Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance
Background: Heterogeneity is usually a major concern in meta-analysis. Although there are some statistical approaches for assessing variability across studies, here we present a new approach to heterogeneity using "MetaPlot" that investigate the influence of a single study on the overall...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tehran University of Medical Sciences
2010-06-01
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| Series: | Iranian Journal of Public Health |
| Subjects: | |
| Online Access: | https://ijph.tums.ac.ir/index.php/ijph/article/view/3123 |
| _version_ | 1818393752473960448 |
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| author | J Poorolajal M Mahmoodi R Majdzadeh A Fotouhi |
| author_facet | J Poorolajal M Mahmoodi R Majdzadeh A Fotouhi |
| author_sort | J Poorolajal |
| collection | DOAJ |
| description | Background: Heterogeneity is usually a major concern in meta-analysis. Although there are some statistical approaches for assessing variability across studies, here we present a new approach to heterogeneity using "MetaPlot" that investigate the influence of a single study on the overall heterogeneity.
Methods: MetaPlot is a two-way (x, y) graph, which can be considered as a complementary graphical approach for testing heterogeneity. This method shows graphically as well as numerically the results of an influence analysis, in which Higgins' I2 statistic with 95% (Confidence interval) CI are computed omitting one study in each turn and then are plotted against reciprocal of standard error (1/SE) or "precision". In this graph, "1/SE" lies on x axis and "I2 results" lies on y axe.
Results: Having a first glance at MetaPlot, one can predict to what extent omission of a single study may influence the overall heterogeneity. The precision on x-axis enables us to distinguish the size of each trial. The graph describes I2 statistic with 95% CI graphically as well as numerically in one view for prompt comparison. It is possible to implement MetaPlot for meta-analysis of different types of outcome data and summary measures.
Conclusion: This method presents a simple graphical approach to identify an outlier and its effect on overall heterogeneity at a glance. We wish to suggest MetaPlot to Stata experts to prepare its module for the software. |
| first_indexed | 2024-12-14T05:50:18Z |
| format | Article |
| id | doaj.art-ae5d1328338740ec92556cb2a6c4e99a |
| institution | Directory Open Access Journal |
| issn | 2251-6085 2251-6093 |
| language | English |
| last_indexed | 2024-12-14T05:50:18Z |
| publishDate | 2010-06-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Iranian Journal of Public Health |
| spelling | doaj.art-ae5d1328338740ec92556cb2a6c4e99a2022-12-21T23:14:44ZengTehran University of Medical SciencesIranian Journal of Public Health2251-60852251-60932010-06-01392Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a GlanceJ Poorolajal0M Mahmoodi1R Majdzadeh2A Fotouhi3 Background: Heterogeneity is usually a major concern in meta-analysis. Although there are some statistical approaches for assessing variability across studies, here we present a new approach to heterogeneity using "MetaPlot" that investigate the influence of a single study on the overall heterogeneity. Methods: MetaPlot is a two-way (x, y) graph, which can be considered as a complementary graphical approach for testing heterogeneity. This method shows graphically as well as numerically the results of an influence analysis, in which Higgins' I2 statistic with 95% (Confidence interval) CI are computed omitting one study in each turn and then are plotted against reciprocal of standard error (1/SE) or "precision". In this graph, "1/SE" lies on x axis and "I2 results" lies on y axe. Results: Having a first glance at MetaPlot, one can predict to what extent omission of a single study may influence the overall heterogeneity. The precision on x-axis enables us to distinguish the size of each trial. The graph describes I2 statistic with 95% CI graphically as well as numerically in one view for prompt comparison. It is possible to implement MetaPlot for meta-analysis of different types of outcome data and summary measures. Conclusion: This method presents a simple graphical approach to identify an outlier and its effect on overall heterogeneity at a glance. We wish to suggest MetaPlot to Stata experts to prepare its module for the software.https://ijph.tums.ac.ir/index.php/ijph/article/view/3123HeterogeneityMeta-AnalysisSystematic reviewStata graph |
| spellingShingle | J Poorolajal M Mahmoodi R Majdzadeh A Fotouhi Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance Iranian Journal of Public Health Heterogeneity Meta-Analysis Systematic review Stata graph |
| title | Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance |
| title_full | Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance |
| title_fullStr | Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance |
| title_full_unstemmed | Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance |
| title_short | Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance |
| title_sort | metaplot a novel stata graph for assessing heterogeneity at a glance |
| topic | Heterogeneity Meta-Analysis Systematic review Stata graph |
| url | https://ijph.tums.ac.ir/index.php/ijph/article/view/3123 |
| work_keys_str_mv | AT jpoorolajal metaplotanovelstatagraphforassessingheterogeneityataglance AT mmahmoodi metaplotanovelstatagraphforassessingheterogeneityataglance AT rmajdzadeh metaplotanovelstatagraphforassessingheterogeneityataglance AT afotouhi metaplotanovelstatagraphforassessingheterogeneityataglance |