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Dose-response meta-analysis: application and practice using the R software
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. T...
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/PMC6635664/ https://www.ncbi.nlm.nih.gov/pubmed/30999740 http://dx.doi.org/10.4178/epih.e2019006 |
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author | Shim, Sung Ryul Lee, Jonghoo |
author_facet | Shim, Sung Ryul Lee, Jonghoo |
author_sort | Shim, Sung Ryul |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6635664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Korean Society of Epidemiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-66356642019-07-25 Dose-response meta-analysis: application and practice using the R software Shim, Sung Ryul Lee, Jonghoo Epidemiol Health Methods 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. Korean Society of Epidemiology 2019-03-28 /pmc/articles/PMC6635664/ /pubmed/30999740 http://dx.doi.org/10.4178/epih.e2019006 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 Lee, Jonghoo Dose-response meta-analysis: application and practice using the R software |
title | Dose-response meta-analysis: application and practice using the R software |
title_full | Dose-response meta-analysis: application and practice using the R software |
title_fullStr | Dose-response meta-analysis: application and practice using the R software |
title_full_unstemmed | Dose-response meta-analysis: application and practice using the R software |
title_short | Dose-response meta-analysis: application and practice using the R software |
title_sort | dose-response meta-analysis: application and practice using the r software |
topic | Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635664/ https://www.ncbi.nlm.nih.gov/pubmed/30999740 http://dx.doi.org/10.4178/epih.e2019006 |
work_keys_str_mv | AT shimsungryul doseresponsemetaanalysisapplicationandpracticeusingthersoftware AT leejonghoo doseresponsemetaanalysisapplicationandpracticeusingthersoftware |