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hiv感染患者的临床策略是并且将继续是不可或缺的。最具临床相关性的策略是动态的，即将患者时变的临床病史纳入临床决策的策略。不幸的是，传统的统计方法不能恰当地比较动态策略。相反，需要专门设计用于处理动态策略和时变混杂因素的方法。我们建议继续发展分析方法，以提高观察性HIV队列估计的有效性。我们将在之前的合作基础上，比较静态和动态策略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“何时切换购物车？”，“从哪个购物车开始？”（注意“什么时候换？”和“换什么？”）以及“何时监测免疫和病毒学参数？”我们的主要数据来源将是HIV- cause Collaboration，我们将与欧洲、美国和非洲的其他HIV联盟建立合作关系。我们将生成和维护用户友好的、可公开使用的软件和详细的文档，使这些方法可供艾滋病毒研究界使用。我们的工作将实现和扩展尖端的方法来模拟随机试验使用观测数据，包括基于反概率加权的动态边缘结构模型和参数g公式的方法。这些方法缺乏广泛使用的一个原因是它们的技术复杂性，这使得许多实践者似乎无法使用它们。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的方法应用于大规模的预期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。这一建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的孵化器的国际作用。

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.