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剂量-反应荟萃分析:使用 R 软件的应用和实践。

Dose-response meta-analysis: application and practice using the R software.

机构信息

Department of Preventive Medicine, Korea University College of Medicine, Seoul, Korea.

Urological Biomedicine Research Institute, Soonchunhyang University Hospital, Seoul, Korea.

出版信息

Epidemiol Health. 2019;41:e2019006. doi: 10.4178/epih.e2019006. Epub 2019 Mar 28.

Abstract

The objective of this study was to describe the general approaches of dose-response meta-analysis (DRMA) available for the quantitative synthesis of data using the R software. We conducted a DRMA using two types of data, the difference of means in continuous data and the odds ratio in binary data. The package commands of the R software were "doseresmeta" for the overall effect sizes that were separated into a linear model, quadratic model, and restricted cubic split model for better understanding. The effect sizes according to the dose and a test for linearity were demonstrated and interpreted by analyzing one-stage and two-stage DRMA. The authors examined several flexible models of exposure to pool study-specific trends and made a graphical presentation of the dose-response trend. This study focused on practical methods of DRMA rather than theoretical concepts for researchers who did not major in statistics. The authors hope that this study will help many researchers use the R software to perform DRMAs more easily, and that related research will be pursued.

摘要

本研究旨在描述可用于使用 R 软件对数据进行定量综合的剂量-反应荟萃分析(DRMA)的一般方法。我们使用两种类型的数据进行了 DRMA,即连续数据的均值差和二项数据的优势比。R 软件的包命令为“doseresmeta”,用于将总体效应大小分为线性模型、二次模型和受限立方分割模型,以更好地理解。通过分析一阶段和两阶段 DRMA,展示和解释了根据剂量的效应大小和线性检验。作者检查了几种暴露的灵活模型,以汇集研究特定趋势,并以图形方式呈现剂量-反应趋势。本研究侧重于为非统计学专业的研究人员提供 DRMA 的实用方法,而不是理论概念。作者希望本研究将帮助许多研究人员更轻松地使用 R 软件进行 DRMA,并进行相关研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e103/6635664/0d1e89c49e49/epih-41-e2019006f1.jpg

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