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评估流感疫苗相对有效性研究中测量和未测量混杂因素的方法:文献综述。

Methods to account for measured and unmeasured confounders in influenza relative vaccine effectiveness studies: A brief review of the literature.

机构信息

Global Medical Evidence Generation, Sanofi, Swiftwater, Pennsylvania, USA.

Department of Modeling, Epidemiology, and Data Science, Sanofi, Lyon, France.

出版信息

Influenza Other Respir Viruses. 2022 Sep;16(5):846-850. doi: 10.1111/irv.12999. Epub 2022 May 11.

Abstract

Observational seasonal influenza relative vaccine effectiveness (rVE) studies employ a variety of statistical methods to account for confounding and biases. To better understand the range of methods employed and implications for policy, we conducted a brief literature review. Across 37 included rVE studies, 10 different types of statistical methods were identified, and only eight studies reported methods to detect residual confounding, highlighting the heterogeneous state of the literature. To improve the comparability and credibility of future rVE research, researchers should clearly explain methods and design choices and implement methods to detect and quantify residual confounding.

摘要

观察性季节性流感相对疫苗效力 (rVE) 研究采用各种统计方法来解释混杂和偏倚。为了更好地了解所采用方法的范围和对政策的影响,我们进行了简要的文献回顾。在 37 项包含 rVE 研究的研究中,确定了 10 种不同类型的统计方法,只有 8 项研究报告了检测残余混杂的方法,这突出了文献的异质性状态。为了提高未来 rVE 研究的可比性和可信度,研究人员应清楚地解释方法和设计选择,并实施检测和量化残余混杂的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02c0/9343322/f5c6593a2e80/IRV-16-846-g003.jpg

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