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Intervention meta-analysis: application and practice using R software

The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, re...

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Detalles Bibliográficos
Autores principales: Shim, Sung Ryul, Kim, Seong-Jang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Korean Society of Epidemiology 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6545497/
https://www.ncbi.nlm.nih.gov/pubmed/30999738
http://dx.doi.org/10.4178/epih.e2019008
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author Shim, Sung Ryul
Kim, Seong-Jang
author_facet Shim, Sung Ryul
Kim, Seong-Jang
author_sort Shim, Sung Ryul
collection PubMed
description The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research.
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spelling pubmed-65454972019-06-13 Intervention meta-analysis: application and practice using R software Shim, Sung Ryul Kim, Seong-Jang Epidemiol Health Methods The objective of this study was to describe general approaches for intervention meta-analysis available for quantitative data synthesis using the R software. We conducted an intervention meta-analysis using two types of data, continuous and binary, characterized by mean difference and odds ratio, respectively. The package commands for the R software were “metacont”, “metabin”, and “metagen” for the overall effect size, “forest” for forest plot, “metareg” for meta-regression analysis, and “funnel” and “metabias” for the publication bias. The estimated overall effect sizes, test for heterogeneity and moderator effect, and the publication bias were reported using the R software. In particular, the authors indicated methods for calculating the effect sizes of the target studies in intervention meta-analysis. This study focused on the practical methods of intervention meta-analysis, rather than the theoretical concepts, for researchers with no major in statistics. Through this study, the authors hope that many researchers will use the R software to more readily perform the intervention meta-analysis and that this will in turn generate further related research. Korean Society of Epidemiology 2019-03-28 /pmc/articles/PMC6545497/ /pubmed/30999738 http://dx.doi.org/10.4178/epih.e2019008 Text en ©2019, Korean Society of Epidemiology This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods
Shim, Sung Ryul
Kim, Seong-Jang
Intervention meta-analysis: application and practice using R software
title Intervention meta-analysis: application and practice using R software
title_full Intervention meta-analysis: application and practice using R software
title_fullStr Intervention meta-analysis: application and practice using R software
title_full_unstemmed Intervention meta-analysis: application and practice using R software
title_short Intervention meta-analysis: application and practice using R software
title_sort intervention meta-analysis: application and practice using r software
topic Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6545497/
https://www.ncbi.nlm.nih.gov/pubmed/30999738
http://dx.doi.org/10.4178/epih.e2019008
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