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Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis
BACKGROUND: The proposed sequential and combinatorial algorithm, suggested as a standard tool for assessing, exploring, and reporting heterogeneity in the meta-analysis, is useful but time-consuming particularly when the number of included studies is large. Metaplot is a novel graphical approach tha...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238175/ https://www.ncbi.nlm.nih.gov/pubmed/34181682 http://dx.doi.org/10.1371/journal.pone.0253341 |
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author | Poorolajal, Jalal Noornejad, Shahla |
author_facet | Poorolajal, Jalal Noornejad, Shahla |
author_sort | Poorolajal, Jalal |
collection | PubMed |
description | BACKGROUND: The proposed sequential and combinatorial algorithm, suggested as a standard tool for assessing, exploring, and reporting heterogeneity in the meta-analysis, is useful but time-consuming particularly when the number of included studies is large. Metaplot is a novel graphical approach that facilitates performing sensitivity analysis to distinguish the source of substantial heterogeneity across studies with ease and speed. METHOD: Metaplot is a Stata module based on Stata’s commands, known informally as "ado". Metaplot presents a two-way (x, y) plot in which the x-axis represents the study codes and the y-axis represents the values of I(2) statistics excluding one study at a time (n-1 studies). Metaplot also produces a table in the ’Results window’ of the Stata software including details such as I(2) and χ(2) statistics and their P-values omitting one study in each turn. RESULTS: Metaplot allows rapid identification of studies that have a disproportionate impact on heterogeneity across studies, and communicates to what extent omission of that study may reduce the overall heterogeneity based on the I(2) and χ(2) statistics. Metaplot has no limitations regarding the number of studies or types of outcome data (binomial or continuous data). CONCLUSIONS: Metaplot is a simple graphical approach that gives a quick and easy identification of the studies having substantial influences on overall heterogeneity at a glance. |
format | Online Article Text |
id | pubmed-8238175 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82381752021-07-09 Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis Poorolajal, Jalal Noornejad, Shahla PLoS One Research Article BACKGROUND: The proposed sequential and combinatorial algorithm, suggested as a standard tool for assessing, exploring, and reporting heterogeneity in the meta-analysis, is useful but time-consuming particularly when the number of included studies is large. Metaplot is a novel graphical approach that facilitates performing sensitivity analysis to distinguish the source of substantial heterogeneity across studies with ease and speed. METHOD: Metaplot is a Stata module based on Stata’s commands, known informally as "ado". Metaplot presents a two-way (x, y) plot in which the x-axis represents the study codes and the y-axis represents the values of I(2) statistics excluding one study at a time (n-1 studies). Metaplot also produces a table in the ’Results window’ of the Stata software including details such as I(2) and χ(2) statistics and their P-values omitting one study in each turn. RESULTS: Metaplot allows rapid identification of studies that have a disproportionate impact on heterogeneity across studies, and communicates to what extent omission of that study may reduce the overall heterogeneity based on the I(2) and χ(2) statistics. Metaplot has no limitations regarding the number of studies or types of outcome data (binomial or continuous data). CONCLUSIONS: Metaplot is a simple graphical approach that gives a quick and easy identification of the studies having substantial influences on overall heterogeneity at a glance. Public Library of Science 2021-06-28 /pmc/articles/PMC8238175/ /pubmed/34181682 http://dx.doi.org/10.1371/journal.pone.0253341 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Poorolajal, Jalal Noornejad, Shahla Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis |
title | Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis |
title_full | Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis |
title_fullStr | Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis |
title_full_unstemmed | Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis |
title_short | Metaplot: A new Stata module for assessing heterogeneity in a meta-analysis |
title_sort | metaplot: a new stata module for assessing heterogeneity in a meta-analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8238175/ https://www.ncbi.nlm.nih.gov/pubmed/34181682 http://dx.doi.org/10.1371/journal.pone.0253341 |
work_keys_str_mv | AT poorolajaljalal metaplotanewstatamoduleforassessingheterogeneityinametaanalysis AT noornejadshahla metaplotanewstatamoduleforassessingheterogeneityinametaanalysis |