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: 10159693
• 负责人: Stephen R Cole
• 金额: $ 72.52万
• 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/10159693
• 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.

摘要 更健全和准确的卫生知识是更好的卫生政策和行动的基石。有 艾滋病毒中坚韧问题可以用新的量化工具更好地解决。实验结果 通过开发和使用 统计学、流行病学、因果推理和人工智能界面的新方法。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 提供了一个独特的机会，从艾滋病毒研究更好的结果。在这项工作中，我们提出了新的 风险（或生存）函数。这些新的估计提高了准确性，适应竞争事件，允许 将影响推广到感兴趣的特定人群，包括滋扰功能的机器学习 放松对模型形式的假设，并允许敏感性分析量化不受控制的影响， 偏见具体目标是开发，测试和传播这些新的估计工具。这些目的是 1）估计该大型美国队列中全因和病因特异性死亡率的长期治疗史 2）使用治疗估计符合方案参数的观察模拟 比较基于整合酶抑制剂治疗的复合终点的决策设计 在北美艾滋病队列研究与设计合作组织中接受基于依法韦仑的治疗; 3)估计TDF-FTC与ABC-3 TC组的符合方案参数;以及4）估计符合方案参数。 17 α-羟孕酮己酸酯与设盲安慰剂对早产风险的方案参数 赞比亚艾滋病毒阳性孕妇的分娩。组建的团队拥有领先的专业知识， 流行病学、统计学和艾滋病医学。科学产品将包括出版物和讲习班 介绍新的方法和新的实质性结论， 将提出的方法应用于具体目标中确定的每一个问题。我们还将生产 公开可用的R包和SAS宏来实现所提出的估计。