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定量方法，以便从艾滋病毒感染者的复杂纵向数据中进行准确推断。边际结构模型是一种准确、灵活的基于潜在结局的方法，用于使用观察数据估计病因学效应。这种纵向数据分析的创新方法在学术文献中迅速增加。本提案的目的是：（1）扩展边际结构模型，以将研究样本的估计值推广到目标人群;（2）将边际结构模型扩展到嵌套队列研究;（3）扩展边际结构模型，以考虑不同的暴露测量误差;（4）扩展边际结构模型，以考虑使用贝叶斯方法的先验信息;（5）为边际结构模型开发估计，该模型联合收割机了逆概率加权估计和参数G公式;（6）开发广泛适用的统计软件，以实施目标1-5中的扩展方法。目标1-5中界定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至关重要。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。自2006年以来，全国艾滋病毒感染者和艾滋病控制中心每季度在8个艾滋病毒和艾滋病控制中心的临床护理中接诊20 000多名感染艾滋病毒的成年人。这项研究的成果将在一系列针对每个目标的同行评议论文中进行描述。这些论文将定义每个问题，描述一个创新的解决方案，提供支持的解决方案的Monte Carlo模拟实验，说明使用CNICS数据分析的方法，并提供指导实施的解决方案，使用开发的统计软件。最后，本提案描述了一个重要和创新的研究计划，将产生开创性的方法工具，从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.