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：
8466626
负责人：
Stephen R Cole
金额：
$ 36.95万
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/8466626
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 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 public health relevance randomized trial research study simulation 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，应用和传播高级但实际的定量方法，以从对感染HIV感染的个体的复杂纵向数据中准确推断。边际结构模型是一种基于观察数据来估算病因效应的准确且灵活的潜在结果方法。这种用于纵向数据分析的创新方法在学术文献中的外观迅速增加。该提案的目的是：（1）扩展边缘结构模型，以将估计值从研究样本概括为目标人群；（2）将边际结构模型扩展到嵌套在同类中的研究；（3）扩展边缘结构模型以说明差异暴露测量误差；（4）扩展边缘结构模型，以使用贝叶斯方法来说明事先信息；（5）开发了一个边缘结构模型的估计值，该模型结合了反比概率加权和参数G形成型的估计；（6）开发广泛适用的统计软件，以实现AIMS 1-5中扩展的方法。目标1-5中定义的创新对于边际结构模型对于应用艾滋病毒和公共卫生的应用科学家更有用是必不可少的。为了实现这些目标，我们将使用综合临床系统（CFAR）网络（CNICS）的重要及时的经验数据。 CNIC包括自2006年以来在8个CFAR部位的临床护理中每季度看到的20,000多名艾滋病毒感染的成年人。这项研究的果实将在每种目标的一系列同行评审论文中描述。这些论文将定义每个问题，描述一个创新的解决方案，以蒙特卡洛模拟实验为解决方案提供支持，使用CNICS数据分析说明方法，并为实施解决方案的指南，使用开发的统计软件实施解决方案。总之，本提案描述了一项重要且创新的研究计划，该计划将产生开创性的方法学工具，以从艾滋病毒和其他疾病的复杂观察性研究中进行准确推断。