Dynamic strategies for the clinical management of HIV disease

描述（由申请人提供）：观察性队列是，并将继续是不可或缺的，以评估艾滋病毒感染患者的临床策略。临床上最相关的策略是动态的，即在临床决策中纳入患者随时间变化的临床病史的策略。不幸的是，传统的统计方法不能适当地比较动态策略。相反，需要专门设计用于处理动态策略和随时间变化的混杂因素的方法。我们建议继续开发分析方法，以提高观察性艾滋病毒队列估计数的有效性。我们将建立在我们以前的合作努力，比较静态和动态策略的联合抗逆转录病毒治疗（cART）的有效性。我们建议通过将创新的分析方法应用于复杂的观察数据来回答有关HIV感染患者管理的关键临床问题。这些问题包括“何时转换cART？“，“什么卡开始？“（着眼于“什么时候切换？“和“切换到什么？和“何时监测免疫学和病毒学参数？“用于艾滋病患者的管理。我们的主要数据来源将是HIV-CAUSAL协作，我们将与欧洲，美国，和非洲我们将生成和维护用户友好的、公开可用的软件和详细的文档，以使这些方法可用于艾滋病毒研究界。我们的工作将实施和扩展尖端的方法来模拟随机试验使用观察数据，包括基于动态边际结构模型和参数g公式加权的逆概率的方法。这些方法没有得到广泛使用的一个原因是，人们认为这些方法技术复杂，使许多从业人员似乎无法使用。经过二十年的方法和软件开发，我们的团队现在正在将创新方法应用于大型前瞻性HIV队列，并表明这些方法可以添加到HIV研究人员的标准武器库中。该提案将加强艾滋病毒-因果关系合作组织作为观察性艾滋病毒研究新方法孵化器的国际作用。

DESCRIPTION (provided by applicant): Observational cohorts are, and will continue to be, indispensable to evaluate clinical strategies for HIV-infected patients. The most clinically relevant strategies are dynamic, that is, strategies that incorporate the patients' time-varying clinical history in the clinical decision. Unfortunately, conventional statistical methods cannot appropriately compare dynamic strategies. Rather methods specifically designed to deal with dynamic strategies and time-varying confounders are needed. We propose to continue to develop analytical methods to enhance the validity of estimates from observational HIV cohorts. We will build upon our previous collaborative efforts to compare the effectiveness of combined antiretroviral therapy (cART) of both static and dynamic strategies. We propose to answer key clinical questions about the management of HIV-infected patients by applying innovative analytic methods to complex observational data. These questions include "when to switch cART?", "what cART to start?" (with an eye towards "when to switch?" and "what to switch to?"), and "when to monitor immunologic and virologic parameters?" for the management of HIV patients. Our primary data source will be the HIV-CAUSAL Collaboration, and we will establish collaborations with other HIV consortia in Europe, the U.S., and Africa. We will generate and maintain user-friendly, publicly-available software and detailed documentation to make these methods available to the HIV research community. Our work will implement and extend cutting-edge approaches to emulate randomized trials using observational data, including methods based on inverse probability of weighting of dynamic marginal structural models and the parametric g-formula. One reason for the lack of widespread use of these methods is their perceived technical complexity, which has made them appear inaccessible to many practitioners. After two decades of methods and software development, our group is now applying innovative methods to large prospective HIV cohorts, and showing that these methods can be added to the standard arsenal of HIV researchers. This proposal will strengthen the international role of the HIV-CAUSAL Collaboration as an incubator for new methodologies in observational HIV research.