Structural models for treatment and exposure effects in clinical HIV cohorts

描述(由申请人提供)：这项拟议研究的总体目标是开发、应用和传播先进而实用的量化方法，以便能够从复杂的纵向数据中准确推断感染艾滋病毒的个人。边缘结构模型是一种准确和灵活的基于潜在结果的方法，用于使用观测数据估计病因效应。这种用于纵向数据分析的创新方法在学术文献中迅速出现。该建议的目的是：(1)扩展边际结构模型以将研究样本的估计推广到目标总体；(2)将边际结构模型扩展到嵌套在队列中的研究；(3)扩展边际结构模型以考虑差异暴露测量误差；(4)使用贝叶斯方法扩展边际结构模型以考虑先验信息；(5)开发结合逆概率加权估计和参数G公式的边际结构模型的估计；以及(6)开发广泛适用的统计软件以实现目标1-5的扩展方法。目标1-5中定义的创新对于边际结构模型变得对研究艾滋病毒和公共卫生的应用科学家更有用是至关重要的。为了实现这些目标，我们将使用来自艾滋病研究中心(CFAR)综合临床系统网络(CNICs)的重要和及时的经验数据。CNIC包括自2006年以来在8个CFAR地点每季度在临床护理中看到的2万多名艾滋病毒感染成年人。这项研究的成果将在针对每个目标的一系列同行评议论文中描述。这些论文将定义每个问题，描述一个创新的解决方案，通过蒙特卡洛模拟实验为该解决方案提供支持，说明使用CNICs数据分析的方法，并使用开发的统计软件提供实施该解决方案的指导。总之，本提案描述了一项重要的创新研究计划，该计划将产生开创性的方法学工具，从对艾滋病毒和其他疾病的复杂观察研究中做出准确的推断。

DESCRIPTION (provided by applicant): The overall goal of this proposed research is to develop, apply and disseminate advanced, yet practical quantitative methods to enable accurate inference from complex longitudinal data on individuals infected with HIV. Marginal structural models are an accurate and flexible potential outcomes-based approach for estimating etiologic effects using observational data. This innovative approach for longitudinal data analysis has a rapidly increasing appearance in the scholarly literature. The aims of this proposal are to: (1) extend marginal structural models to generalize estimates from a study sample to a target population; (2) extend marginal structural models to studies nested in cohorts; (3) extend marginal structural models to account for differential exposure measurement error; (4) extend marginal structural models to account for prior information using Bayesian methods; (5) develop estimates for a marginal structural model that combine estimation from inverse probability weighting and the parametric G-formula; and (6) develop widely- applicable statistical software to implement methods for extensions in aims 1-5. The innovations defined in aims 1-5 are essential for marginal structural models to become more useful to applied scientists studying HIV and public health. To accomplish these aims, we will use important and timely empirical data from the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS). The CNICS includes over 20,000 HIV infected adults seen quarterly in clinical care at 8 CFAR sites since 2006. The fruits of this research will be described in a series of peer-reviewed papers for each aim. These papers will define each problem, describe an innovative solution, provide support for the solution in terms of Monte Carlo simulation experiments, illustrate the method using analysis of CNICS data, and provide guidance on implementation of the solution using developed statistical software. In conclusion, the present proposal describes a significant and innovative program of research that will result in groundbreaking methodological tools for making accurate inferences from complex observational studies of HIV and other diseases.