CoVPN Cross-Protocol Analyses

CoVPN 跨协议分析

• 批准号: 10570780
• 负责人: Peter B. Gilbert
• 金额: $ 133.49万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-29 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10570780
• 关键词: 2019-nCoV; Address; Antibodies; Biometry; COVID-19; COVID-19 Prevention Network; COVID-19 outbreak; COVID-19 pandemic; COVID-19 vaccine; Clinical; Clinical Trials Network; Disease; Enrollment; HIV Vaccine Trials Network; Health; Immune; Immune response; Immunity; Incidence; Individual; Infection; Laboratories; Lead; Leadership; Mediator of activation protein; Methods; Modeling; Monitor; Participant; Periodicity; Phase; Population; Protocols documentation; Reporting; Reproducibility; Risk; SARS-CoV-2 infection; Safety; Signal Transduction; Site; Standardization; Statistical Methods; Techniques; United States National Institutes of Health; Vaccination; Vaccines; Work; base; cohort; computer code; data management; design; efficacy trial; epidemiological model; experience; immunogenicity; improved; innovation; intervention effect; pathogen; recruit; response; tool; vaccine efficacy; vaccine evaluation; vaccine safety; vaccine trial

Project Abstract This proposal outlines the scientific agenda for the COVID-19 Prevention Network (CoVPN) Vaccines Statistical and Data Management Center (SDMC) for implementation of cross-protocol work in support of COVID-19 phase 3 efficacy trials. With the global COVID-19 pandemic, we recognize a significant need for vaccines that modify COVID-19 in SARS-CoV-2 infected individuals. Addressing this gap, the HVTN SDMC has joined 4 other National Institute of Health (NIH) clinical trial networks to form the CoVPN, an enhanced network dedicated to developing globally effective vaccines for SARS-CoV-2. The innovation of the CoVPN SDMC and advantages to this work include the collective experience of our group- leader in statistical methods in vaccine evaluation, especially immune correlates assessment, and the long-standing relationships we have with the clinical and lab groups in the CoVPN lead Adapting and leveraging statistical methods for evaluating vaccines against other pathogens to SARS-CoV-2. E.g. causal inference methods for evaluating multiple types of formally defined correlates of protection including correlates of vaccine efficacy, mediators of vaccine efficacy, and stochastic-intervention effects on vaccine efficacy.

项目摘要 该提案概述了 COVID-19 预防网络 (CoVPN) 疫苗的科学议程 统计和数据管理中心（SDMC），用于实施跨协议工作以支持 COVID-19 3 期疗效试验。 随着全球 COVID-19 大流行，我们认识到对改变 COVID-19 的疫苗的巨大需求 SARS-CoV-2 感染者。为了解决这一差距，HVTN SDMC 加入了其他 4 个国家研究所的行列 美国国立卫生研究院 (NIH) 临床试验网络组建 CoVPN，这是一个致力于开发 全球有效的 SARS-CoV-2 疫苗。 CoVPN SDMC 的创新和这项工作的优势包括我们的集体经验 疫苗评估统计方法（尤其是免疫相关评估）领域的组长 我们与 CoVPN 领导者的临床和实验室团队建立了长期合作关系 调整和利用统计方法来评估针对 SARS-CoV-2 其他病原体的疫苗。 例如。用于评估多种类型的正式定义的保护相关性的因果推理方法 包括疫苗功效的相关性、疫苗功效的中介因素以及随机干预效应 疫苗功效。