metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface.
IF 2.3 4区 计算机科学Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONSR JournalPub Date : 2022-09-01Epub Date: 2022-12-19DOI:10.32614/rj-2022-047
Daeyoung Lim, Ming-Hui Chen, Joseph G Ibrahim, Sungduk Kim, Arvind K Shah, Jianxin Lin
{"title":"metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface.","authors":"Daeyoung Lim, Ming-Hui Chen, Joseph G Ibrahim, Sungduk Kim, Arvind K Shah, Jianxin Lin","doi":"10.32614/rj-2022-047","DOIUrl":null,"url":null,"abstract":"<p><p>Meta-analysis, a statistical procedure that compares, combines, and synthesizes research findings from multiple studies in a principled manner, has become popular in a variety of fields. Meta-analyses using study-level (or equivalently <i>aggregate</i>) data are of particular interest due to data availability and modeling flexibility. In this paper, we describe an R package <b>metapack</b> that introduces a unified formula interface for both meta-analysis and network meta-analysis. The user interface-and therefore the package-allows flexible variance-covariance modeling for multivariate meta-analysis models and univariate network meta-analysis models. Complicated computing for these models has prevented their widespread adoption. The package also provides functions to generate relevant plots and perform statistical inferences like model assessments. Use cases are demonstrated using two real data sets contained in <b>metapack</b>.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"14 3","pages":"142-161"},"PeriodicalIF":2.3000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168678/pdf/nihms-1894279.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"R Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.32614/rj-2022-047","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/12/19 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Meta-analysis, a statistical procedure that compares, combines, and synthesizes research findings from multiple studies in a principled manner, has become popular in a variety of fields. Meta-analyses using study-level (or equivalently aggregate) data are of particular interest due to data availability and modeling flexibility. In this paper, we describe an R package metapack that introduces a unified formula interface for both meta-analysis and network meta-analysis. The user interface-and therefore the package-allows flexible variance-covariance modeling for multivariate meta-analysis models and univariate network meta-analysis models. Complicated computing for these models has prevented their widespread adoption. The package also provides functions to generate relevant plots and perform statistical inferences like model assessments. Use cases are demonstrated using two real data sets contained in metapack.
R JournalCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍:
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.
The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to:
- put their contribution in context, in particular discuss related R functions or packages;
- explain the motivation for their contribution;
- provide code examples that are reproducible.