CoVPN Cross-Protocol Analyses

CoVPN 跨协议分析

基本信息

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
项目类别：
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

项目摘要

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的小组领导者，特别是免疫相关评估，以及我们与 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。