Improved analysis of experiments and observational studies in HIV

改进艾滋病毒实验和观察研究的分析

• 批准号: 10268264
• 负责人: Stephen R Cole
• 金额: $ 72.13万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-22 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10268264
• 关键词: AIDS clinical trial group; Acquired Immunodeficiency Syndrome; Address; American; Artificial Intelligence; Caproates; Cohort Analysis; Collaborations; Computer software; Data; Data Analyses; Development; Educational workshop; Epidemiologist; Epidemiology; Event; Goals; HIV; Hand; Health Policy; Hydroxyprogesterone; Integrase Inhibitors; Lamivudine; Machine Learning; Masks; Medicine; Methodology; Methods; Modeling; Modernization; Observational Study; Odds Ratio; Outcome Measure; Paper; Placebos; Policies; Population; Pregnant Women; Premature Birth; Probability; Programming Languages; Protocols documentation; Public Health; Publications; Randomized; Recording of previous events; Reproducibility; Research; Research Design; Risk; Risk Estimate; Series; Testing; Vertebral column; Woman; Work; analog; arm; base; causal model; clinical decision-making; cohort; complex data; data tools; design; efavirenz; experimental study; health knowledge; improved; innovation; interest; mortality; novel strategies; open source; semiparametric; statistics; symposium; theories; tool; treatment comparison; virology

ABSTRACT More robust and accurate health knowledge is a cornerstone of better health policy and action. There are tough questions in HIV that can be addressed better with new quantitative tools. Results from experimental and observational HIV studies can be made better and more policy-relevant through development and use of new methods at the interface of statistics, epidemiology, causal inference, and artificial intelligence. An innovative combination of semiparametric statistical theory, causal models, and ensemble machine learning provides a unique opportunity for better results from HIV studies. In this work, we propose new estimators of the risk (or survival) function. These new estimators improve accuracy, accommodate competing events, allow effects to be generalized to specific populations of interest, incorporate machine learning of nuisance functions to relax assumptions about model form, and allow sensitivity analyses to quantify the impact of uncontrolled biases. The specific aims are vehicles to develop, test, and disseminate these new estimators. These aims are to 1) estimate the long-term treated history of all-cause and cause-specific mortality in this large US cohort of women with HIV; 2) estimate the observational analog of the per-protocol parameter using a treatment decision design to compare composite endpoints under an integrase-inhibitor-based treatment compared to an efavirenz-based treatment in the North American AIDS Cohort Collaboration on Research and Design; 3) estimate the per-protocol parameter for TDF-FTC versus ABC-3TC arms; and 4) estimate the per- protocol parameter for 17 alpha-hydroxyprogesterone caproate versus masked placebo on risk of preterm birth in Zambian HIV+ pregnant women. The assembled team features field-leading expertise in epidemiology, statistics, and HIV medicine. Scientific products will include publications and workshop presentations describing new methodological approaches and new substantive findings that emerge after applying the proposed methods to each of the problems identified in the specific aims. We will also produce publicly available R packages and SAS macros to implement the proposed estimators.

抽象的 更可靠、更准确的健康知识是更好的卫生政策和行动的基石。有 艾滋病毒中的棘手问题可以通过新的定量工具得到更好的解决。实验结果 通过开发和使用以下方法，可以使艾滋病毒观察性研究变得更好、更具政策相关性： 统计学、流行病学、因果推理和人工智能接口的新方法。一个 半参数统计理论、因果模型和集成机器学习的创新组合 为获得更好的艾滋病毒研究结果提供了独特的机会。在这项工作中，我们提出了新的估计量 风险（或生存）函数。这些新的估算器提高了准确性，适应竞争事件，允许 将影响推广到特定的感兴趣人群，结合机器学习的滋扰功能 放宽对模型形式的假设，并允许敏感性分析来量化不受控制的影响 偏见。具体目标是开发、测试和传播这些新估计器的工具。这些目标是 1) 估计美国这一大型队列中全因和特定原因死亡率的长期治疗史 感染艾滋病毒的妇女； 2) 使用治疗方法估计每个方案参数的观察模拟 比较基于整合酶抑制剂的治疗下的复合终点的决策设计 北美艾滋病队列研究和设计合作中基于依非韦伦的治疗； 3) 估计 TDF-FTC 与 ABC-3TC 组的每个方案参数； 4）估计每 17 α-羟基黄体酮己酸酯与掩盖安慰剂对早产风险的方案参数 赞比亚艾滋病病毒感染者孕妇的分娩。组建的团队拥有领域领先的专业知识 流行病学、统计学和艾滋病毒医学。科学产品将包括出版物和研讨会 介绍了新的方法论和新的实质性发现 将所提出的方法应用于具体目标中确定的每个问题。我们还将生产 公开可用的 R 包和 SAS 宏来实现建议的估计器。