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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Epidemiology
2019
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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. |
format | Online Article Text |
id | pubmed-6545497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Korean Society of Epidemiology |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT shimsungryul interventionmetaanalysisapplicationandpracticeusingrsoftware AT kimseongjang interventionmetaanalysisapplicationandpracticeusingrsoftware |