Analytical Methods/HIV Treatment and Co-factor Effects

分析方法/HIV 治疗和辅助因素效应

• 批准号: 7387337
• 负责人: JAMES M ROBINS
• 金额: $ 53.98万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-08-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7387337
• 关键词: Acquired Immunodeficiency Syndrome; Algorithms; Back; CCR5 gene; CD4 Lymphocyte Count; Childhood; Class; Clinical; Cohort Studies; Compatible; Computer software; Computing Methodologies; Confidence Intervals; Cox Proportional Hazards Models; Data; Databases; Derivation procedure; Development; Disease; Disease Progression; Equation; Face; HIV; Highly Active Antiretroviral Therapy; Hospitals; Infection; Investigation; Left; Maps; Markov Chains; Methodology; Methods; Microcomputers; Modeling; Needle Sharing; Numbers; Observational Study; Outcome; Patients; Persons; Pharmaceutical Preparations; Physicians; Prevalence; Protease Inhibitor; Protocols documentation; Randomized; Randomized Controlled Clinical Trials; Randomized Controlled Trials; Rate; Recording of previous events; Research Personnel; Resistance; Risk Behaviors; Risk Factors; Sampling; Sex Behavior; Solutions; Staging; Standards of Weights and Measures; Stochastic Processes; Structural Models; Surveys; Testing; Time; Treatment Protocols; Uncertainty; Vaccination; Validation; Viral; Woman; Work; analytical method; base; cofactor; cost; data modeling; design; dosage; novel; response; theories; user-friendly; vaccine efficacy

DESCRIPTION (provided by applicant): The principal aim of this proposal is to further development of new methods for analyzing observational data bases and randomized trials of HIV-infected persons and the application of these methods to data obtained in randomized and observational studies in an attempt to help answer important open substantive questions concerning the treatment and course of HIV-related disease. The proposed approaches are based either on (i) the estimation of new classes of causal models which include structural nested models, marginal structural models (MSMs), direct effect structural nested models, continuous time structural nested models, and optimal regime structural models (SNMs). Many of the new methods are fundamentally "epidemiologic" in that they require data on time-dependent confounding factors, that is, risk factors for outcomes that also predict subsequent treatment with the drug or cofactor under study. In particular, we plan to use optimal regime SNMs to help determine the optimal times to start HAART therapy and to change HAART regimens as a function of a subject's CD4 count, HIV RNA, clinical history, and, where available, results of genotypic or phenotypic resistance testing. Specifically, we plan to reanalyze, with collaborators, data from the Multicenter AIDS Cohort Study, The Women's Interagency HIV Study, The Swiss HIV Cohort Study, The Study of The Consequences of Protease Inhibitor Era (SCOPE), the French Hospital Database on HIV (FHDH) study, the Pediatric Late Outcomes Protocol (PACTG 219) and the ALLRT study.

描述（由申请人提供）：本提案的主要目的是进一步开发用于分析HIV感染者的观察性数据库和随机试验的新方法，并将这些方法应用于随机和观察性研究中获得的数据，以帮助回答有关HIV相关疾病治疗和病程的重要开放实质性问题。所提出的方法是基于（i）估计的新类别的因果模型，其中包括结构嵌套模型，边际结构模型（MSM），直接效应结构嵌套模型，连续时间结构嵌套模型，和最佳制度结构模型（SNM）。许多新方法从根本上讲是“流行病学”的，因为它们需要关于时间依赖性混杂因素的数据，也就是说，结果的风险因素也预测了研究中的药物或辅因子的后续治疗。 特别是，我们计划使用最佳方案SNM来帮助确定开始HAART治疗的最佳时间，并根据受试者的CD4计数、HIV RNA、临床病史以及基因型或表型耐药检测结果（如适用）来改变HAART方案。具体来说，我们计划与合作者一起重新分析多中心艾滋病队列研究、妇女机构间艾滋病研究、瑞士艾滋病队列研究、蛋白酶抑制剂时代后果研究（SCOPE）、法国医院艾滋病数据库（FHDH）研究、儿科晚期结局方案（PACTG 219）和ALLRT研究的数据。