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 更健壮和准确的健康知识是更好的健康政策和行动的基石。有 艾滋病毒中的棘手问题可以通过新的定量工具来更好地解决。实验结果 通过开发和使用，可以使观察性艾滋病毒研究变得更好，更相关 统计，流行病学，因果推理和人工智能的界面上的新方法。一个 半参数统计理论，因果模型和集合机器学习的创新组合 为艾滋病毒研究提供了更好的结果。在这项工作中，我们提出了新的估计器 风险（或生存）功能。这些新的估计器提高了准确性，适应竞争活动，允许 要概括到特定感兴趣的人群的效果，并结合机器学习的滋扰功能 放宽有关模型形式的假设，并允许灵敏度分析以量化不受控制的影响 偏见。具体的目的是开发，测试和传播这些新估计器的工具。这些目标是 至1）估计在这个大型美国队列中，全因和特定原因死亡的长期治疗历史 艾滋病毒的妇女； 2）使用治疗估算每项协议参数的观察类似物 比较基于集成酶抑制剂治疗的复合终点的决策设计比较 在北美艾滋病研究与设计方面的基于efavirenz的治疗； 3）估计TDF-FTC与ABC-3TC臂的每项协议参数； 4）估计 17个α-羟基酸蛋白酶的协议参数与蒙面的安慰剂，处于早产的风险 赞比亚艾滋病毒+孕妇的出生。组装的团队具有现场领先的专业知识 流行病学，统计和艾滋病毒医学。科学产品将包括出版物和研讨会 演讲描述了新的方法论方法和新的实质发现 将提出的方法应用于特定目的中确定的每个问题。我们还将生产 公开可用的R软件包和SAS宏来实施拟议的估计器。