Dynamic strategies for the clinical management of HIV disease

描述(由申请人提供)：观察性队列对于评估艾滋病毒感染患者的临床策略是不可或缺的，而且将继续是不可或缺的。临床上最相关的策略是动态的，即在临床决策中结合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恰当地比较动态策略。相反，需要专门为处理动态战略和时变混杂因素而设计的方法。我们建议继续开发分析方法，以提高从观察到的艾滋病毒队列估计的有效性。我们将在以前合作努力的基础上，比较静态和动态策略的联合抗逆转录病毒疗法(CART)的有效性。我们建议通过对复杂的观察数据应用创新的分析方法来回答有关艾滋病毒感染患者管理的关键临床问题。这些问题包括“什么时候换购物车？”、“换什么购物车？”(着眼于“什么时候换？”和“换成什么？”)，以及“何时监测免疫学和病毒学参数？”用于艾滋病毒患者的管理。我们的主要数据来源将是艾滋病毒-因果合作，我们将与欧洲、美国和非洲的其他艾滋病毒联盟建立合作关系。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件，以便向艾滋病毒研究界提供这些方法。我们的工作将实现和扩展使用观测数据模拟随机试验的尖端方法，包括基于动态边缘结构模型的加权逆概率的方法和参数g公式。没有广泛使用这些方法的一个原因是它们被认为是技术上的复杂性，这使得许多从业者似乎无法接触到它们。经过二十年的方法和软件开发，我们小组现在正在将创新方法应用于大量预期的艾滋病毒队列，并表明这些方法可以添加到艾滋病毒研究人员的标准库中。这项提议将加强艾滋病毒-因果合作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.