Use of Real-World EMR Data to Rapidly Evaluate Treatment Effects of Existing Drugs for Emerging Infectious Diseases: Remdesivir for COVID-19 Treatment as an Example

IF 0.8 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Statistics in Biosciences Pub Date : 2024-01-02 DOI:10.1007/s12561-023-09411-8

Chenguang Zhang, Masayuki Nigo, Shivani Patel, Duo Yu, Edward Septimus, Hulin Wu

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利用真实世界的 EMR 数据快速评估现有药物对新发传染病的治疗效果：以治疗 COVID-19 的雷米地韦为例

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics in Biosciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

2.00

自引率

0.00%

发文量

期刊介绍： Statistics in Biosciences (SIBS) is published three times a year in print and electronic form. It aims at development and application of statistical methods and their interface with other quantitative methods, such as computational and mathematical methods, in biological and life science, health science, and biopharmaceutical and biotechnological science. SIBS publishes scientific papers and review articles in four sections, with the first two sections as the primary sections. Original Articles publish novel statistical and quantitative methods in biosciences. The Bioscience Case Studies and Practice Articles publish papers that advance statistical practice in biosciences, such as case studies, innovative applications of existing methods that further understanding of subject-matter science, evaluation of existing methods and data sources. Review Articles publish papers that review an area of statistical and quantitative methodology, software, and data sources in biosciences. Commentaries provide perspectives of research topics or policy issues that are of current quantitative interest in biosciences, reactions to an article published in the journal, and scholarly essays. Substantive science is essential in motivating and demonstrating the methodological development and use for an article to be acceptable. Articles published in SIBS share the goal of promoting evidence-based real world practice and policy making through effective and timely interaction and communication of statisticians and quantitative researchers with subject-matter scientists in biosciences.