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用于设计 COVID-19 疫苗疗效试验的集成预测建模。

Ensemble forecast modeling for the design of COVID-19 vaccine efficacy trials.

机构信息

Department of Biostatistics, University of Florida, Gainesville, FL, United States.

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA, United States.

出版信息

Vaccine. 2020 Oct 27;38(46):7213-7216. doi: 10.1016/j.vaccine.2020.09.031. Epub 2020 Sep 15.

Abstract

To rapidly evaluate the safety and efficacy of COVID-19 vaccine candidates, prioritizing vaccine trial sites in areas with high expected disease incidence can speed endpoint accrual and shorten trial duration. Mathematical and statistical forecast models can inform the process of site selection, integrating available data sources and facilitating comparisons across locations. We recommend the use of ensemble forecast modeling - combining projections from independent modeling groups - to guide investigators identifying suitable sites for COVID-19 vaccine efficacy trials. We describe an appropriate structure for this process, including minimum requirements, suggested output, and a user-friendly tool for displaying results. Importantly, we advise that this process be repeated regularly throughout the trial, to inform decisions about enrolling new participants at existing sites with waning incidence versus adding entirely new sites. These types of data-driven models can support the implementation of flexible efficacy trials tailored to the outbreak setting.

摘要

为了快速评估 COVID-19 候选疫苗的安全性和有效性,可以优先选择预计发病率较高地区的疫苗试验点,从而加快终点入组速度并缩短试验持续时间。数学和统计预测模型可以为选址过程提供信息,整合可用的数据源,并促进各地点之间的比较。我们建议使用集成预测建模 - 结合来自独立建模小组的预测 - 来指导研究人员确定 COVID-19 疫苗有效性试验的合适地点。我们描述了该过程的适当结构,包括最低要求、建议的输出以及用于显示结果的用户友好工具。重要的是,我们建议在整个试验过程中定期重复此过程,以决定是在发病率下降的现有地点招募新参与者,还是添加全新的地点。这类基于数据的模型可以支持实施针对疫情环境的灵活有效性试验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9891/7492005/7eaac3f8d33a/gr1_lrg.jpg

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