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.

摘要