{"title":"Doing Meta-Analysis with R - A Hands-On Guide","authors":"C. Lortie","doi":"10.18637/jss.v102.b02","DOIUrl":null,"url":null,"abstract":"Scientific synthesis is a diverse field of contemporary science. Syntheses advance knowledge in many domains and can include data compilation, theory syntheses, methods contrasts, and systematic reviews with meta-analyses through an integrated and big-picture view of evidence (Halpern et al. 2020). All these knowledge tools are typically strongly supported by statistical software including the open-source programming language R. Within this environment, there are nearly 100 packages to support meta-analyses each with different functions and specific capabilities (Lortie and Filazzola 2020). Meta-analyses are defined in most domains as the calculation of effect sizes or a weighted relative strength of evidence from a set of studies or trials to then subsequently examine high-level statistical patterns and variance (Gurevitch, Koricheva, Nakagawa, and Stewart 2018). They are increasingly used in many fields of science to examine consilience in hypotheses (Lortie 2014) and have been proposed as the gold or even platinum standard of evidence when there is statistical agreement in the efficacy of an intervention across studies (Stegenga 2011). Consequently, there is a critical need for accessible, pragmatic publications, resources, and texts that enable scientists with varying levels of expertise to engage in scientific syntheses using meta-analysis.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"622","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Software","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.18637/jss.v102.b02","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 622
Abstract
Scientific synthesis is a diverse field of contemporary science. Syntheses advance knowledge in many domains and can include data compilation, theory syntheses, methods contrasts, and systematic reviews with meta-analyses through an integrated and big-picture view of evidence (Halpern et al. 2020). All these knowledge tools are typically strongly supported by statistical software including the open-source programming language R. Within this environment, there are nearly 100 packages to support meta-analyses each with different functions and specific capabilities (Lortie and Filazzola 2020). Meta-analyses are defined in most domains as the calculation of effect sizes or a weighted relative strength of evidence from a set of studies or trials to then subsequently examine high-level statistical patterns and variance (Gurevitch, Koricheva, Nakagawa, and Stewart 2018). They are increasingly used in many fields of science to examine consilience in hypotheses (Lortie 2014) and have been proposed as the gold or even platinum standard of evidence when there is statistical agreement in the efficacy of an intervention across studies (Stegenga 2011). Consequently, there is a critical need for accessible, pragmatic publications, resources, and texts that enable scientists with varying levels of expertise to engage in scientific syntheses using meta-analysis.
科学综合是当代科学的一个多元化领域。综合促进了许多领域的知识,可以包括数据汇编、理论综合、方法对比,以及通过综合和大视角的证据进行meta分析的系统综述(Halpern et al. 2020)。所有这些知识工具通常都得到统计软件的大力支持,包括开源编程语言r。在这个环境中,有近100个软件包支持元分析,每个软件包具有不同的功能和特定功能(Lortie和Filazzola 2020)。在大多数领域,荟萃分析被定义为计算效应大小或来自一组研究或试验的加权证据的相对强度,然后检查高级统计模式和方差(Gurevitch, Koricheva, Nakagawa, and Stewart 2018)。它们越来越多地用于许多科学领域,以检查假设的一致性(Lortie 2014),并且当在研究中干预的有效性存在统计一致性时,已被提议作为金甚至白金证据标准(Stegenga 2011)。因此,迫切需要可访问的、实用的出版物、资源和文本,使具有不同专业水平的科学家能够使用元分析进行科学综合。
期刊介绍:
The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.