Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg
Abstract Background Despite the widespread interest in meta-analysis of proportions, its rationale, certain theoretical and methodological concepts are poorly understood. The generalized linear models framework is well-established and provides a natural and optimal model for meta-analysis, network m...
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Archives of Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13690-023-01215-y |
| _version_ | 1797275787898912768 |
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| author | Victoria Nyawira Nyaga Marc Arbyn |
| author_facet | Victoria Nyawira Nyaga Marc Arbyn |
| author_sort | Victoria Nyawira Nyaga |
| collection | DOAJ |
| description | Abstract Background Despite the widespread interest in meta-analysis of proportions, its rationale, certain theoretical and methodological concepts are poorly understood. The generalized linear models framework is well-established and provides a natural and optimal model for meta-analysis, network meta-analysis, and meta-regression of proportions. Nonetheless, generic methods for meta-analysis of proportions based on the approximation to the normal distribution continue to dominate. Methods We developed metapreg, a tool with advanced statistical procedures to perform a meta-analysis, network meta-analysis, and meta-regression of binomial proportions in Stata using binomial, logistic and logistic-normal models. First, we explain the rationale and concepts essential in understanding statistical methods for meta-analysis of binomial proportions and describe the models implemented in metapreg. We then describe and demonstrate the models in metapreg using data from seven published meta-analyses. We also conducted a simulation study to compare the performance of metapreg estimators with the existing estimators of the population-averaged proportion in metaprop and metan under a broad range of conditions including, high over-dispersion and small meta-analysis. Conclusion metapreg is a flexible, robust and user-friendly tool employing a rigorous approach to evidence synthesis of binomial data that makes the most efficient use of all available data and does not require ad-hoc continuity correction or data imputation. We expect its use to yield higher-quality meta-analysis of binomial proportions. |
| first_indexed | 2024-03-07T15:19:28Z |
| format | Article |
| id | doaj.art-e9c1e0aa8a3e4ebeb86c746c69e2c820 |
| institution | Directory Open Access Journal |
| issn | 2049-3258 |
| language | English |
| last_indexed | 2024-03-07T15:19:28Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Archives of Public Health |
| spelling | doaj.art-e9c1e0aa8a3e4ebeb86c746c69e2c8202024-03-05T17:46:04ZengBMCArchives of Public Health2049-32582024-01-0182113910.1186/s13690-023-01215-yMethods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapregVictoria Nyawira Nyaga0Marc Arbyn1Unit of Cancer Epidemiology, SciensanoUnit of Cancer Epidemiology, SciensanoAbstract Background Despite the widespread interest in meta-analysis of proportions, its rationale, certain theoretical and methodological concepts are poorly understood. The generalized linear models framework is well-established and provides a natural and optimal model for meta-analysis, network meta-analysis, and meta-regression of proportions. Nonetheless, generic methods for meta-analysis of proportions based on the approximation to the normal distribution continue to dominate. Methods We developed metapreg, a tool with advanced statistical procedures to perform a meta-analysis, network meta-analysis, and meta-regression of binomial proportions in Stata using binomial, logistic and logistic-normal models. First, we explain the rationale and concepts essential in understanding statistical methods for meta-analysis of binomial proportions and describe the models implemented in metapreg. We then describe and demonstrate the models in metapreg using data from seven published meta-analyses. We also conducted a simulation study to compare the performance of metapreg estimators with the existing estimators of the population-averaged proportion in metaprop and metan under a broad range of conditions including, high over-dispersion and small meta-analysis. Conclusion metapreg is a flexible, robust and user-friendly tool employing a rigorous approach to evidence synthesis of binomial data that makes the most efficient use of all available data and does not require ad-hoc continuity correction or data imputation. We expect its use to yield higher-quality meta-analysis of binomial proportions.https://doi.org/10.1186/s13690-023-01215-yMeta-analysisMeta-regressionNetwork meta-analysisStataLogistic regressionBinomial |
| spellingShingle | Victoria Nyawira Nyaga Marc Arbyn Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg Archives of Public Health Meta-analysis Meta-regression Network meta-analysis Stata Logistic regression Binomial |
| title | Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg |
| title_full | Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg |
| title_fullStr | Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg |
| title_full_unstemmed | Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg |
| title_short | Methods for meta-analysis and meta-regression of binomial data: concepts and tutorial with Stata command metapreg |
| title_sort | methods for meta analysis and meta regression of binomial data concepts and tutorial with stata command metapreg |
| topic | Meta-analysis Meta-regression Network meta-analysis Stata Logistic regression Binomial |
| url | https://doi.org/10.1186/s13690-023-01215-y |
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