Analyzing Multiple Process Data in Studies of HIV/AIDS

描述（由申请人提供）多个结果和多变量过程 在艾滋病毒和艾滋病研究中定期收集数据。实例包括 疾病状态的多种标志物，如CD 4细胞计数和HIV-RNA水平 （病毒载量）;疾病进展的多个终点，如CD 4 和至艾滋病进展的时间;双变量治疗反应过程，如 时变抗逆转录病毒治疗和病毒载量反应;或几种措施 一个基本的结构，如多尺度评估 神经认知功能此外，在长期的研究中，如自然 历史研究，辍学（自然减员）可以被认为是一个过程， 本身，并调整潜在选择的主要终点分析 偏见往往需要框架的估计程序， 端点和脱落机制的联合分布。存在 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，考克斯比例风险模型），以及 重复测量（例如随机效应模型或广义估计 方程），但允许对几个结果进行综合分析的要少得多 同步该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研究人员 研究以有意义和可解释的方式， 这些问题来自于产生复杂结果的研究。 将通过三种方式实现这一目标。首先，统计模型 根据CD 4细胞计数的联合演变对HIV疾病阶段进行分类 将开发血浆病毒载量：这包括推导单变量 进展风险的测量和HIV阶段的经验分类。的 后者将在潜在类模型的背景下开发。的 分类方法将应用于其他设置， 遇到的指标，如抑郁症的纵向模式， 神经认知能力的多种指标。第二，最先进的方法 的因果推理将进行比较，并应用于研究纵向 高效抗逆转录病毒治疗（HAART）方案对几种 HIV自然史的各个方面，包括CD 4和病毒载量的变化， 卫生服务利用和身体质量分布。第三，高度 估计协变量效应的灵活类混合模型 将开发与结果相关的辍学纵向数据。这些模型 旨在对辍学有透明的假设，并允许 敏感性分析，用于检查选择偏差的可能范围。 新的统计方法论的研究是由分析方法论推动的。 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项艾滋病毒自然感染研究中的关键问题， 历史：HERS，一个1300名妇女的队列，随访7年; ALIVE，一个队列， 对3000名静脉注射吸毒者的研究;以及健康生活营养 一项在新英格兰对大约700人进行的队列研究。

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.