the primary endpoint for potential selection biases frequently requires framing the estimation procedure in terms of the joint distribution of the endpoint and the dropout mechanism. There exists a wide range of statistical tools for analyzing one outcome or process at a time, such as models for event histories (e.g. Cox proportional hazards model) and repeated measures (e.g. random effects models or generalized estimating equations), but far fewer that allow an integrated analysis of several outcomes simultaneously. The primary goal of this project is to develop and disseminate new biostatistical methods that will enable researchers in HIV and AIDS research to address, in meaningful and interpretable ways, centrally important questions from studies that generate complex arrays of outcomes. This objective will be met in three ways. First, statistical models for classifying HIV disease stage based on the joint evolution of CD4 cell count and plasma viral load will be developed: this includes deriving a univariate measure of progression risk, and empirical classifications of HIV stage. The latter will be developed in the context of latent class models. The classification methods will be applied to other settings where multiple indicators are encountered, such as longitudinal patterns of depression and multiple indicators of neurocognive ability. Second, state-of-the-art methods for causal inference will be compared and applied for studying longitudinal effects of highly-active antiretroviral therapy (HAART) regimens on several aspects of HIV natural history, including variations in CD4 and viral load, health services utilization, and distribution of body mass. Third, a highly flexible class of mixture models for estimating covariate effects from longitudinal data with outcome-related dropout will be developed. These models are designed to have transparent assumptions about dropout, and allow sensitivity analyses for inspecting the possible range of selection bias. The research on new statistical methodology has been motivated by analytic issues that arise in longitudinal cohort studies in HIV and AIDS. As such, we will use our methods to address key questions from three studies of HIV natural history: HERS, a cohort of 1300 women followed for seven years; ALIVE, a cohort study of 3000 intravenous drug users; and the Nutrition for Healthy Living Study, a cohort study of about 700 in New England.