Structural models for treatment and exposure effects in clinical HIV cohorts

临床 HIV 队列中治疗和暴露效应的结构模型

基本信息

批准号：
8810299
负责人：
Stephen R Cole
金额：
$ 4.73万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-03-19 至 2017-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8810299
关键词：
Accounting Acquired Immunodeficiency Syndrome Adult Affect Appearance Bayesian Method Benchmarking Classification Clinical Cohort Studies Collection Complex Computer software Cox Proportional Hazards Models Data Data Analyses Disease Eligibility Determination Epidemiologist Event Failure Fruit Goals Guidelines HIV Health Health Policy Heterogeneity Individual Information Systems Knowledge Laboratories Literature Measurement Measures Mediator of activation protein Medical Medical History Methods Modeling Monte Carlo Method Observational Study Outcome Outpatients Paper Patients Peer Review Probability Public Health Randomized Research Research Design Robin bird Sampling Studies Scientist Series Site Solutions Statistical Models Structural Models System Target Populations Therapeutic Effect Time To specify Treatment Protocols Weight Woman Work antiretroviral therapy base biomedical scientist case control clinical care clinically relevant cohort demographics evidence base flexibility improved innovation meetings mortality novel programs prospective randomized trial research study tool treatment effect

项目摘要

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.

描述（由申请人提供）：这项拟议研究的总体目标是开发、应用和传播先进且实用的定量方法，以便能够从艾滋病毒感染者的复杂纵向数据中进行准确推断。边缘结构模型是一种准确且灵活的基于潜在结果的方法，用于使用观察数据估计病因效应。这种纵向数据分析的创新方法在学术文献中的出现迅速增加。该提案的目的是：（1）扩展边际结构模型，将研究样本的估计推广到目标人群；（2）将边际结构模型扩展到队列中的嵌套研究； (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解释先验信息； (5) 结合逆概率加权和参数 G 公式的估计，开发边际结构模型的估计； (6) 开发广泛适用的统计软件来实施目标 1-5 中的扩展方法。目标 1-5 中定义的创新对于边际结构模型对于研究艾滋病毒和公共卫生的应用科学家来说变得更加有用至关重要。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) 的重要且及时的经验数据。自 2006 年以来，CNICS 包括超过 20,000 名 HIV 感染成年人，每季度在 8 个 CFAR 站点进行临床护理。这项研究的成果将在针对每个目标的一系列同行评审论文中进行描述。这些论文将定义每个问题，描述创新的解决方案，通过蒙特卡罗模拟实验为解决方案提供支持，通过 CNICS 数据分析说明该方法，并为使用开发的统计软件实施该解决方案提供指导。总之，本提案描述了一项重要且创新的研究计划，该计划将产生突破性的方法工具，用于从艾滋病毒和其他疾病的复杂观察研究中做出准确的推论。